Dynamic Adjustment Research Articles

An Immersive VR Training Scenario for Safe Methanol Bunkering in the Maritime Industry

Abstract The maritime industry, pivotal in global commerce, faces critical challenges in mitigating environmental impacts, especially greenhouse gas emissions. The International Maritime Organization (IMO) targets a substantial reduction in emissions by 2050, necessitating innovative approaches to fuel usage. Methanol, recognized for its lower carbon intensity, emerges as a viable alternative fuel. However, its flammability and toxicity introduce complex safety and operational demands. To address these challenges, we have developed a state-of-the-art Virtual Reality (VR) training program specifically for methanol bunkering operations. This training, utilizing Unity 3D and Meta Quest 2, offers a highly interactive and realistic experience. Trainees engage in a comprehensive simulation that covers the entire bunkering process, including safety checks, equipment handling, and emergency response protocols. The VR environment allows for dynamic adjustments by trainers, enhancing the realism and educational value of the scenario. Our program also includes an assessment component that evaluates the trainee’s performance across various metrics such as safety protocol adherence, decision-making, and situational awareness. The anticipated outcome of this VR training initiative is to significantly improve the preparedness of maritime personnel in handling alternative fuels, thereby supporting the industry’s transition towards sustainable practices and contributing to broader environmental protection goals.

Towards net zero: Comprehensive approach for voluntary carbon trading among microgrids

In the context of growing focus over climate changes and promoting sustainability across a various range of fields, microgrids can play a significant role in global decarbonization endeavors, contributing to carbon neutrality and ultimately attaining net-zero emissions in the energy sector. The objective of this paper is to address the integration of voluntary carbon trading within microgrid communities, in the perspective to mitigate greenhouse gas (GHG) emissions and boost the integration of renewable energy sources (RES). Introducing five modular algorithms managing key aspects of carbon trading, the study engineers a comprehensive framework aiming to optimally orchestrate the Voluntary Carbon Market (VCM) within microgrids. Particularly, the developed algorithms address aspects such as market enrollment decision, optimal energy dispatch, managing the market transactions, sellers-buyers matching as well as the dynamic adjustment of carbon credit prices and emission taxes. The framework has been rigorously tested across three distinct scenarios. Results show that the developed VCM, with adequate configurations, has the potential to incentivize microgrids to curtail their GHG emissions, encourage the integration of more RES into their generation mix, and rewards the more proactive microgrids with substantial financial incentives.

Improved real-time [formula omitted] control for aero-engines based on the equilibrium manifold expansion model

This paper investigates the transient performance and disturbance rejection control problem for aero-engines based on the equilibrium manifold expansion (EME) model. Considering the operating characteristics of the aero-engine vary greatly during different flight states, it is difficult to ensure the performance of the aero-engine in different states using a single controller designed offline. To solve this problem, this paper proposes an improved real-time H∞ controller. Through driving the controller to match the changes in dynamic characteristics of the system, the stability and disturbance rejection performance of the system are guaranteed over a wide range effectively. In addition, a dynamic adjustment mechanism is designed for the disturbance rejection parameter in the proposed real-time H∞ controller, which further improves the transient performance of the system. The sufficient conditions to ensure the stability of the system under the designed method are also given. Finally, the effectiveness and superiority of the proposed method are verified through an aero-engine numerical simulation program and a hardware-in-the-loop (HIL) experiment.

Prediction of typical gas components in cigarette smoke based on transformer

Abstract Harm reduction of cigarette products has become one of the primary objectives of the entire tobacco industry. The prediction of typical gas components (TGC) and dynamic adjustment of process parameters are crucial for cigarette coke reduction and harm reduction. In this study, a deep learning (DL)-assisted TGC prediction framework is proposed to predict CO and NI simultaneously. Firstly, a large number of experimental and simulation parameters of cigarette process are collected as the data base for subsequent model construction. Then, the feature importance analysis of the process parameters was carried out by combining the mechanism of the combustion process through the mutual information method. Finally, DL models based on Multilayer Perceptron, One-Dimension Convolutional Neural Network and Transformer (TF) was developed as data-driven surrogate models to establish the mapping relationship between process parameters and TGC. The results show that TF model generalizes best and can predict TGC quickly and accurately with R2 over 0.99. This work will provide a valuable predictive and decision-making tool for cigarette harm reduction.

Dynamic Adjustment: The Transformation of Trilateral Relations between China, the United States and Latin America (1949-2023)

This paper examines the transformative dynamics of the trilateral relationship between the United States, China, and Latin America from the 1950s to the present. Through the lens of national role theory, it explores how the national roles of these actors have shifted in response to geopolitical, economic, and ideological changes. The study is structured chronologically, analyzing distinct periods that mark the evolution of this trilateral dynamic. It delves into the historical evolution of roles and interactions, policy shifts, and strategic calculations to offer insights into the complexities of trilateral relations. The paper contributes to the literature by highlighting the interplay between self-perceived national roles and external expectations, and it provides a nuanced perspective on the challenges and opportunities inherent in navigating the multifaceted interactions between the US, China, and Latin America. The research aims to enhance understanding of the broader geopolitical trends and regional dynamics that have influenced the trajectory of these relations, offering implications for global politics, regional stability, and economic development.

Siamese InternImage for Change Detection

For some time, CNN was the de facto state-of-the-art method in remote sensing image change detection. Although transformer-based models have surpassed CNN-based models due to their larger receptive fields, CNNs still retain their value for their efficiency and ability to extract precise local features. To overcome the limitations of the restricted receptive fields in standard CNNs, deformable convolution allows for dynamic adjustment of sampling locations in convolutional kernels, improving the network’s ability to model global contexts. InternImage is an architecture built upon deformable convolution as its foundational operation. Motivated by InternImage, in this paper, a CNN-based change detection vision foundation model is proposed. By introducing deformable convolution into Siamese InternImage architecture, the proposed CNN-based change detection vision foundation model is capable of capturing long-range dependencies and global information. A refinement block is utilized to merge local detail, where channel attention is incorporated. The proposed approach achieved excellent performance on the LEVIR-CD and WHU-CD datasets.

Smart home power management algorithm using real-time model predictive control for a stand-alone PV system with battery energy storage

A smart home power management system is critical for stand-alone home-photovoltaic (HPV) with battery energy storage. Existing approaches often focus on maximizing power extraction from PV systems without considering real-time power adjustments or battery state of charge (SoC), which can lead to over-current or over-voltage issues that damage the battery. To address these limitations, this paper proposes a home power management algorithm that dynamically adjusts the power flow between the PV system, home loads, and the battery based on real-time system power measurements and battery SoC. This dynamic adjustment ensures an uninterrupted power supply to the home loads while maintaining battery safety. The proposed home manager algorithm features multiple operating modes: Maximum power point (MPP) charging, partial charging, fast charging, float charging, and partial discharging. Each mode is integrated with its dedicated model predictive controller (MPC) to achieve its specific control objective. Finally, through a semi-experimental simulation using a process-in-the-loop (PIL) test approach with the embedded board eZdsp TMS320F28335, testing under various irradiance levels shows that the home manager achieves significant improvement over conventional approaches. For instance, over the MPP mode it achieve a power efficiency of 99.36% with a 2.05% SoC increase, while fast charging mode resulted in 87.44% efficiency with an 11.2% SoC increase. However, float charging mode maintained an 87.27% efficiency with a 2.6% SoC increase, and partial discharging led to a 1.3% SoC decrease. The overall battery efficiency reached 96.45%, demonstrating the algorithm’s ability to optimize power flow, ensure a reliable energy supply, and maintain battery safety under varying weather conditions.

Addressing Constraint Coupling and Autonomous Decision-Making Challenges: An Analysis of Large-Scale UAV Trajectory-Planning Techniques

With the increase in UAV scale and mission diversity, trajectory planning systems faces more and more complex constraints, which are often conflicting and strongly coupled, placing higher demands on the real-time and response capabilities of the system. At the same time, conflicts and strong coupling pose challenges the autonomous decision-making capability of the system, affecting the accuracy and efficiency of the planning system in complex environments. However, recent research advances addressing these issues have not been fully summarized. An in-depth exploration of constraint handling techniques and autonomous decision-making issues will be of great significance to the development of large-scale UAV systems. Therefore, this paper aims to provide a comprehensive overview of this topic. Firstly, the functions and application scenarios of large-scale UAV trajectory planning are introduced and classified in detail according to the planning method, realization function and the presence or absence of constraints. Then, the constraint handling techniques are described in detail, focusing on the priority ranking of constraints and the principles of their fusion and transformation methods. Then, the importance of autonomous decision-making in large-scale UAV trajectory planning is described in depth, and related dynamic adjustment algorithms are introduced. Finally, the future research directions and challenges of large-scale UAV trajectory planning are outlooked, providing directions and references for future research in the fields of UAV clustering and UAV cooperative flight.

Towards Emotionally Intelligent Virtual Environments: Classifying Emotions through a Biosignal-Based Approach

This paper introduces a novel method for emotion classification within virtual reality (VR) environments, which integrates biosignal processing with advanced machine learning techniques. It focuses on the processing and analysis of electrocardiography (ECG) and galvanic skin response (GSR) signals, which are established indicators of emotional states. To develop a predictive model for emotion classification, we extracted key features, i.e., heart rate variability (HRV), morphological characteristics, and Hjorth parameters. We refined the dataset using a feature selection process based on statistical techniques to optimize it for machine learning applications. The model achieved an accuracy of 97.78% in classifying emotional states, demonstrating that by accurately identifying and responding to user emotions in real time, VR systems can become more immersive, personalized, and emotionally resonant. Ultimately, the potential applications of this method are extensive, spanning various fields. Emotion recognition in education would allow further implementation of adapted learning environments through responding to the current emotional states of students, thereby fostering improved engagement and learning outcomes. The capability for emotion recognition could be used by virtual systems in psychotherapy to provide more personalized and effective therapy through dynamic adjustments of the therapeutic content. Similarly, in the entertainment domain, this approach could be extended to provide the user with a choice regarding emotional preferences for experiences. These applications highlight the revolutionary potential of emotion recognition technology in improving the human-centric nature of digital experiences.

Numerical Simulation Study on Optimization of Development Parameters of Condensate Gas Reservoirs

Due to the retrograde condensation phenomenon in the development process, the fluid phase change is complex, and it becomes particularly difficult to accurately describe the fluid flow characteristics and residual oil and gas distribution characteristics during the development of condensate gas reservoirs. It is difficult to select the development program and subsequent dynamic adjustment for the efficient, reasonable, and sustainable development of condensate gas reservoirs. In this paper, the phase characteristics of condensate gas reservoirs are clarified; the basic fluid model is created by using computer modeling, using Win-Prop; and in view of the characteristics of the target condensate gas reservoirs, the CMG (Computer Modeling Group) numerical simulation method is applied to study the effects of six factors, the thickness of the reservoir, permeability, porosity, rock compression coefficient, the ratio of the vertical permeability to the horizontal permeability, and the injection of different media, on the development effect through the study of different development parameters of gas condensate reservoirs. The purpose of this study is to provide guidance for the rational development of condensate gas reservoirs in practical production.

Recent odor experience selectively modulates olfactory sensitivity across the glomerular output in the mouse olfactory bulb.

Although animals can reliably locate and recognize odorants embedded in complex environments, the neural circuits for accomplishing these tasks remain incompletely understood. Adaptation is likely to be important as it could allow neurons in a brain area to adjust to the broader sensory environment. Adaptive processes must be flexible enough to allow the brain to make dynamic adjustments, while maintaining sufficient stability so that organisms do not forget important olfactory associations. Processing within the mouse olfactory bulb is likely involved in generating adaptation, although there are conflicting models of how it transforms the glomerular output of the mouse olfactory bulb. Here we performed 2-photon Ca2+ imaging from mitral/tufted glomeruli in awake mice to determine the time course of recovery from adaptation, and whether it acts broadly or selectively across the glomerular population. Individual glomerular responses, as well as the overall population odor representation was similar across imaging sessions. However, odor-concentration pairings presented with interstimulus intervals upwards of 30-s evoked heterogeneous adaptation that was concentration-dependent. We demonstrate that this form of adaptation is unrelated to variations in respiration, and olfactory receptor neuron glomerular measurements indicate that it is unlikely to be inherited from the periphery. Our results indicate that the olfactory bulb output can reliably transmit stable odor representations, but recent odor experiences can selectively shape neural responsiveness for upwards of 30 seconds. We propose that neural circuits that allow for non-uniform adaptation across mitral/tufted glomerular could be important for making dynamic adjustments in complex odor environments.

Theoretical study of a triple-band tunable terahertz metamaterial absorber with polarization insensitive and high refractive index sensing based on bulk Dirac semimetal

In this paper, a triple-band metamaterial absorber in the terahertz frequencies is proposed, and its refractive index sensing characteristics are analyzed, where the bulk Dirac semimetal (BDS) periodic array is on top of a photonic crystal slab backed with a metal ground plane. The simulation results show that the absorber achieves three perfect absorption peaks in the range of 3.4–5.2 THz, whose absorption rates are over 96%, and a maximum quality factor (Q) of 74.1. The designed absorber exhibits excellent polarization insensitivity and dynamic tunability; further, the tuning of the Fermi energy level of BDS enables the dynamic adjustment of absorption frequencies and absorption rates of these peaks. By analyzing the distributions of the electromagnetic field and different structural parameters, it is revealed that the absorber mainly dissipates the electromagnetic wave through coupled resonance and localized surface plasmon resonance (LSPR) effects to achieve perfect absorption. Further, the metamaterial absorber shows the capacity to detect analytes with varying refractive indices, and the absorber has a maximum sensitivity S of 405 GHz/RIU with high detection accuracy. This work provides novel design options for triple-band terahertz metamaterial absorbers and their potential applications in refractive index sensing.

Effect of Financial Metrics on Risk Indicators in Nigeria Listed Deposit Money Banks

This study investigates the effect of financial metrics on risk indicators in Nigerian deposit money banks. The analysis employs yearly time series data spanning from 2007 to 2022, acquired from the Exchange Group PLC. Descriptive statistics, panel unit root tests, Hausman tests, and Panel Ordinary Least Squares (OLS) procedures were used at a 95% confidence interval. The study utilized secondary data sourced from the Exchange Group PLC database. The R-squared (0.564390) and Adjusted R-squared (0.540629) values indicate that the models have strong explanatory power. The results show that all variables are stationary at their levels (I(0)). The primary financial metrics influencing risk indicators among deposit money banks in Nigeria are revenue growth, net interest margin, and earnings per share. It was recommended that banks should implement effective risk management systems that can handle increased complexity and scale of operations, and regularly update them, leveraging blockchain technology for decentralized risk management as it relates to revenue growth rate of banks. Maintain a healthy net interest margin through effective risk management practices and internal controls, and utilize this strength to invest in risk mitigation measures, introducing incentive programs to encourage employee involvement in risk management. Conduct regular financial reviews and audits to ensure accurate earnings reporting and risk identification, utilizing AI-powered tools for earnings analysis to identify anomalies and potential risks. Prioritize prudent lending practices and effective risk management to maintain financial stability, implementing dynamic adjustments to the debt-to-equity ratio in response to changes in risk detection needs.

Dynamic confidence-based constraint adjustment in distributional constrained policy optimization: enhancing supply chain management through adaptive reinforcement learning

Dynamic confidence-based constraint adjustment in distributional constrained policy optimization: enhancing supply chain management through adaptive reinforcement learning

Engagement Analysis Using Electroencephalography Signals in Games for Hand Rehabilitation with Dynamic and Random Difficulty Adjustments

Engagement Analysis Using Electroencephalography Signals in Games for Hand Rehabilitation with Dynamic and Random Difficulty Adjustments

Bank Branches Expansion and Corporate Cash Holdings: Evidence from the Distribution of Bank Branches in China

ABSTRACT Using the geographical distribution data of banks’ physical branches at micro-firm level, this paper investigates the impact of bank branches expansion on corporate cash holdings. We find that the more the number of bank branches around firms, the less corporate cash holdings, implying that bank branches expansion helps reduce the level of corporate cash holdings. And the negative effect is more prominent in small-sized firms and non-SOEs. Further mechanism tests show that bank branches expansion reduces cash holdings mainly through intensified bank competition and shortening the geographical distance between bank and firm. We also find that bank branches expansion speeds up the dynamic adjustment to the target level of corporate cash holdings and mitigates agency problems. Our study highlights the crucial role played by the geographical distribution of bank branches in shaping corporate financial decisions and provides important policy implications for banking sector reform.

A lightweight passive ankle exoskeleton adjusted stiffness by shape memory alloy

Purpose The purpose of this paper is to actively calibrate power density to match the application requirements with as small an actuator as possible. So, this paper introduces shape memory alloy to design variable stiffness elements. Meanwhile, the purpose of this paper is also to solve the problem of not being able to install sensors on shape memory alloy due to volume limitations. Design/methodology/approach This paper introduces the design, modeling and control process for a variable stiffness passive ankle exoskeleton, adjusting joint stiffness using shape memory alloy (SMA). This innovative exoskeleton aids the human ankle by adapting the precompression of elastic components by SMA, thereby adjusting the ankle exoskeleton’s integral stiffness. At the same time, this paper constructs a mathematical model of SMA to achieve a dynamic stiffness adjustment function. Findings Using SMA as the driving force for stiffness modification in passive exoskeletons introduces several distinct advantages, inclusive of high energy density, programmability, rapid response time and simplified structural design. In the course of experimental validation, this ankle exoskeleton, endowed with variable stiffness, proficiently executed actions like squatting and walking and it can effectively increase the joint stiffness by 0.2 Nm/Deg. Originality/value The contribution of this paper is to introduce SMA to adjust the stiffness to actively calibrate power density to match the application requirements. At the same time, this paper constructs a mathematical model of SMA to achieve a dynamic stiffness adjustment function.

Rapid motion planning of manipulator in three-dimensional space under multiple scenes

Most working scenes of industrial robots are static scenes and from a previous static scene to a current static scene is a multi-scene. Finding optimal paths with limited time is difficult for motion planning in a high-dimensional space or in multiple scenes. The low efficiency of motion planning of rapidly-exploring random tree star in high-dimensional spaces, low adaptability of global replanning to multiple scenes are addressed by proposing the local replanning based on goal dynamically-guiding rapidly-exploring random tree star (LR-GD-RRT*). The algorithm contributes to fast path tree exploration and multi-scene motion planning. For path tree exploration, sampling points heuristically generating and new nodes growth by goal dynamically-guiding are proposed to reduce the blind and ineffective searches. Moreover, dynamic adjustment of size of new node neighborhood according to density of the obstacles is proposed to search for more neighbor nodes to optimize the path and also to reduce ineffective computation to improve efficiency. For multi-scene, three steps of trimming, re-exploration, and reconnection for local replanning are proposed. For path tree exploration, simulations in 2D plane, 3D space, and the manipulator show that GD-RRT* improves convergence speed, shortens path length and search time, compared with RRT*. For multi-scene, simulations in 3D space and with the manipulator show that the local replanning of the current scene has both lower path cost and higher planning efficiency compared with the global replanning of the previous scene. Motion of the six-degree-of-freedom robot end in a real scene also verifies the effectiveness of the LR-GD-RRT*.

TPSQLi: Test Prioritization for SQL Injection Vulnerability Detection in Web Applications

The rapid proliferation of network applications has led to a significant increase in network attacks. According to the OWASP Top 10 Projects report released in 2021, injection attacks rank among the top three vulnerabilities in software projects. This growing threat landscape has increased the complexity and workload of software testing, necessitating advanced tools to support agile development cycles. This paper introduces a novel test prioritization method for SQL injection vulnerabilities to enhance testing efficiency. By leveraging previous test outcomes, our method adjusts defense strength vectors for subsequent tests, optimizing the testing workflow and tailoring defense mechanisms to specific software needs. This approach aims to improve the effectiveness and efficiency of vulnerability detection and mitigation through a flexible framework that incorporates dynamic adjustments and considers the temporal aspects of vulnerability exposure.

Dynamic prediction and optimization of tunneling parameters with high reliability based on a hybrid intelligent algorithm

In this paper, a hybrid intelligent framework comprising Bayesian optimization (BO), gradient boosting with categorical features (CatBoost) and the nondominated sorting genetic algorithm-III (NSGA-III) was proposed to support multiobjective optimization of shield construction parameters without large sample datasets, improve the shield performance, and ensure reliable and interpretable results. First, with the use of the specific tunneling energy, advancing speed and cutter wear as objective functions, a BO-CatBoost prediction model for shield construction parameters and various objectives was constructed, and the key influencing factors were identified via the SHapley Additive exPlanations (SHAP) method. Then, a BO-CatBoost-NSGA-III model was developed to obtain Pareto solutions under different scenarios involving the adjustment of the key influencing factors. Finally, adopting the Wuhan Metro as the background, the accuracy, stability, and generalizability of the constructed algorithm were verified. The results indicated that (1) the developed BO-CatBoost algorithm is superior to 9 other algorithms. The R2 values of the proposed approach were 0.976 and 0.901–0.976 on the test set. (2) The developed BO-CatBoost-NSGA-III algorithm could be used to obtain Pareto solutions under different scenarios via the adjustment of the key influencing factors with the SHAP method, and the optimal solutions could facilitate improvements in the advancing speed, specific tunneling energy and cutter wear of 3.45 %, 6.09 %, and 0.52 %, respectively, with an overall average reliability of 90.5 %. (3) By comparing various prediction algorithms, optimization schemes of different objectives and geological conditions, the accuracy, stability, and generalizability of the constructed algorithm were verified. The developed BO-CatBoost-NSGA-III framework could enable dynamic adjustment of shield construction parameters for decision-making purposes in the event of conflicting shield construction objectives and exhibits generality.