Hasil untuk "Balance of trade"

Yingyi Qian, Yingyi Qian, Gérard Roland et al.

C. Hwang, A. Masud

A. P. Miettinen, J. Nurminen

Z. Baida, F. Koldijk, Yao-Hua Tan et al.

D. Backus, P. Kehoe

We document properties of business cycles in ten countries over the last hundred years, contrasting the behavior of real quantities with that of the price level and the stock of money. Although the magnitude of output fluctuations has varied across countries and periods, relations among variables have been remarkably uniform. Consumption has generally been about as variable as output, and investment substantially more variable, and both have been strongly procyclical. The trade balance has generally been countercyclical. The exception to this regularity is government purchases, which exhibit no systematic cyclical tendency. With respect to the size of output fluctuations, standard deviations are largest between the two world wars. In some countries (notably Australia and Canada) they are substantially larger prior to World War I than after World War II, but in others (notably Japan and the United Kingdom) there is little difference between these periods. Properties of price levels, in contrast, exhibit striking differences between periods. Inflation rates are more persistent after World War II than before, and price level fluctuations are typically procyclical before World War II, countercyclical afterward. We find no general tendency toward increased persistence in money growth rates, but find that fluctuations in money are less highly correlated with output in the postwar period.

A. Esmat, Martijn de Vos, Y. Ghiassi-Farrokhfal et al.

Abstract Peer-to-Peer (P2P) energy trading, which allows energy consumers/producers to directly trade with each other, is one of the new paradigms driven by the decarbonization, decentralization, and digitalization of the energy supply chain. Additionally, the rise of blockchain technology suggests unprecedented socio-economic benefits for energy systems, especially when coupled with P2P energy trading. Despite such future prospects in energy systems, three key challenges might hinder the full integration of P2P energy trading and blockchain. First, it is quite complicated to design a decentralized P2P market that keeps a fair balance between economic efficiency and information privacy. Secondly, with the proliferation of storage devices, new P2P market designs are needed to account for their inter-temporal dependencies. Thirdly, a practical implementation of blockchain technology for P2P trading is required, which can facilitate efficient trading in a secured and fraud-resilient way, while eliminating any intermediaries’ costs. In this paper, we develop a new decentralized P2P energy trading platform to address all the aforementioned challenges. Our platform consists of two key layers: market and blockchain. The market layer features a parallel and short-term pool-structured auction and is cleared using a novel decentralized Ant-Colony Optimization method. This market arrangement guarantees a near-optimally efficient market solution, preserves players’ privacy, and allows inter-temporal market products trading. The blockchain layer offers a high level of automation, security, and fast real-time settlements through smart contract implementation. Finally, using real-world data, we simulate the functionality of the platform regarding energy trading, market clearing, smart contract operations, and blockchain-based settlements.

I. Chen, Fredrik D. Johansson, D. Sontag

Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy. In sensitive applications such as healthcare or criminal justice, this trade-off is often undesirable as any increase in prediction error could have devastating consequences. In this work, we argue that the fairness of predictions should be evaluated in context of the data, and that unfairness induced by inadequate samples sizes or unmeasured predictive variables should be addressed through data collection, rather than by constraining the model. We decompose cost-based metrics of discrimination into bias, variance, and noise, and propose actions aimed at estimating and reducing each term. Finally, we perform case-studies on prediction of income, mortality, and review ratings, confirming the value of this analysis. We find that data collection is often a means to reduce discrimination without sacrificing accuracy.

M. Zuk, Andrew M Stoehr

H. Arslan, Irfan Khan, M. I. Latif et al.

Andrew M. Liebhold, E. Brockerhoff, L. Garrett et al.

K. Laursen

A. Kelly, W. Becker, E. Helsing

N. Metcalfe, C. Alonso-Alvarez

M. Amirul Ghiffari, Febri Dolis Herdiani, Ishak Ariawan et al.

Food safety requires rapid and accurate bacterial identification to prevent disease and economic losses. This study compares three lightweight deep learning models—Tiny-ViT, ShuffleNetV2, and EfficientNet-Lite—for classifying 33 bacterial species from a combined public dataset. Models were trained using transfer learning with original and augmented data and evaluated using 5-fold cross-validation. Tiny-ViT achieved the highest performance with 99.66% accuracy and 99.70% precision, setting a new state-of-the-art for the DIBaS dataset. EfficientNet-Lite reached 99.32% accuracy with superior efficiency—threefold lower FLOPs (397.49M), fewer parameters (3.41M), and faster inference (0.90 ms/image). Comparison of per-class error rates across four models—Tiny-ViT Original, Tiny-ViT Augmented, EfficientNet-Lite Augmented, and EfficientNet-Lite Original—showed consistent stability, where each bacterial class exhibited low mean error and narrow 95% confidence intervals (CI95%), reflecting statistical reliability. These findings highlight a trade-off: Tiny-ViT offers maximum accuracy, while EfficientNet-Lite provides optimal accuracy–efficiency balance for edge-based bacterial diagnostics.

Roberta Ramilli, Nicola Lowenthal, Marco Crescentini et al.

Multisine electrochemical impedance spectroscopy (EIS) represents a highly promising technique for the online characterization of battery functional states, offering the potential to monitor, in real-time, key degradation phenomena such as aging, internal resistance variation, and state of health (SoH) evolution. However, its widespread adoption in embedded systems is currently limited by the need to balance measurement accuracy with strict energy constraints and the requirement for short acquisition times. This work proposes a novel broadband EIS approach based on a multiband multisine excitation strategy in which the excitation signal spectrum is divided into multiple sub-bands that are sequentially explored. This enables the available energy to be concentrated on a limited portion of the spectrum at a time, thereby significantly improving the signal-to-noise ratio (SNR) without substantially increasing the total measurement time. The result is a more energy-efficient method that maintains high diagnostic precision. We further investigated the optimal design of these multiband multisine sequences, taking into account realistic constraints imposed by the sensing hardware such as limitations in excitation amplitude and noise level. The effectiveness of the proposed method was demonstrated within a comprehensive simulation framework implementing a complete impedance measurement system. Compared with conventional excitation techniques (i.e., the sine sweep and the classical single-band multisine methods), the proposed strategy is an optimal trade-off solution both in terms of energy efficiency and measurement time. Therefore, the technique is a valuable solution for real-time, embedded, and in situ battery diagnostics, with direct implications for the development of intelligent battery management systems (BMS), predictive maintenance, and enhanced safety in energy storage applications.

Kanaga Bharathi N, Manoharan Abirami, Devi Vighneshwari et al.

Abstract This study is focusing on the techno-economic optimization of hybrid renewable energy systems and the energy. The system integrates geothermal, wind, and solar sources for sustainable hydrogen production its important. The objective is to maximize energy efficiency, reduce operational costs, and ensure stable energy delivering. A simulation-based framework is used for analyse system behaviour under various environmental conditions it helps. The scope includes defining parameter, sensitivity analysis, and optimization using iterative algorithms which are complex. Time-step simulations evaluating energy dynamics help to and performance trade-offs, which is necessary for understanding. The proposed hybrid system achieves 78.5% energy efficiency and 64.3% exergy efficiency, and this is good. It produces 500 kg of hydrogen daily with an LCOE of $0.085 per kWh, which is quite low. Sensitivity results show that a 15% increase in wind speed improves output by 10%, and this is significant. A 20% drop in solar irradiance reduces output by 8%, which is not good. Geothermal contributes 40% of the total energy share, with wind and solar supplying 35% and 25%, respectively, and this shows balance. Optimization improves hydrogen production efficiency by 12% and leads to a six-year payback period, which is reasonable. The system shows resilience under load changes, supporting its robustness that is impressive. The findings validate the system’s scalability and economic potential, which is promising for future. Future work will explore advanced storage and real-time adaptive control.

Qusay Hassan

This nationwide study employs a spatial multi-criteria evaluation implemented in ArcGIS Model Builder to balance renewable-energy potential, ecosystem-service supply and development impacts. The analysis covers the entire of Iraq at 30 m resolution. The analysis of data identified suitable areas for renewable energy projects, including solar farms and biomass production, taking into account environmental, socio-economic, and land-use factors on the basis of four levels. The results give a more detailed land suitability classification for solar farms, using a four-class scheme according to the possible impact on ecosystem services. The results show that 49 % of the analyzed areas are suitable for both biomass and solar farms, while 6 % are suitable exclusively for solar energy, and 45 % are not suitable for either. Under the ecosystem services trade-off analysis, 82 % of the land has good suitability for balancing agricultural biomass production with water services; 10 % has intermediate suitability, and 8 % is not suitable. In the case of solar energy, the analysis found that 14 % of the land is highly suitable, 70 % moderately suitable, 11 % with lower suitability, and 5 % least suitable. The analyses also provide further details on the effect of policy and regulatory frameworks. For agricultural biomass 44 % of the areas have no constraints, 50 % are affected by policy constraints without substantial ecosystem services trade-offs, and 3 % by both constraints and trade-offs. For solar farms, 40 % of the areas are free of constraints, 14 % are affected by ecosystem services trade-offs, 42 % by policy constraints without substantial ecosystem services impacts and 7 % by both constraints and trade-offs.

Xinyuan Wu, Vasilis Friderikos

Unmanned Aerial Vehicles (UAVs) integrated with Intelligent Reflecting Surfaces (IRS) offer promising solutions for enhancing radio access networks in dense urban microcell environments. Yet, UAV-mounted IRS (U-IRS) designs suffer from limited availability due to high energy consumption for continuous hovering, whilst fixed terrestrial IRS deployments provide energy efficiency at the cost of flexibility. Recognizing that the key challenge is to balance the mobility of UAV-mounted IRS with the energy efficiency of terrestrial IRS, we propose a novel Robotic Aerial Intelligent Reflecting Surface (RA-IRS) that employs a mechanical anchoring mechanism to secure the IRS without the need for sustained hovering, thereby significantly reducing energy consumption while retaining the mobility required to serve dynamic hotspot areas. A multi-epoch optimization framework is developed to jointly determine RA-IRS deployment, visiting order, hotspot selection, and active/passive beamforming with the goal of maximizing served traffic demand within an urban microcell. Statistical channel information initially guides RA-IRS anchoring and trajectory design via two sequential linear programs, followed by an alternating-optimization scheme that refines hotspot selection and active/passive beamforming under instantaneous channel conditions. Numerical evaluations demonstrate that with several RA-IRS devices being deployed in the microcell, the served traffic reaching 1.3–1.45 times the baseline traffic observed without deployment, depending on the heterogeneity of the scenario. Compared to UAV-mounted IRS systems, RA-IRS achieve up to 96% energy savings, while under stringent QoS constraints—where terrestrial IRS yield only marginal improvements—RA-IRS deliver gains that are two to three times higher. Together, these benefits effectively balance the trade-offs between mobility and energy efficiency in dense urban networks.

Marcelo Lattarulo Campos

Abstract One of the most fascinating aspects of the plant immune system is the growth versus defense antagonism. This phenomenon describes a physiological condition where the activation of defense mechanisms suppresses growth, and vice-versa. This trade-off has profound implications in natural and agronomical ecosystems, making it a critical focus of research in plant biology. In this viewpoint, I offer a historical perspective our understanding of the growth versus defense antagonism in plants, highlighting a significant paradigm shift in the field. Traditionally, this negative correlation was attributed to limited resources, which plants must allocate either to growth or to defense. However, recent discoveries of the genetic components governing the balance between plant growth and defense revealed that this tradeoff is a strategic adaptation for fitness optimization in varying environments. I also share personal insights into the current challenges and emerging opportunities in this research area. By exploring the past, present, and future of growth versus defense antagonism, this article aims to contribute to the development of innovative strategies that enhance plant resilience and productivity. Such advances are critical for transforming agriculture in the face of increasing pest and pathogen pressures and the mounting challenges of climate change.

Sirinda Palahan

Overseas investment and trade can be daunting for beginners due to the vast amount of complex information. This paper presents a chatbot system that integrates natural language processing and information retrieval techniques to simplify the document retrieval process. The proposed system identifies the most relevant content, enabling users to navigate the intricate landscape of foreign trade and investment more efficiently. Our methodology combines the BM25 model and a deep learning model to rank and retrieve documents, aiming to reduce noise in the document content and enhance the accuracy of the results. Experiments with Thai natural language queries have demonstrated the effectiveness of our system in retrieving pertinent documents. A user satisfaction survey further validated the system's effectiveness. Most respondents found the system helpful and agreed with the suggested documents, indicating its potential as a valuable tool for Thai entrepreneurs navigating foreign trade and investment.