Hasil untuk "Balance of trade"

H. Grubel

Richard E. Baldwin, Richard E. Baldwin, Richard E. Baldwin

E. Milner‐Gulland, E. Bennett

A. Rose, Janet L. Yellen

Alexander Kotrschal, B. Rogell, Andreas Bundsen et al.

Summary The large variation in brain size that exists in the animal kingdom has been suggested to have evolved through the balance between selective advantages of greater cognitive ability and the prohibitively high energy demands of a larger brain (the “expensive-tissue hypothesis” [1]). Despite over a century of research on the evolution of brain size, empirical support for the trade-off between cognitive ability and energetic costs is based exclusively on correlative evidence [2], and the theory remains controversial [3, 4]. Here we provide experimental evidence for costs and benefits of increased brain size. We used artificial selection for large and small brain size relative to body size in a live-bearing fish, the guppy (Poecilia reticulata), and found that relative brain size evolved rapidly in response to divergent selection in both sexes. Large-brained females outperformed small-brained females in a numerical learning assay designed to test cognitive ability. Moreover, large-brained lines, especially males, developed smaller guts, as predicted by the expensive-tissue hypothesis [1], and produced fewer offspring. We propose that the evolution of brain size is mediated by a functional trade-off between increased cognitive ability and reproductive performance and discuss the implications of these findings for vertebrate brain evolution.

E. Kato, T. Ophelders, A. Telea et al.

Gradient meshes are powerful vector graphic primitives known for producing smooth and detailed color transitions. However, their fixed rectangular topology complicates editing, as adding detail in one region introduces control points across the entire mesh. To improve editability and better support artist workflows, we propose a simplification method for gradient meshes based on local refinement. Our method transforms a traditional, globally-refined mesh into a locally-refined one by iteratively merging adjacent faces and eliminating redundant data while preserving visual quality. We achieve this by rasterizing the mesh and applying established visual quality metrics to ensure consistency with the original. Additionally, we offer artists control over the simplification process by introducing an error threshold, allowing them to balance the level of simplification with visual fidelity. Finally, we conduct a thorough comparison of various mesh simplification strategies to analyze the trade-offs between simplification quality and speed so as to inform users to the optimal one that they can use to obtain the desired trade-off.

Hongseok Kim, Joo-Hang Lee, S. Bahrami et al.

Recent advancement of distributed renewable generation has motivated microgrids to trade energy directly with one another, as well as with the utility, in order to minimize their operational costs. Energy trading among microgrids, however, confronts challenges such as reaching a fair trading price, maximizing participants’ profit, and satisfying power network constraints. In this paper, we formulate the direct energy trading among multiple microgrids as a generalized Nash bargaining (GNB) problem that involves the distribution network's operational constraints (e.g., power balance equations and voltage limits). We prove that solving the GNB problem maximizes the social welfare and also fairly distributes the revenue among the microgrids based on their market power. To address the nonconvexity of the GNB problem, we propose a two-phase approach. The first phase involves solving the optimal power flow problem in a distributed fashion using the alternative direction method of multipliers to determine the amount of energy trading. The second phase determines the market clearing price and mutual payments of the microgrids. Simulation results on an IEEE 33-bus system with four microgrids show that the proposed framework substantially reduces total network cost by 37.2%. Our results suggest direct trading need be enforced by regulators to maximize the social welfare.

Vivian Z. Yue

Yun Shu, Yang Li, Yazhen Wu et al.

Climate policies that target carbon emissions can induce co-benefits for air quality. Previous urban studies have typically focused on either carbon reduction or air pollution control independently, but few have examined their combined effects on reducing carbon emissions and consequential environmental gains. We develop an integrated modelling framework to assess the impacts of different low-carbon transitions and end-of-pipe controls on PM2.5 and ozone concentrations and associated premature mortality in the megacity of Guangzhou. The results show that the implementation of both deep carbon mitigation and aggressive air pollution control policies can reduce the city's pollutant emissions to 34%–51% of the 2020 levels by 2035. Consequently, the population-weighted PM2.5 concentration in 2035 is projected to decrease by 5 μg/m3 compared to the 2035 baseline scenario. However, the ozone concentration is expected to rise by 35 μg/m3 due to the reduced titration effect of NO on ozone. These changes are estimated to prevent approximately 3.0 thousand (95% CI: 2.0–3.9) PM2.5-related premature deaths, while increasing ozone-related premature deaths by approximately 1.6 thousand (95% CI: 0.7–2.7). Moreover, implementing multiregional integrated control measures in Guangzhou and its neighbouring cities yields greater air quality and health benefits for Guangzhou compared to local enforcement alone, resulting in 1.5 times more avoided PM2.5-related premature deaths. Additionally, the increase in ozone-related premature deaths from these cooperative emission control strategies is merely 0.3 times the figure observed under local enforcement alone. The transport and industry sectors play a crucial role in reducing air pollutant emissions, whereas reductions in the solvent use sector can help mitigate the adverse effects of reduced NOx on ozone pollution. These findings highlight the need for comprehensively multiregional strategies to balance the trade-offs between reducing PM2.5 and ozone-related health impacts, offering valuable insights for urban policy makers aiming to optimize both climate and air quality goals on a broader scale.

Jesica de Armas, José A. Moreno-Pérez

Sustainable logistics aims to balance economic efficiency, environmental responsibility, and social well-being in supply chain operations. This study explores the use of Variable Neighborhood Search (VNS), a metaheuristic optimization method, in addressing sustainable logistics challenges and provides insights into the potential it has to support them by delivering efficient solutions that align with global sustainability goals. The review identifies key trends, including a significant increase in research since 2019, with a strong focus on routing, scheduling, and location problems. Hybrid approaches, combining VNS with other methods, and multiobjective optimization to address trade-offs between sustainability goals are prominent. The most frequently applied VNS versions align closely with those commonly used in the broader literature, reflecting similar adoption proportions. In recent years, a noticeable increase in studies incorporating adaptation mechanisms into VNS frameworks has emerged. This trend is largely driven by the growing influence of Artificial Intelligence approaches across numerous fields of science and engineering, highlighting the need for more dynamic and intelligent optimization techniques. However, important research gaps remain. These include limited consideration of uncertainty and dynamic logistics systems, underrepresentation of social sustainability, and a lack of standardized benchmarks for comparing results. Future work should address these challenges and explore emerging applications.

Nur Hasyimah Ramli, Nursyazni Abdul Rahim, Nur Azimah Osman et al.

Mangrove forests have exceptional carbon sequestration capacity for mitigating climate change impacts. Increased atmospheric CO2 can accelerate crops growth, improves water-use efficiency, and disrupt soil-plant balance. The performance of Avicennia alba in terms of morphometrics and biomass under environmental stresses such as elevated CO2 was poorly understood. Thus, this study aims to determine the growth response and survivability of A. alba by examining height, leaf number, and growth rate under elevated CO2 from the early stages of development. A number of 120 seed samples of A. alba was divided into two groups; 60 germinated seeds placed in a CO2 incubator and 60 in a shade house as control. The growth rate, plant height, leaf number, and mortality were compared between the two groups and statistical analyses were conducted. Increased concentrations of CO₂ enhance the development and survival of seedlings by promoting greater photosynthesis and more effective water use. The decrease in leaf production is most likely due to a shift in resource allocation, where plants prioritize the accumulation of total biomass over leaf formation. An understanding of this trade-off elucidates the potential response of plants to increasing CO₂ levels in climate change scenarios.

Yutong Zhang, Cheng Wei, Xibin Cao

This paper presents a model-based framework for the dynamic verification of spacecraft systems, which tightly integrates an executable systems modeling language architectural model with the in-house orbital analysis tool SpaceSim to achieve a closed-loop workflow encompassing system design, analysis, and verification. The method is abstracted into 2 generic types of meta-models: the co-simulation meta-model, which captures the structure of co-simulation commands and data formats, and the system-of-interest meta-model, which ensures hierarchical and modular system architectures, thereby supporting flexible iterative design and verification. The proposed framework is demonstrated through a space mission case study, in which dynamic simulation is used to compute key performance indicators such as energy and information flow balance and to validate associated requirements in real time. The adaptability of the approach is further evaluated through multiple simulated mission change scenarios across 3 dimensions: simulation context, system behavior, and parameter modification. Results indicate that the proposed method effectively reduces the complexity and effort required for model updates and enhances the overall flexibility of system analysis. This study offers a generalizable paradigm for integrating model-based systems engineering with domain-specific simulation tools, laying the groundwork for subsequent high-fidelity model replacement, trade-off analysis, and optimization-based design.

Matias Fernández-Jorquera, Marco Zepeda-Rabanal, Norelys Aguila-Camacho et al.

Stabilizing inverted pendulum systems remains a challenging and open control problem due to their inherent instability and relevance in a wide range of real-world applications, including robotics and aerospace systems. While PID and fractional-order PID (FOPID) controllers offer distinct advantages, they individually suffer from trade-offs between performance and control energy. This paper presents the design, implementation, and experimental validation of a switched SW FOPID-PID controller for the stabilization of an inverted pendulum (InvP) system, aiming to achieve an improved balance between system performance and control energy used. The controller was tuned offline using particle swarm optimization (PSO) and a mathematical model of the system for simulation. Additional PID and FOPID controllers were also designed, tuned and validated for comparison purposes. Their performance was assessed through key indicators, including ITAE, ISI, settling time, peak values, and variance and compared against a manufacturer-provided PID controller. The experimental results demonstrated that all three designed controllers outperformed the manufacturer’s PID under nominal conditions. The SW FOPID-PID controller achieved the best overall performance, balancing control energy efficiency and response quality. Under external disturbances, the FOPID and SW FOPID-PID controllers exhibited superior robustness, with the switched controller being the most effective, responding quickly to disturbances while minimizing positional and angular errors. Still, this research is limited to a specific plant and switching strategy; thus, further validation on other systems and switching criteria is necessary to generalize these findings.

Kaleem Arshid, Ali Krayani, Lucio Marcenaro et al.

The development of efficient and reliable trajectory-planning strategies for swarms of unmanned aerial vehicles (UAVs) is an increasingly important area of research, with applications in surveillance, search and rescue, smart agriculture, defence operations, and communication networks. This article provides a comprehensive and critical review of the various techniques available for UAV swarm trajectory planning, which can be broadly categorised into three main groups: traditional algorithms, biologically inspired metaheuristics, and modern artificial intelligence (AI)-based methods. The study examines cutting-edge research, comparing key aspects of trajectory planning, including computational efficiency, scalability, inter-UAV coordination, energy consumption, and robustness in uncertain environments. The strengths and weaknesses of these algorithms are discussed in detail, particularly in the context of collision avoidance, adaptive decision making, and the balance between centralised and decentralised control. Additionally, the review highlights hybrid frameworks that combine the global optimisation power of bio-inspired algorithms with the real-time adaptability of AI-based approaches, aiming to achieve an effective exploration–exploitation trade-off in multi-agent environments. Lastly, the article addresses the major challenges in UAV swarm trajectory planning, including multidimensional trajectory spaces, nonlinear dynamics, and real-time adaptation. It also identifies promising directions for future research. This study serves as a valuable resource for researchers, engineers, and system designers working to develop UAV swarms for real-world, integrated, intelligent, and autonomous missions.

Weiqi Hua, Jing Jiang, Hongjian Sun et al.

Abstract Prosumers are active participants in future energy systems who produce and consume energy. However, the emerging role of prosumers brings challenges of tracing carbon emissions behaviours and formulating pricing scheme targeting on individual prosumption behaviours. This paper proposes a novel blockchain-based peer-to-peer trading framework to trade energy and carbon allowance. The bidding/selling prices of prosumers can directly incentivise the reshaping of prosumption behaviours to achieve regional energy balance and carbon emissions mitigation. A decentralised low carbon incentive mechanism is formulated targeting on specific prosumption behaviours. Case studies using the modified IEEE 37-bus test feeder show that the proposed trading framework can export 0.99 kWh of daily energy and save 1465.90 g daily carbon emissions, outperforming the existing centralised trading and aggregator-based trading.

Naoto Shiraishi, K. Funo, Keiji Saito

We consider the speed limit for classical stochastic Markov processes with and without the local detailed balance condition. We find that, for both cases, a trade-off inequality exists between the speed of the state transformation and the entropy production. The dynamical activity is related to a time scale and plays a crucial role in the inequality. For the dynamics without the local detailed balance condition, we use the Hatano-Sasa entropy production instead of the standard entropy production. Our inequalities consist of the quantities that are commonly used in stochastic thermodynamics and explicitly show underlying physical mechanisms.

Guillaume Plantin, H. Sapra, H. Shin

M. Yu. Malkina, R. V. Balakin

Objective: to identify trends in the development of the Russian economy during the new anti-Russian sanctions. To this end, the paper analyzes the impact of the new sanctions on the budgetary sphere, the development of economic sectors and industries and the Russian regions.Methods: the work uses data from the Federal State Statistics Service (Rosstat), the Federal Treasury, the Ministry of Economic Development of the Russian Federation (Ministry of Economic Development), the Central Bank of the Russian Federation (Bank of Russia), the Federal Tax Service (FTS), the Federal Customs Service (FCS), the Federal Antimonopoly Service (FAS). For their analysis, graphical, tabular, and analytical methods are used.Results: the new anti-Russian sanctions caused significant changes in the structure of the Russian exports and trade balance. The reduced inflow of foreign currency put significant pressure on the ruble exchange rate, which created inflation risks. The devaluation was restrained by the Bank of Russia new currency regime. The favorable situation in the energy market in 2022 led to an increase in budget revenues. In 2023, economic growth resulted from an active fiscal policy supported by moderate monetary expansion. However, it was achieved at the cost of an increase in the budget deficit and public debt. The response of different industries and regions to the sanctions shock was heterogeneous. The development of certain adaptive strategies by economic entities, as well as the response of the authorities played an important role. The new sanctions regime prompted a revision of the state economy regulation paradigm. Dirigisme manifested itself in the increase of state orders, subsidies and other support for protected industries with one-time withdrawals of opportunistic revenues of big business. Monetary and fiscal policy gradually acquired a hybrid character. Direct and indirect price regulation became an important instrument of state regulation during the sanctions.Scientific novelty: it consists in a comprehensive analysis of development trends during the new anti-Russian sanctions, their impact on foreign economic parameters, budgetary sphere, financial system, development of Russian industries and regions.Practical significance: statistical information on various aspects of the Russian economy development during the new antiRussian sanctions was summarized and analyzed. The work can be useful to researchers studying the functioning of the Russian economy under sanctions, as well as to businesspersons for assessing the risks, opportunities and prospects of their businesses development, and to government agencies for forming an effective anti-crisis policy.

Chockri Thabet

This paper explores the evolution and future perspectives of agricultural policies in Tunisia, focusing on their role in enhancing food security. The agricultural sector, while contributing around 9% to GDP and employing 16% of the active population, faces numerous challenges including water scarcity, climate change, and economic pressures from international trade. The study identifies that despite economic diversification, agriculture remains crucial for rural livelihoods and food security. Also, the paper critiques existing policies, particularly the inefficiencies in subsidies and the complexity of administrative procedures, which disadvantage small farmers. The analysis underscores the need for policy reforms aimed at improving farmers’ incomes, reducing policy costs, and enhancing efficiency. Recommendations include developing infrastructure, promoting modern agricultural technologies, and adjusting trade policies to better balance export promotion with import substitution. The study concludes that a dynamic and transparent agricultural policy, responsive to international changes and inclusive of all farmer categories, is essential for sustainable agricultural development and food security in Tunisia.

Estrella Gomez-Herrera, B. Martens, Geomina Turlea