Dynamic Scheme Research Articles

Current and Future Applications of Machine Perfusion and Other Dynamic Preservation Strategies in Liver Transplantation

Current and Future Applications of Machine Perfusion and Other Dynamic Preservation Strategies in Liver Transplantation

Achieving productivity and operator well-being: a dynamic task allocation strategy for collaborative assembly systems in Industry 5.0

Collaborative robots, or cobots, offer a unique combination of productivity and flexibility that has led to significant growth in adoption over the past decade. Moreover, recently, there has been a shift towards a human-centered design of the workspace, known as one of the drivers of Industry 5.0, which prioritizes the well-being of operators. To achieve this, various human factors such as ergonomics, mental workload, personal skills, and capabilities need to be considered in the workspace design, and their impact on system productivity must be evaluated. The integration of a human and a cobot in the same workplace can affect the performance of the human operator, as the perception of the cobot can impact their work. This highlights the importance of taking human factors into account, as a lack of consideration in these aspects has contributed to the failure of many implementations. To link the objectives of productivity, flexibility, and human factors consideration, a dynamic real-time multi-objective task allocation strategy for collaborative assembly systems is developed. This approach considers the different characteristics of the resources and optimizes for two objectives, makespan, and energy expenditure of the operator. By using this approach, it is possible to modify the behavior of the cobot by reallocating tasks between the two resources based on the operator’s current needs. In other words, if the operator appears too stressed due to time constraints or their energy rate level is too high, some of their assigned tasks can be transferred to the cobot. This helps to maintain a balanced system while reducing the operator’s stress.

Dynamic Current-Limitation Strategy of Grid-Forming Inverters Based on SR Latches

Dynamic Current-Limitation Strategy of Grid-Forming Inverters Based on SR Latches

Dynamic Emission Reduction Strategy for Shipping Company Considering Shipper's Cancellation of Cabin Space

Dynamic Emission Reduction Strategy for Shipping Company Considering Shipper's Cancellation of Cabin Space

An improved chaotic GWO-LGBM hybrid algorithm for emotion recognition

To address the challenges associated with the unbalanced search abilities and low search accuracy of the Gray Wolf Optimization (GWO) algorithm, we propose an enhanced version termed the Improved Chaotic Gray Wolf Optimization Algorithm (ICGWO) based on cubic mapping. Initially, the population positions of gray wolves were perturbed to expedite the convergence speed. Subsequently, a nonlinear convergence factor was introduced to enhance both search ability and efficiency. Finally, within the algorithm, a dynamic inertia-weighting strategy was implemented to improve convergence speed and solution accuracy. Experimental simulations encompassing 23 classical test functions demonstrate that the ICGWO algorithm exhibits superior convergence speed, solution accuracy, and stability compared to other swarm intelligence optimization algorithms and certain enhanced GWO algorithms. In comparison to GWO, utilizing the ICGWO method for hyperparameter optimization within the Light Gradient Boosting Machine (LGBM) for emotional recognition led to notable enhancements in average accuracy and F1 score by 7.29 % and 3.92 % respectively for valence, and 4.33 % and 3.23 % respectively for arousal. Additionally, the standard deviation of the ICGWO-LGBM was less than one-tenth of the standard deviation of GWO-LGBM in both conditions. These results underscore the strong optimization capabilities and high stability of the ICGWO algorithm.

DBUP: Dynamic blockchain UTXO processing for storage efficiency optimization

In traditional blockchain systems, the Unsent Transaction Output (UTXO) dataset stores all generated UTXOs, which typically represent digital assets of the users. UTXOs can be used to track the output status of cryptocurrency transactions. However, with the development of blockchain networks, the UTXO dataset continuously increasing in scale. In the UTXO dataset, a large number of low-value UTXOs occupied huge storage space, making the blockchain dataset expanded. This impacts the running performance of the whole blockchain system. To address this issue, this paper proposes the Dynamic Blockchain UTXO Processing strategy (DBUP) for UTXO storage efficiency optimization. This strategy aims to rapidly consume the low-value UTXOs, effectively alleviating UTXO dataset expansion. Furthermore, Through improved simulated annealing (ISA) algorithm and a UTXO dynamic adjustment mechanism in DBUP, the strategy ensures that the transaction fee in the transaction process remain at a low level. The experimental evaluation indicates that our strategy can achieve the better performance against the Low Value First (LVF) and the First-In-First-Out (FIFO) strategies, enhancing the storage efficiency of blockchain systems.

Exploring the sources of systemic risk and trading strategies in energy and stock markets

This study aims to investigate the impact of the energy transition on asset pricing by analyzing the different sources of systemic risk in a comprehensive system of one traditional energy ETF, one clean energy ETF, seven oil-exporting country ETFs, and six oil-importing country ETFs. We find that shocks to the traditional (clean) energy market are the primary source of short-term (long-term) systemic risk, suggesting that oil-dependent countries have started to incorporate the long-term risks associated with clean energy into the pricing of related assets. Meanwhile, we document both homogeneity within and heterogeneity between oil exporters and oil importers in the transmission patterns of systemic risk. These findings can help provide customized trading strategies for investors with diverse profiles. Additionally, we compare the regime-dependent hedging strategy with both the regime-dependent diversification strategy and the dynamic hedging strategy, demonstrating that it is the most effective for investors aiming to minimize portfolio volatility. Collectively, our results provide valuable insights that can assist policymakers, portfolio managers, and investors in adapting to the evolving dynamics of the energy transition.

Exploring interpretable evolutionary optimization via significance of each constraint and population diversity

Evolutionary algorithms (EAs) have been widely employed to solve complex constrained optimization problems (COPs). However, numerous EAs treat constraints as a collective black box, employing a uniform processing technique for all constraints. Generally, there exists variability in the significance of each constraint within COPs. To address this issue, this paper is the first attempt to investigate the significance of each constraint spontaneously during the evolution process, and then proposes a co-directed evolutionary algorithm (CdEA-SCPD) for exploring interpretable COPs. First, CdEA-SCPD develops an adaptive penalty function designed to assign different weights to constraints based on their violation severity, thereby varying the significance of each constraint to enhance interpretability and facilitate the algorithm to converge more rapidly toward the global optimum. In addition, a dynamic archiving strategy and a shared replacement mechanism are developed to improve the population diversity of CdEA-SCPD. Extensive experiments on benchmark functions from IEEE CEC2006, CEC2010, and CEC2017 and three engineering problems demonstrate the superiority of the proposed CdEA-SCPD compared to existing competitive EAs. Specifically, on the benchmark functions from IEEE CEC2010, the proposed method yields ρ values lower than 0.05 in the multiple-problem Wilcoxon's signed rank test and ranks first in the Friedman's test. Furthermore, ablation analysis and parameter analysis have demonstrated the beneficial effects of the proposed strategies.

Using Dynamic Stability Strategy to Counteract Rapid Changing Demand Considering Deterioration and Partial Backordering

Using Dynamic Stability Strategy to Counteract Rapid Changing Demand Considering Deterioration and Partial Backordering

Learning-based dynamic pricing strategy with pay-per-chapter mode for online publisher with case study of COL

We consider how to make dynamic pricing decision for Chinese Online (COL) at T time-points, an online publisher that allow authors to sell their ongoing book projects. Instead of paying for a book, readers pay for each chapter (pay-per-chapter mode) of the ongoing book project. This mode allows readers to pay for as many chapters as they want without taking the risk that the releasing of new chapters might be delayed or stopped. Despite of the dynamics of chapter-by-chapter released of COL products, the fixed pricing strategy (FPS) does not make fully use of the reading data generated by releasing chapters of the ongoing book. We propose a learning-based dynamic pricing strategy (LDPS) that exploits the newly information to maximize cumulative revenue for the publisher. The LDPS captures the ever changing features of readers. It employs the Thompson sampling method to balance the exploration of investigating different prices sufficiently with the exploitation of settling on the optimal price. Taking COL as a case study and implementing our strategy in the context of the aforementioned real-life data set, we show that LDPS outperform several classical strategies such as Greedy, Prior-Free TS and Prior-Given TS, and average revenue of LDPS is increased by 0.5 % average per time-point compared to the publisher's historical decisions. We also provide some management implications for the COL publisher by analyzing the pricing range of different genres of books and the choice of the exploration threshold parameter.

Secure image communication based on two-layer dynamic feedback encryption and DWT information hiding.

In response to the vulnerability of image encryption techniques to chosen plaintext attacks, this paper proposes a secure image communication scheme based on two-layer dynamic feedback encryption and discrete wavelet transform (DWT) information hiding. The proposed scheme employs a plaintext correlation and intermediate ciphertext feedback mechanism, and combines chaotic systems, bit-level permutation, bilateral diffusion, and dynamic confusion to ensure the security and confidentiality of transmitted images. Firstly, a dynamically chaotic encryption sequence associated with a secure plaintext hash value is generated and utilized for the first round of bit-level permutation, bilateral diffusion, and dynamic confusion, resulting in an intermediate ciphertext image. Similarly, the characteristic values of the intermediate ciphertext image are used to generate dynamically chaotic encryption sequences associated with them. These sequences are then employed for the second round of bit-level permutation, bilateral diffusion, and dynamic confusion to gain the final ciphertext image. The ciphertext image hidden by DWT also provides efficient encryption, higher level of security and robustness to attacks. This technology offers indiscernible secret data insertion, rendering it challenging for assailants to spot or extract concealed information. By combining the proposed dynamic closed-loop feedback secure image encryption scheme based on the 2D-SLMM chaotic system with DWT-based hiding, a comprehensive and robust image encryption approach can be achieved. According to the results of theoretical research and experimental simulation, our encryption scheme has dynamic encryption effect and reliable security performance. The scheme is highly sensitive to key and plaintext, and can effectively resist various common encryption attacks and maintain good robustness. Therefore, our proposed encryption algorithm is an ideal digital image privacy protection technology, which has a wide range of practical application prospects.

Exact discrete-time stability analysis of multi-DOF haptic rendering: Impact of multi-rate, time-delay, and mechanical parameters

Previous studies on the stability analysis of haptic devices have predominantly focused on single-DOF devices, thereby limiting attention to multi-DOF devices, particularly those employing multi-rate sampling schemes. In this paper, we introduce a formulation for the coupled dynamics between the haptic device and the virtual environment for a multi-DOF haptic device controlled using a dual-rate sampling scheme. Subsequently, we analyze its stability through the application of a dynamic decoupling strategy within an exact discrete-time state-space framework while the device is engaged in rendering a virtual wall along one of its operational space coordinates. Furthermore, we explore how the combined influence of the dual-rate sampling approach, time delay, and the mechanical design affects the stability boundaries of the multi-DOF haptic device at a fixed workspace location as well as within the entire usable workspace. Additionally, we utilize a model-order reduction (MOR) framework to simplify the determination of the device’s stability limits, irrespective of the specific combinations of time delay and sampling rates employed.

Evaluating drivers and predictability of catch composition in a highly mixed trawl fishery using stacked and joint species distribution models

Evaluating drivers and the predictability of catch is valuable for the management of mixed fisheries. Drivers can represent or help to identify levers for management and predictable catch compositions are a key component of simulation tools and dynamic management strategies. But modelling mixed fisheries can be challenging due to the large number of taxa, and analysis typically focuses on a few key species or highly aggregated taxa.Here we employ seven types of stacked and joint species distribution models to explore the drivers and predictability of trawl-level catches in an ocean prawn trawl fishery, in New South Wales, Australia. Catch data was sourced from an observer program, with 130 taxa able to be modelled. The main drivers of catch composition were latitude, depth, and seasonality represented here by water temperature. Water column mixing, lunar illumination, and fishing effort were also important for some taxa. Up to 60–80 taxa were predicted with good predictive skill (AUC>0.8, >35 % decline in mean absolute error relative to an intercept-only model), and an additional 40–60 taxa were predicted with lower but still useful predictive skill (AUC>0.7, 25–35 % decline in error). However, the level of predictive skill varied considerably among model type.The best framework for prediction was stacked random forests using a hurdle modelling approach, followed by a spatial joint species distribution model. Our results show that predictive models at a fine spatial-temporal and taxonomic resolutions can be a viable information tool for highly mixed fisheries, but these tools ultimately need to be tested against specific management objectives and performance metrics, such as spatial closures and bycatch:target catch ratios.

Optimization research on control strategies for photovoltaic energy storage systems considering multi-mode operation

In this paper, a selective input/output strategy is proposed for improving the life of photovoltaic energy storage (PV-storage) virtual synchronous generator (VSG) caused by random load interference, which can sharply reduce costs of storage device. The strategy consists of two operating modes and a power coordination control method for the VSGs. Firstly, a selective VSG input strategy is proposed based on the magnitude of disturbances, a method of offline solving model equation is used for determine the VSG input time. An online method is used for matching the disturbance frequency variations, which enables a selective VSG startup method, allowing the grid to prioritize the utilization of the generator's physical inertia. Secondly, a dynamic VSG exit strategy is developed based on dynamic frequency characteristics to prevent secondary oscillations in the frequency recovery phase of the PV-storage VSG following grid disturbances. This strategy is crucial as grid variations may affect energy storage lifespan and reduce frequency recovery speed. Finally, the proposed approach is validated for correctness and effectiveness through computer simulations and semi-physical experiments using the NI-PXI + LabVIEW platform. Through the above optimization and research, the selective start of VSG is realized, the energy storage life is improved, the capacity of charge and discharge cycles is reduced by 37.82 % compared with the strategy without investment and withdrawal, and the life loss in the secondary charge and discharge process of PV-storage VSG is avoided, which is conducive to frequency recovery.

Self-passivation/self-delivery/self-healing anticorrosion polymer coating for marine applications

Corrosion of steel in the marine environment greatly reduces their service life. Polymeric coatings are the most popular anticorrosion technology, but seawater penetration cannot be prohibited because of the distinct stacking structure of the macromolecular chains. In this context, a novel anticorrosive hyperbranched polyurethane-based coating with dopamine (DOPA) at the terminals is prepared herein. The built-in DOPA is able to capture the iron ions released from the corroded substrate and form DOPA-Fe3+ complexation, which further cooperates with the surrounding seawater and imparts self-passivation, self-delivery and self-healing capabilities to the coating. Under the joint action of these measures, the corrosion of tinplate (serving as the steel model) is reduced to a record-low level (corrosion current = 1 × 10−9 A cm−2, corrosion rate = 1 × 10−5 mm year−1). Conceptually, the present dynamic active anticorrosion strategy greatly outperforms the traditional static passive approach, and turns the unfavorable but unavoidable seawater into a favorable factor, which paves the way for the development of long-lasting marine coatings.

Dynamic mater-slave control strategy for transient coordination of inverter based microgrids under asymmetric faults

The ever-expanding inverter-based microgrids (MGs) require the grid-forming distributed renewable energy resources (DERs) to enhance the system safety. However, the different types of grid-forming DERs are prone to have transient conflicts with each other under short circuit faults, leading to unexpected outage or even system collapse. The fast response and poor overcurrent capability of DERs make transient coordination a challenge. This paper proposes a dynamic master–slave architecture for transient coordination control in islanded MGs under asymmetric faults. A dynamic reconfigurable voltage reference unit (DVRU) is designed according to the real faults. In this manner, the other DERs could comply with this DVRU to achieve both system voltage support, inrush current restraining, and power oscillation suppression. It is action signals instead of phase signals need to be transferred by the communication bus with the proposed local fault current differential time delay method (FCDDM). Thus, the low-bandwidth and the enhanced robust communication could be met under transient state. Additionally, the feasibility of the proposed method to combined with relay protection is analyzed. Finally, case study results on islanded MGs consisting of various grid-forming and grid-following DERs demonstrate the effectiveness and robustness of the proposed method under short circuit faults as well as communication failures.

Modeling the Transmission Dynamics and Optimal Control Strategy for Huanglongbing

Huanglongbing (HLB), also known as citrus greening disease, represents a severe and imminent threat to the global citrus industry. With no complete cure currently available, effective control strategies are crucial. This article presents a transmission model of HLB, both with and without nutrient injection, to explore methods for controlling disease spread. By calculating the basic reproduction number (R0) and analyzing threshold dynamics, we demonstrate that the system remains globally stable when R0<1, but persists when R0>1. Sensitivity analyses reveal factors that significantly impact HLB spread on both global and local scales. We also propose a comprehensive optimal control model using the pontryagin minimum principle and validate its feasibility through numerical simulations. Results show that while removing infected trees and spraying insecticides can significantly reduce disease spread, a combination of measures, including the production of disease-free budwood and nursery trees, nutrient solution injection, removal of infected trees, and insecticide application, provides superior control and meets the desired control targets. These findings offer valuable insights for policymakers in understanding and managing HLB outbreaks.

Experiential Marketing: Challenges and Opportunities

Experiential marketing emerges as a dynamic and influential strategy, providing immersive engagements that captivate consumers and foster brand loyalty. However, executing such strategies successfully presents numerous challenges. This abstract delves into the complex landscape of implementing experiential marketing, highlighting the hurdles and uncovering the vast opportunities. businesses face logistical intricacies, budget constraints, and the need for innovative technological integration. additionally, maintaining authenticity and ensuring scalability pose significant challenges. nevertheless, experiential marketing holds abundant prospects, including forging deep emotional connections with consumers, fostering brand loyalty, and driving word-of-mouth marketing. through industry insights and case studies, this paper provides strategic guidance to overcome these challenges and leverage opportunities. It advocates for a comprehensive approach balancing creativity with feasibility, using data-driven insights to tailor experiences resonating with diverse audiences. emphasizing continuous innovation and adaptability, this abstract serves as a roadmap for marketers navigating the experiential marketing landscape to drive meaningful engagement and brand growth.

Dynamic management of ground thermal response uncertainty through temporal analysis of parameter sensitivity

Accurately estimating the dimensionless ground thermal response factor (g-function) is critical for the design and operation of geothermal energy systems. This process, however, is fraught with uncertainties stemming from the difficulty in controlling on-site geometric parameters and estimating thermal properties through field experiments. The relative contributions of these parameters to response uncertainty change temporally as the response progresses. Thus, understanding this temporal sensitivity variation is crucial for effective response uncertainty management. To address this, we conducted transient global sensitivity analyses. Regardless of borefield setups, ground thermal properties were key factors in thermal response uncertainty. However, their influence decreased from the late-mid term as borehole radius and borehole distance became more significant in single-borehole and multi-borehole setups, respectively. This time-varying relative sensitivity suggests that prioritizing parameters to reduce response uncertainty depends on the temporal characteristics of the ground load. To demonstrate this, we systematically halved the uncertainty for each parameter and examined the resultant change in the fluid temperature uncertainty for a dynamic ground load scenario. Notably, prioritizing parameters with high early-response sensitivity significantly reduced fluid temperature uncertainty. These findings highlight the need for dynamic uncertainty management strategies in geothermal system design and operation. For dynamic load profiles utilizing the ground as a heat source or sink, prioritizing parameters with high sensitivity in the early- to mid-term is crucial. Conversely, for long-term storage applications, focusing on parameters with high sensitivity in the mid- to long-term response becomes essential.

The Managerial Economics Implications Of Rupiah Exchange Rate Fluctuations On Investment And Corporate Growth

The managerial economics implications of Rupiah exchange rate fluctuations on investment and corporate growth are critical considerations for businesses operating in Indonesia's volatile economic environment. This study, conducted through an extensive literature review, explores the multifaceted impacts of exchange rate volatility on corporate decision-making and performance. Key findings indicate that fluctuations in the Rupiah increase investment risks and uncertainties, adversely affecting private sector investment and corporate growth. The review highlights that exchange rate volatility escalates production costs and diminishes profit margins, thereby impeding business expansion and competitive positioning in international markets. From a managerial perspective, effective risk management strategies such as hedging and portfolio diversification are essential for mitigating exchange rate risks. Additionally, efficient resource management and operational efficiency are pivotal in cushioning the adverse effects of currency fluctuations. The study underscores the importance of dynamic pricing strategies and financial instruments in stabilizing cash flows and sustaining growth amidst economic turbulence. This paper contributes to the theoretical and practical understanding of exchange rate impacts on managerial decisions and provides actionable insights for businesses to navigate currency volatility. Future research should focus on empirical analyses to validate the findings and explore case studies across various sectors to further elucidate the nuances of managing exchange rate risks in different business contexts.