Hasil untuk "Balance of trade"

Esther Mengelkamp, Benedikt Notheisen, Carolin Beer et al.

J. Uotila, Markku V. J. Maula, T. Keil et al.

M. Todaro

P. Krugman, M. Obstfeld

Yingyi Qian, Yingyi Qian, Gérard Roland et al.

Nancy C. Johnson

A. P. Miettinen, J. Nurminen

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

A. Dixit, V. Norman

Rieke Hansen, S. Pauleit

Green infrastructure (GI) and ecosystem services (ES) are promoted as concepts that have potential to improve environmental planning in urban areas based on a more holistic understanding of the complex interrelations and dynamics of social–ecological systems. However, the scientific discourses around both concepts still lack application-oriented frameworks that consider such a holistic perspective and are suitable to mainstream GI and ES in planning practice. This literature review explores how multifunctionality as one important principle of GI planning can be operationalized by approaches developed and tested in ES research. Specifically, approaches developed in ES research can help to assess the integrity of GI networks, balance ES supply and demand, and consider trade-offs. A conceptual framework for the assessment of multifunctionality from a social–ecological perspective is proposed that can inform the design of planning processes and support stronger exchange between GI and ES research.

Rodrigo Nogueira, Wei Yang, Kyunghyun Cho et al.

The advent of deep neural networks pre-trained via language modeling tasks has spurred a number of successful applications in natural language processing. This work explores one such popular model, BERT, in the context of document ranking. We propose two variants, called monoBERT and duoBERT, that formulate the ranking problem as pointwise and pairwise classification, respectively. These two models are arranged in a multi-stage ranking architecture to form an end-to-end search system. One major advantage of this design is the ability to trade off quality against latency by controlling the admission of candidates into each pipeline stage, and by doing so, we are able to find operating points that offer a good balance between these two competing metrics. On two large-scale datasets, MS MARCO and TREC CAR, experiments show that our model produces results that are either at or comparable to the state of the art. Ablation studies show the contributions of each component and characterize the latency/quality tradeoff space.

Mark J. Flannery, A. Protopapadakis

Tiago Almeida, Isaias Felzmann, Lucas Wanner

Approximate Computing (AxC) enhances energy and resource efficiency by intentionally relaxing accuracy requirements, offering an attractive trade-off for FPGA-based hardware accelerators. In Approximate High-Level Synthesis (AHLS), effective Design Space Exploration (DSE) is crucial for identifying Pareto-optimal solutions that balance accuracy and hardware cost. However, existing DSE approaches rely on either model-based analytical estimations or model-free evaluations, each presenting trade-offs in speed, fidelity, and scalability. To better understand these differences, this work introduces and compares two complementary DSE strategies for AHLS: 1) a DFG-based approach that analytically estimates error propagation, and 2) an Input-Aware Heuristic (IAH) that evaluates candidate approximations using actual application behavior. Both approaches leverage a pre-characterized library of approximate arithmetic components and perform post-HLS evaluation to assess FPGA resource utilization and accuracy. Experimental results across image processing applications demonstrate that the IAH identifies broader and more accurate Pareto fronts, achieving up to 12 percentage points of additional savings in LUT and FF usage compared to static DFG-based exploration while maintaining accuracy within defined thresholds. These findings show that model-free guidance significantly improves AHLS exploration.

Atika Sunarto, Muhammad Ali Adnan, Azizan Khair

Cryptocurrency is rapidly developing as a new digital investment instrument in Indonesia, yet the reliability of the Indonesian civil law framework in protecting consumers remains uncertain. This study aims to investigate: to what extent does Indonesian civil law provide adequate legal protection to consumers engaged in cryptocurrency investment activities? This issue is urgent due to high price volatility, market manipulation risks, and weak legal certainty for investors. Previous studies, such as Rere (2024), focused on criminal aspects of digital asset fraud, while Zain (2025) examined trade regulations under Bappebti’s supervision. However, neither fully explores the civil law protection for consumers as investors. This research assesses the effectiveness of civil law in ensuring legal safeguards for cryptocurrency investors. The method employed is a normative juridical approach, utilising a literature study as the primary method. The findings reveal that existing regulations, particularly Bappebti Regulation No. 5 of 2019, are still limited to technical trading aspects and business actor oversight, lacking substantive provisions on civil liability for consumer losses. This study contributes to legal scholarship by advocating for regulatory reform that strikes a balance between digital innovation and consumer protection. For international readers, the article provides insights into how developing countries address legal challenges related to cryptocurrency investments and stimulates comparative legal discourse on digital consumer protection across jurisdictions.

Yu Li, Ruifan Li, Hongbing Yu et al.

Urbanization in coastal megacities leads to trade-offs between development and ecology, degrading carbon sinks and disrupting spatial-functional balance. Current models often fail to integrate land use, carbon dynamics, and ecological zoning. This study created a system that brings together Patch-generating Land Use Simulation (PLUS), Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Production-Living-Ecological Space (PLES) to examine how urban growth affects carbon storage and ecological balance in Shenzhen, China. Using GlobeLand30 datasets (2000–2020), land use was simulated under ecological priority scenarios until 2030. Findings revealed that a 50.15% expansion of built-up areas caused a 7.9% decline in carbon stock, with coastal areas hit hardest. Production-Living-Ecological Space coordination improved slightly, but urban areas showed imbalances. A zoning scheme identified 40% built-up land as the threshold for carbon collapse, and the 2010 ecological control line policy reduced carbon loss by 41%. This study offers a replicable model for balancing urban development with ecological resilience.

Lisa Biber-Freudenberger, Christina Bogner, Georg Bareth et al.

Summary: This perspective explores the multifaceted development challenges related to road network expansion in sub-Saharan Africa, where recent infrastructure investments reflect transformative ambitions but also imply socio-ecological tradeoffs. Roads can boost economic growth by facilitating trade, tourism, and access to essential services, yet they simultaneously contribute to ecosystem fragmentation, biodiversity loss, and human-wildlife conflicts. Looking at the history of Africa’s road development, we find that mega-projects—often funded by international donors—reshape political and economic landscapes while altering rural livelihoods and ecosystems. We synthesize literature and case studies to reveal critical trends and propose solutions, urging for a shift toward sustainable, evidence-based infrastructure strategies that balance development with environmental stewardship. We further advocate for transdisciplinary approaches and community engagement to align road expansion with long-term stakeholder needs so as to minimize adverse impacts on Africa’s socio-ecological systems.

Seyed Reza Haddadi, Masoumeh Hashemi, Richard C. Peralta et al.

Previous researches have proved that the synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable the rapid and cost-effective identification of sugar beet stresses caused by water and/or nitrogen deficiencies. RGB images representing stressed and non-stressed crops were used in the analysis. To improve robustness, data augmentation was applied, generating six variations on each image and expanding the dataset from 150 to 900 images for training and testing. Each MLIM was trained and tested using 54 combinations derived from nine canopy and RGB-based input features and six ML algorithms. The most accurate MLIM used RGB bands as inputs to a Multilayer Perceptron, achieving 96.67% accuracy for overall stress detection, and 95.93% and 94.44% for water and nitrogen stress identification, respectively. A Random Forest model, using only the green band, achieved 92.22% accuracy for stress detection while requiring only one-fourth the computation time. For specific stresses, a Random Forest (RF) model using a Scale-Invariant Feature Transform descriptor (SIFT) achieved 93.33% for water stress, while RF with RGB bands and canopy cover reached 85.56% for nitrogen stress. To address the trade-off between accuracy and computational cost, a bargaining theory-based framework was applied. This approach identified optimal MLIMs that balance performance and execution efficiency.

Mihalis Maliakas, Dimitra-Dionysia Stergiopoulou

Trades are important objects in combinatorial design theory that may be realized as certain elements of kernels of inclusion matrices. Total trades were introduced recently by Ghorbani, Kamali and Khosravshahi, who showed that over a field of characteristic zero the vector space of trades decomposes into a direct sum of spaces of total trades. In this paper, we show that the vector space spanned by the permutations of a total trade is an irreducible representation of the symmetric group. As a corollary, the previous decomposition theorem is recovered. Also, a basis is obtained for the module of total trades in the spirit of Specht polynomials. More generally, in the second part of the paper we consider intersection matrices and determine the irreducible decompositions of their images. This generalizes previously known results concerning ranks of special cases.

Durgesh Nandini, Rebekka Koch, Mirco Schoenfeld

This study investigates the effectiveness of Large Language Models (LLMs) for the extraction of structured knowledge in the form of Subject-Predicate-Object triples. We apply the setup for the domain of Economics application. The findings can be applied to a wide range of scenarios, including the creation of economic trade knowledge graphs from natural language legal trade agreement texts. As a use case, we apply the model to regional trade agreement texts to extract trade-related information triples. In particular, we explore the zero-shot, one-shot and few-shot prompting techniques, incorporating positive and negative examples, and evaluate their performance based on quantitative and qualitative metrics. Specifically, we used Llama 3.1 model to process the unstructured regional trade agreement texts and extract triples. We discuss key insights, challenges, and potential future directions, emphasizing the significance of language models in economic applications.

Дхиани Мехта , Никундж Патель

The growing economic prominence of BRICS nations (Brazil, Russia, India, China, and South Africa) has attracted considerable attention to the macroeconomic dynamics driving their development. As these economies grow rapidly and become more integrated into global markets, it becomes increasingly difficult to balance economic growth, trade liberalization, and sustainable fiscal policies. Government size, a key factor in fiscal management, tends to increase with national income (as suggested by Wagner’s Law) and in response to trade openness (as outlined by the Compensation Hypothesis). Understanding these dynamics is crucial due to the unique fiscal pressures and global competitiveness faced by BRICS countries. This study investigates the validity of Wagner’s law and the Compensation Hypothesis in the context of BRICS. Using a panel nonlinear autoregressive distributed lag model on annual panel data from 1999 to 2023, our findings confirm Wagner’s law, showing a positive relationship between economic growth and government size. Additionally, the results support the Compensation Hypothesis, indicating that trade openness enhances government size. This study underscores the potential trade-offs between promoting economic growth and trade liberalization, as these strategies may inadvertently expand the government sector and affect fiscal stability. As BRICS economies continue to integrate into global markets, this research contributes to the discussion on Wagner’s law and trade openness, offering new insights into sustainable fiscal policies, government expenditure optimization, and the pursuit of global competitiveness and economic growth within the BRICS framework.