Adjustment Mechanism Research Articles

Development and Experiment of Adaptive Oolong Tea Harvesting Robot Based on Visual Localization

Aimed to improve the quality of picked tea leaves and the efficiency of tea harvesting, an adaptive oolong tea harvesting robot with an adjustment module of a cutting tool and a harvesting line localization algorithm is proposed. The robot includes a vision measurement module and an adjustment mechanism of a cutting tool, enabling it to assess the shape of tea bushes and adaptively adjust the cutter configuration. To address the challenges of complex tea bush structures and environmental noise, a Prior–Tukey RANSAC algorithm was proposed for accurate harvesting model fitting. Our algorithm leverages prior knowledge about tea bush stem characteristics, uses the Tukey loss function to enhance robustness to outliers, and incorporates workspace constraints to ensure that the cutting tool remains within feasible operational limits. To evaluate the performance of the robot, experiments were conducted in a tea garden in Wuyi Mountain, China. Under ideal conditions, our algorithm achieved an inlier ratio of 43.10% and an R2 value of 0.9787, significantly outperforming traditional RANSAC and other variants. Under challenging field conditions, the proposed algorithm demonstrated robustness, maintaining an inlier ratio of 47.50% and an R2 value of 0.9598. And the processing time of the algorithm met the real-time requirements for effective tea-picking operations. The field experiments also showed an improvement in intact tea rates, from 79.34% in the first harvest to 81.57% in the second harvest, with a consistent usable tea rate of around 85%. Additionally, the robot had a harvesting efficiency of 260.14 kg/h, which was superior to existing handheld and riding-type tea pickers. These results indicate that the robot effectively balances efficiency, accuracy, and robustness, providing a promising solution for high-quality tea harvesting in complex environments.

BLASTFURNACESINTHEAGEOFDECARBONIZATION: THESEARCHFOR COKE ALTERNATIVES

The aim of this work is to analyze the prospects of using hydrogen as an alternative to coke in blast furnaces in the context of decarbonizing the steel industry, particularly considering the introduction of the Carbon Border Adjustment Mechanism (CBAM). It is shown that the widespread notion of a complete replacement of coke with hydrogen needs clarification, as there are technological limitations and economic challenges. According to the analysis of global experience, the use of hydrogen in blast furnaces allows for a reduction of CO2 emissions by 10-70%, which is particularly relevant in the context of CBAM, but requires significant capital investments in the modernization of production and the creation of infrastructure for the production and transportation of hydrogen. It is shown that the efficiency of hydrogen use, and therefore its potential for reducing the cost of carbon quotas within the CBAM framework, depends on the technology of its production, the method of injection into the blast furnace, and the conditions of blast furnace smelting.

Exceptio Inadimpleti Contractus: CBAMs as International Countermeasures

A growing number of states and international organizations, including the EU and the US, are envisaging carbon border adjustment mechanisms (CBAMs) as a means to implement their climate policies. From an economic perspective, CBAMs are instruments aiming to level crossborder trade between countries that implement uneven carbon pricing schemes. From a legal perspective, CBAMs are considered ‘general exceptions’ to free trade, whose lawfulness is debated in the context of the World Trade Organization (WTO). This article develops a systemic analysis of CBAMs, notably the EU CBAM, under general and particular international law, and argues that they could be framed under the specific exception of ‘countermeasures’ under treaty law, particularly the General Agreement on Tariffs and Trade (GATT), thus fundamentally establishing a presumption of legality.

Integrated adaptive communication in multi-agent systems: Dynamic topology, frequency, and content optimization for efficient collaboration

In multi-agent systems (MAS), effective communication is essential for coordination and achieving common goals, especially in complex environments. However, existing communication methods face significant challenges, such as high resource consumption and limited adaptability to dynamic environments. To address these, we propose the integrated adaptive communication network (IACN) built on multi-agent proximal policy optimization (MAPPO), which enhances communication efficiency and adaptability in MAS. IACN dynamically adjusts communication topology using a learnable graph and optimizes content based on task relevance. Additionally, it incorporates an adaptive frequency adjustment mechanism to balance communication demands based on task urgency and environmental changes. Experiments in multi-agent particle environments demonstrate that IACN significantly outperforms existing methods in terms of overall performance, coordination effectiveness, and adaptability.

An Intelligent Maneuver Decision-Making Approach for Air Combat Based on Deep Reinforcement Learning and Transformer Networks

The traditional maneuver decision-making approaches are highly dependent on accurate and complete situation information, and their decision-making quality becomes poor when opponent information is occasionally missing in complex electromagnetic environments. In order to solve this problem, an autonomous maneuver decision-making approach is developed based on deep reinforcement learning (DRL) architecture. Meanwhile, a Transformer network is integrated into the actor and critic networks, which can find the potential dependency relationships among the time series trajectory data. By using these relationships, the information loss is partially compensated, which leads to maneuvering decisions being more accurate. The issues of limited experience samples, low sampling efficiency, and poor stability in the agent training state appear when the Transformer network is introduced into DRL. To address these issues, the measures of designing an effective decision-making reward, a prioritized sampling method, and a dynamic learning rate adjustment mechanism are proposed. Numerous simulation results show that the proposed approach outperforms the traditional DRL algorithms, with a higher win rate in the case of opponent information loss.

Tomato sprayed monocalcium phosphate had production-phytoremediation dual function with high soil Cd extraction and safer fruit production

In order to make use of the large biomass of tomato plant to fulfill the purpose of remediating-while-producing, two commercial tomato varieties, ‘Baiguoqiangfeng’ (BG) and ‘Ouguan’ (OG) were grown in Cd contaminated acidic soil to compare their performance on Cd phytoextraction, and monocalcium phosphate (Ca) was foliar applied to reduce their fruit Cd concentration. The results showed that the BG was a more Cd tolerant variety, comparing with OG, it suffered lighter tissue peroxidation and photosynthesis obstacle, owning weaker amino acid metabolism, secondary metabolism and stress signal transduction under Cd stress. The Ca application reduced its ABA level but increased the GSH, IAA, ZR and GA3 level, and enhanced its lysine degradation, tyrosine metabolism, alanine, asparagine and glutamate metabolism, plant hormone signal transduction and phenylpropanoid biosynthesis under Cd stress. With these metabolic regulations, the Ca application promoted its leaf biomass accumulation, guaranteeing the total Cd extraction amount (0.88 mg pot−1 as 0.20 mg kg −1), and reduced the fruit Cd partition, decreasing the fruit Cd concentration by 71.4% with higher yield. Meanwhile, the OG had lower Cd phytoextraction capacity than the BG, and Ca spray enhanced its cell energy generation, flavonoids biosynthesis and photosynthetic carbon fixation, but had no effect on fruit Cd concentration. The two tomato varieties had different responses to Ca application under Cd stress in their hormone signaling, energy metabolism, secondary metabolism and amino acids metabolism, which furtherly differed their Cd phytoextraction capacity and production safety. Therefore, the monocalcium phosphate spray combined ‘Baiguoqiangfeng’ tomato realized the dual function of production-phytoremediation, and the mechanism of plant Cd sensitivity adjustment through phenylpropanoid biosynthesis and amino acids metabolism deserved further study.

Carbon governance or trade gaming: China's approach to addressing the EU's carbon border adjustment mechanism

The EU's Carbon Border Adjustment Mechanism (CBAM) poses a significant challenge for developing countries, especially China, a key player in the global market and a major carbon emitter. We use the GTAP-E model to evaluate CBAM's impact on China's economy and carbon reduction efforts, and propose two response strategies: carbon governance approaches and international trade gaming. Our findings show that CBAM may lead to a slight decrease of 0.003% in China's GDP and a 0.004% reduction in carbon emissions. It also generates ripple effects across the supply chain, notably affecting energy-intensive upstream industries. We find that the combination of carbon pricing and trade agreements is more effective than trade protection measures in minimizing economic losses from CBAM. This is achieved through mechanisms such as sharing carbon reduction costs and promoting decarbonization on both the supply and demand sides. Furthermore, improving carbon governance can enhance the green competitiveness of Chinese products. Going forward, China should maintain trade agreements with developing countries and enhance its carbon pricing mechanism to better align with global climate governance. Our study offers valuable policy insights for developing countries as they navigate the challenges posed by CBAM.

The effect of alimony on married women's labor supply and fertility: Evidence from state‐level reforms

AbstractReforms that reduce spousal support after divorce are generally thought to reduce the bargaining power of alimony recipients as well as their incentives to participate in the traditional model of household specialization. Using the U.S. Time Use Survey and exploiting a series of recent reforms in several U.S. states that reduced the rights of eligible spouses, we find that wives surprised by the reforms reacted by increasing their labor supply, but the adjustment mechanism varied by educational group. While college educated wives' labor supply increased at the expense of time spent on housework and childcare, wives with less than a college degree sacrificed personal care and leisure time. Effects for men are generally smaller and not statistically significant. We also find a reduction in the rate of new births following the reforms, with the effect being much more pronounced in the case of women with a university education, suggesting that part of the difference in time use may be attributable to differences in preferences and the cost of children by educational levels. Since children are a marital public good, our results imply a decrease in marriage‐specific investment. The estimated effects are robust to various sensitivity tests and are greater in couples with a high‐income differential and therefore more exposed to changes in alimony laws.

Deep reinforcement learning-based local path planning in dynamic environments for mobile robot

Path planning for robots in dynamic environments is a challenging task, as it requires balancing obstacle avoidance, trajectory smoothness, and path length during real-time planning.This paper proposes an algorithm called Adaptive Soft Actor–Critic (ASAC), which combines the Soft Actor–Critic (SAC) algorithm, tile coding, and the Dynamic Window Approach (DWA) to enhance path planning capabilities. ASAC leverages SAC with an automatic entropy adjustment mechanism to balance exploration and exploitation, integrates tile coding for improved feature representation, and utilizes DWA to define the action space through parameters such as target heading, obstacle distance, and velocity In this framework, the action space is defined by DWA’s three weighting parameters: target heading deviation, distance to the nearest obstacle, and velocity. To facilitate the learning process, a non-sparse reward function is designed, incorporating factors such as Time-to-Collision (TTC), heading, and velocity. To validate the effectiveness of the algorithm, experiments were conducted in four different environments, and the algorithm was evaluated based on metrics such as trajectory deviation, smoothness, and time to reach the end point. The results demonstrate that ASAC outperforms existing algorithms in terms of trajectory smoothness, arrival time, and overall adaptability across various scenarios, effectively enabling path planning in dynamic environments.

Three-point adaptive preview steady-state compensation driver model based on articulated heavy vehicle

This article proposes a three-point adaptive preview steady-state compensation driver model based on articulated heavy vehicles (AHVs). Although AHVs have extremely high road transportation efficiency, they have poor driving stability and high accident risk during high-speed transportation due to their large weight, high center of mass and multi-body characteristics. However, there are few studies on intelligent driving control of AHVs. In order to improve the active safety performance of AHVs, a three-point adaptive preview driver model is designed in this article. Based on the driver model, a sliding mode steady-state compensation controller is added. The three-point adaptive preview driver is different from other driver models. Its characteristic is that the preview distance can be automatically adjusted according to the vehicle speed and road curvature. A driver model with three-point preview weighted deviation, preview time, current vehicle speed and vehicle state as input and front wheel angle as output is established. Considering the trailer state, the front wheel angle is compensated and corrected by sliding mode steady-state compensation controller. The simulation results show that the model can reasonably judge the relationship between the target path and the vehicle position, adapt to the dynamic adjustment mechanism of the preview distance, and has good tracking accuracy. The front wheel compensation angle provided by the sliding mode controller under high speed and extreme conditions can ensure that the AHVs can track the target trajectory while maintaining driving stability.

Research on the Value Creation Mechanism of Green-Driven Products in the Internet+ Era

In order to improve the value of green-driven products, this paper studies the value creation mechanism of green-driven products in the Internet+ era. Under the framework of the classic Cournot output game, based on the bounded rationality assumption of the participants, this paper constructs a novel Cournot duopoly investment game model, and analyzes the dynamic adjustment mechanism of the participants' investment. The decision variable of participating enterprises is to select the optimal investment amount, and each production enterprise makes investment decisions in the next period according to the observed marginal profit of the current period to maximize the profit. Through calculation and analysis, it can be known that three boundary equilibriums and one interior point equilibrium of the discrete dynamic model system are obtained in this paper. The experimental analysis shows that the value creation mechanism of green-driven products proposed in this paper can effectively promote the value of green-driven products.

A Three-Party Dynamic Pricing Mechanism for Customized Data Products Based on the Stackelberg Game and Bargaining Model

In the era of big data, breaking down data silos to enable efficient data transactions has become essential, with the fairness and transparency of pricing mechanisms being paramount. This study addresses these challenges by introducing a novel tripartite pricing model for customized data products that integrates the Stackelberg and bargaining game frameworks. By designing distinct utility functions for buyers, sellers, and the platform, the model effectively aligns the varying objectives of each participant. A dynamic adjustment mechanism further enhances this model by adaptively recalibrating the guidance price and pricing range based on real-time updates to buyer budgets and seller offers, thus ensuring fairness and responsiveness throughout the negotiation process. Experimental simulations comprising 100 transaction rounds across diverse buyer–seller profiles validate the model’s effectiveness, achieving a transaction success rate of 92.70% with an average of 6.86 bargaining rounds. These findings underscore the model’s capacity to optimize transaction outcomes, promote pricing equity, and enhance bargaining efficiency. The proposed model has broad applications in sectors such as finance, healthcare, and e-commerce, where precise data pricing mechanisms are essential to maximize transactional value.

Research on Interaction Effect Between Rural Roads and Peasants' Income Based on Granger Causality Test

This study aims to address the lack of methods for adjusting the scale of online ride hailing capacity. Taking G city as an example, a dynamic monitoring index system for the scale of online ride hailing capacity is constructed by comprehensively considering factors such as order volume, driver labor hours and income levels, and the operation status of the taxi market. Pearson correlation analysis and stepwise linear regression methods are applied to screen the indicators, and key indicators and threshold standards for the dynamic adjustment mechanism of online ride hailing capacity are proposed, providing theoretical basis and decision support for the adjustment of urban online ride hailing capacity scale

Research on the Dynamic Adjustment Mechanism of Online Ride Hailing Capacity Scale

This study aims to address the lack of methods for adjusting the scale of online ride hailing capacity. Taking G city as an example, a dynamic monitoring index system for the scale of online ride hailing capacity is constructed by comprehensively considering factors such as order volume, driver labor hours and income levels, and the operation status of the taxi market. Pearson correlation analysis and stepwise linear regression methods are applied to screen the indicators, and key indicators and threshold standards for the dynamic adjustment mechanism of online ride hailing capacity are proposed, providing theoretical basis and decision support for the adjustment of urban online ride hailing capacity scale

Research on the design of posture adjustment structure and control system of carrying robot based on the bionic characteristics of pupae structure

This paper addresses the challenge of incorrect initial positioning of critical patients during lifting and transportation, which can result in secondary injuries due to manual handling. This study proposes a positioning adjustment structure and control strategy for a lifting and transportation robot, inspired by the morphological characteristics of the pupal structure. The positioning adjustment mechanism determines the necessary adjustment values for the patient’s positioning by establishing a human body coordinate system and calculating the difference between the patient’s actual position and the target position. This mechanism achieves optimal positioning by simulating the oscillation of body segments characteristic of the pupal structure. Additionally, this study establishes a dynamic mathematical model for the overall lifting structure and determine its motion parameters. An order for patient body positioning and adjustment is derived based on the twisting amplitude of the human body. A PID controller is designed for the overall transport structure, incorporating optimization through a particle swarm optimization algorithm. This methodology efficiently mitigates excessive overshoot and accelerates convergence towards the target value, significantly minimizing patient discomfort and fluctuation during the positioning procedure.

Climate Agenda in Russia: Shifting Guidelines and New Challenges

The changing of a geopolitical situation and a reorientation of Russian exports to the East were starting an adjustment the climate agenda took place in Russia, which determined the relevance of the research topic. The shift in the primary drivers of the agenda, coupled with the potential for adverse effects on the Russian real economy due to the implementation of cross-border carbon regulations within the European Union, has extended the duration of the transformational process. However, the commitment to transitioning towards a more environmentally friendly approach remains unchanged. In light of the observed change in business focus, the advancement of the climate agenda requires significant efforts from the government. Carbon neutrality and high environmental standards is economically feasible in the medium and long term. At the same time, Russia’s new key partners in the East are actively interacting with the West, which encourages them to act in line with Carbon Border Adjustment Mechanism. Therefore, these standards will eventually extend to Russian companies. The “green” transformation in domestic industries will mitigate the potential dangers of stricter carbon regulations in the East, while also providing additional competitive advantages for the Russian economy.

Delay management for heterogeneous traffic in vehicular sensor networks using packet fragmentation of low priority data

AbstractVehicular sensor networks (VSNs) are expected to revolutionize the transportation systems through automated decision‐making. These networks function on the communication between vehicles, traffic infrastructure, and people. One of the major challenges of VSNs is to deal with heterogeneous traffic due to the diverse nature of information and data generated by different types of sensors and devices within the vehicular environment. To ensure an effective operation of the network, it is crucial to offer a differentiated quality of service to each traffic class. Media access control (MAC) protocols offer an opportunity to provide differentiated channel access to the traffic of different priorities. This work is focused on offering comparison of recent MAC schemes in terms of their performance for heterogeneous traffic generated by VSNs. Two protocols FROG‐MAC and UrgMAC have been selected: FROG‐MAC is developed using a simple concept of packet fragmentation, where the packets of lower priority are fragmented to offer an early transmission opportunity to urgent traffic; on the other hand, urgMAC is designed using advanced cross‐layer techniques of two‐tiered service differentiation mechanism, adaptive data rate adjustment mechanism, urgency‐based contention window size adaptation, traffic type adaptive duty cycle, and multimedia message passing. It has been found that the FROG‐MAC outperforms urgMAC in terms of delay, energy consumption and packet delivery ratio.

The dynamic adjusted system of sea use fees: An empirical research on the sea use management in Zhejiang province

To prevent excessive exploitation of sea resources by developers, countries around the world have established systems for managing sea use. The system of sea use fees in China is continually improving, but it cannot reflect the full value of marine natural resources. Therefore, an optimization mechanism for dynamic adjustment which considered the value of marine natural resources and the adjustment coefficient of sea area fees was constructed in the study. This study explored the difference between marine resources zoning and the dynamic adjustment standard of sea use fees collection and took Zhejiang province as an example. The results showed that the comprehensive scores of coastal marine resources were generally higher than those of the open sea in Zhejiang province. Furthermore, the comprehensive scores of important ecological areas such as bays and islands were higher than those of other marine areas, and the comprehensive score of Ningbo sea area was significantly higher than those of other sea areas. On the basis, there are six classes of the adjustment range of the sea use fees standard were divided in Zhejiang province in the study. The overall increase of fee was 3.9–8.0 %. The results of study can assist decision-making by evaluating the value of marine resources and the adjustment of sea area usage royalties, which will help to improve the efficiency of sea area management.

Theory of AI-driven scheduling (TAIS): a service-oriented scheduling framework by integrating theory of constraints and AI

This paper introduces the Theory of AI-driven scheduling (TAIS), an innovative framework designed to revolutionise service-oriented scheduling (In the context of this paper, ‘service-oriented’ refers to the design and execution of scheduling processes that are specifically tailored to meet the dynamic and varied needs of service-manufacturing industries. This approach emphasises adaptability, customer-centric scheduling, and the efficient allocation of resources to enhance product and service delivery.) by integrating the theory of constraints (TOC) (see APPENDIX I) with cutting-edge Artificial Intelligence (AI) technologies. TAIS extends the traditional five steps of TOC by introducing three additional layers to enhance adaptability, efficiency, and applicability in service-oriented environments. TAIS incorporates continuous monitoring and adjustment mechanisms across all five TOC steps to improve adaptability, ensuring real-time responsiveness to dynamic scheduling challenges. To enhance efficiency, TAIS integrates lifecycle management and governance into the core framework, which helps in the efficient allocation of resources throughout the scheduling process. Lastly, TAIS includes robust security and compliance measures to bolster applicability. By leveraging AI, TAIS offers a predictive, flexible, and scalable solution capable of addressing the complexities and rapid changes inherent in dynamic scheduling tasks and providing a significant leap forward in scheduling and operations management.

Research on Pose Error Modeling and Compensation of Posture Adjustment Mechanism Based on WOA-RBF Neural Network

This paper is aimed to address the issue of decreased accuracy in the ship block docking caused by the structural errors of posture adjustment mechanism. First, inverse kinematic analysis is performed to investigate the sources of static errors in the mechanism. Subsequently, based on the closed-loop vector method, a pose error model for the moving platform is established, which includes eight categories of error terms. The impact of various structural errors on the pose accuracy of the moving platform is then compared and analyzed under both single-limb and multi-limb configurations. Therefore, a compensation method based on the whale optimization algorithm optimized radial basis function neural network is proposed. By transforming pose errors into actuator length errors, it establishes a predictive model between the theoretical pose of the dynamic platform and actuator length errors. After optimizing the network parameters, it yields the actuator length compensation to correct the actual pose of the dynamic platform. Simulation and experimental results validate the effectiveness of this method in enhancing the motion accuracy of the parallel mechanism. The mean pose accuracy of the moving platform is improved by 85.07%, demonstrating a significant compensation effect.