Hasil untuk "Balance of trade"

R. Mundell

Junfeng Wu, Q. Jia, K. Johansson et al.

Dongdong Chen, Ming-hui Chen, Zeng-yan Wang et al.

Objectives This retrospective study compares pregnancy outcomes in polycystic ovary syndrome (PCOS) patients across different controlled ovarian stimulation (COS) protocols—specifically GnRH antagonist and GnRH agonist cycles—combined with various frozen embryo transfer(FET) preparation methods, such as hormone replacement therapy (HRT) and ovulatory cycles. Despite the known variations in COS and FET protocols, the optimal combination for improving pregnancy outcomes in this population remains unclear.Methods We analyzed the first FET cycles of 2510 patients with PCOS at our center between January 2017 and September 2024. Baseline characteristics and pregnancy outcomes were compared using the Kruskal‒Wallis test, the chi-square (χ²) statistic, the Bonferroni correction for multiple comparisons, and inverse probability of treatment weighting (IPTW) adjustment.Results After IPTW adjustment, no significant differences were observed in live birth rates or other key reproductive outcomes among the four protocol combinations (all P > 0.05). Exploratory analyses revealed nonsignificant trends, suggesting two patterns: 1) GnRH agonist (vs. antagonist) COS protocols were associated with lower point estimates for the risks of preterm PROM and HDP; 2) ovulation (vs. HRT) cycles for FET preparation were similarly associated with lower point estimates for these risks.Conclusions For PCOS patients, live birth success is equivalent regardless of COS/FET protocol combination, supporting flexible treatment personalization. Clinical decision-making involves a critical trade-off: GnRH agonist protocols and ovulation FET cycles may be associated with a trend toward lower obstetric morbidity, potentially linked to the promotion of a more physiological ovulatory milieu. This balance between immediate iatrogenic risk and long-term pregnancy health warrants further study.

Haibo Wang, Lutfu Sua, Burak Dolar

This paper examines the relationship between trade and financial openness, as well as the operational efficiency and growth of Turkish banks, from 2010 to 2023. Utilizing CAMELG-DEA and dynamic panel data analysis, the study finds that increased trade openness significantly enhances banking efficiency, primarily due to heightened demand for banking services related to international trade. Financial openness further boosts growth by facilitating capital flows, expanding banks' credit portfolios, and increasing fee income from cross-border transactions. However, poverty levels have a negative impact on bank performance, reducing financial intermediation and innovation opportunities. The results underscore the crucial role of trade and financial openness in fostering banking sector growth in developing economies.

Cui Hu, Ben G. Li

Although Google is blocked in China, Chinese provinces export significantly more to foreign countries that recently searched for them (up to 12 months prior). This attention premium is found mainly at the extensive margin of exports, larger in products that are relatively homogeneous, substitutable, and upstream in the production process, and more pronounced during the COVID pandemic and during the holiday season. The attention premium is not found for Chinese imports from the rest of the world. Our findings attest to online attention as a scarce resource in international trade allocated by importers.

Avirup Chakraborty

The European Union Emissions Trading System (EU ETS), the worlds largest cap-and-trade carbon market, is central to EU climate policy. This study analyzes its efficiency, price behavior, and market structure from 2010 to 2020. Using an AR-GARCH framework, we find pronounced price clustering and short-term return predictability, with 60.05 percent directional accuracy and a 70.78 percent hit rate within forecast intervals. Network analysis of inter-country transactions shows a concentrated structure dominated by a few registries that control most high-value flows. Country-specific log-log regressions of price on traded quantity reveal heterogeneous and sometimes positive elasticities exceeding unity, implying that trading volumes often rise with prices. These results point to persistent inefficiencies in the EU ETS, including partial predictability, asymmetric market power, and unconventional price-volume relationships, suggesting that while the system contributes to decarbonization, its trading dynamics and price formation remain imperfect.

Faezeh Sarlakifar, Mohammadreza Mohammadzadeh Asl, Sajjad Rezvani Khaledi et al.

Traditional Long Short-Term Memory (LSTM) networks are effective for handling sequential data but have limitations such as gradient vanishing and difficulty in capturing long-term dependencies, which can impact their performance in dynamic and risky environments like stock trading. To address these limitations, this study explores the usage of the newly introduced Extended Long Short Term Memory (xLSTM) network in combination with a deep reinforcement learning (DRL) approach for automated stock trading. Our proposed method utilizes xLSTM networks in both actor and critic components, enabling effective handling of time series data and dynamic market environments. Proximal Policy Optimization (PPO), with its ability to balance exploration and exploitation, is employed to optimize the trading strategy. Experiments were conducted using financial data from major tech companies over a comprehensive timeline, demonstrating that the xLSTM-based model outperforms LSTM-based methods in key trading evaluation metrics, including cumulative return, average profitability per trade, maximum earning rate, maximum pullback, and Sharpe ratio. These findings mark the potential of xLSTM for enhancing DRL-based stock trading systems.

Ali Jami, Sajjad Abbaszade, Abdol-Hossein Vahabie

Abstract Balancing exploration and exploitation is a crucial aspect of adaptive decision-making, but psychiatric disorders can disrupt this balance in various ways, shedding light on their neurocognitive roots and guiding targeted interventions. In this systematic review, we aimed to delineate potential exploration–exploitation impairments across psychiatric disorders. Through a thorough search on PubMed, we identified forty-six relevant studies employing tasks probing exploration–exploitation balances, which we synthesized to reveal distinct patterns. These disorders are clustered into three categories: addictive patterns, emotional/cognitive disturbances, and neurological (neurodevelopmental and neurodegenerative) disorders. Our findings show that anxiety and mood disorders often enhance exploratory behaviors, while depression impact decision stability and reward sensitivity. In contrast, schizophrenia, OCD (Obsessive–Compulsive Disorder), and ADHD (Attention-Deficit/Hyperactivity Disorder) are characterized by excessive switching and difficulties in balancing exploration and exploitation, leading to impaired learning and adaptability. Additionally, disorders with addictive-like features disrupt optimal decision-making strategies by either heightening exploration or causing maladaptive persistence, thus skewing the balance away from effective decision-making. Individuals exhibiting addiction-like or compulsive behaviors often demonstrate imbalances in the explore-exploit trade-off, resulting in suboptimal decision-making characterized by reduced exploration, flawed foraging strategies, and impulsive or perseverative choices despite adverse outcomes. This suggests that such disorders may originate from dysfunctional foraging processes applied to decision-making. In sum, different patterns of exploration–exploitation balance in different disorders are crucial in understanding the difficulties in learning and decision making of neuropsychiatric disorders. This suggests that such disorders may stem from dysregulated decision-making processes, where uncertainty plays a central role. Dysfunctions in dopaminergic and noradrenergic pathways appear to disrupt the brain's representation of uncertainty, thereby altering exploratory behavior. In sum, the varying patterns of exploration–exploitation balance across different disorders are critical for understanding the challenges in learning and decision-making associated with neuropsychiatric conditions.

Björn Krautwig, Dominik Wans, Li Li et al.

Autonomous navigation is critical for unlocking the full potential of Unmanned Surface Vehicles (USVs) in complex maritime environments. Deep Reinforcement Learning (DRL) has emerged as a powerful paradigm for developing self-learning control policies, yet the design of reward functions to balance conflicting objectives, particularly fast arrival at the target position and collision avoidance, remains a major challenge. The precise, quantitative impact of reward parameterization on a USV’s maneuvering behavior and the inherent performance trade-offs have not been thoroughly investigated. Here, we demonstrate that by systematically varying reward function weights within a framework relying on the Proximal Policy Optimization (PPO), it is possible to quantitatively map the trade-off between collision avoidance safety and mission time. Our results, derived from simulations, show that agents trained with balanced reward weights achieve target-reaching success rates exceeding 98% in dynamic multi-obstacle scenarios. Conversely, configurations that disproportionately penalize obstacle proximity lead to overly cautious behavior and mission failure, with success rates dropping to 22% due to workspace boundary violations. This work provides a data-driven methodological framework for reward function design and parameter selection in safety-critical robotic applications, moving beyond ad-hoc tuning towards a more structured parameter influence analysis.

Isabella D. R. Stephens, Abigail C. Parsons, David Burnett et al.

Abstract High‐nickel lithium‐ion battery cathode materials are increasingly favored for their superior energy density but face challenges related to toxicity, cost, and critical material supply. This study assesses the current state of play in commercial cathodes, and presents a screening of the literature micro‐dopants for LiNiO2 (LNO), aiming to identify excellent electrochemical performance without compromising affordability or safety. Literature examples of tungsten, niobium, and zirconium doped cathode material all showed good performance, but are considered risky for further consideration due to the high costs and increased supply risk with these materials. A lithium excess sulfate doped material exhibited the best balance of sustainability and performance, delivering improved capacity retention and low raw material cost, with the only compromise of a very slightly elevated supply risk. The study highlights the trade‐offs between performance metrics and sustainability considerations, offering a framework for more commercially viable cathode design.

Alexander Ștefan POPA

This paper examines how the new European AI Act (Regulation (EU) 2024/1689) governs the use of copyrighted content in the training and deployment of general-purpose AI models, focusing on the pivotal Article 53. Article 53 is a key provision that imposes explicit obligations on AI model providers to ensure transparency, rigorous documentation, and adherence to copyright law. The analysis highlights the legal and ethical implications of using copyrighted works to train AI systems, noting the tension between the need for vast datasets and the rights of authors. It explores how providers are required to maintain detailed technical documentation and publicly disclose summaries of training data, implement policies to comply with copyright (including honoring rights-holders’ opt-outs), and guarantee compliance with EU copyright and related rights. These responsibilities aim to increase accountability and enable oversight while fostering trust in AI outputs. At the same time, the paper discusses how the regulation seeks to balance innovation and copyright protection, guided by principles of proportionality and fairness: general-purpose AI development is permitted but constrained to respect authors’ economic and moral rights. The broader EU legal context – including existing copyright exceptions (such as text and data mining allowances), moral rights of creators, and the three-step test – is considered to understand the boundaries of lawful AI training. Finally, the abstract addresses enforcement challenges, such as ensuring transparency without compromising trade secrets, difficulty in tracking protected content in large datasets, and cross-border compliance. The study concludes that Article 53 represents a significant step toward aligning AI innovation with European copyright norms, striking a delicate balance between fostering technological progress and safeguarding intellectual property rights.

Rayson Laroca, Valter Estevam, Gladston J. P. Moreira et al.

ABSTRACT Automatic license plate recognition (ALPR) is a frequent research topic due to its wide‐ranging practical applications. While recent studies use synthetic images to improve license plate recognition (LPR) results, there remain several limitations in these efforts. This work addresses these constraints by comprehensively exploring the integration of real and synthetic data to enhance LPR performance. We subject 16 optical character recognition (OCR) models to a benchmarking process involving 12 public datasets acquired from various regions. Several key findings emerge from our investigation. Primarily, the massive incorporation of synthetic data substantially boosts model performance in both intra‐ and cross‐dataset scenarios. We examine three distinct methodologies for generating synthetic data: template‐based generation, character permutation, and utilizing a generative adversarial network (GAN) model, each contributing significantly to performance enhancement. The combined use of these methodologies demonstrates a notable synergistic effect, leading to end‐to‐end results that surpass those reached by state‐of‐the‐art methods and established commercial systems. Our experiments also underscore the efficacy of synthetic data in mitigating challenges posed by limited training data, enabling remarkable results to be achieved even with small fractions of the original training data. Finally, we investigate the trade‐off between accuracy and speed among different models, identifying those that strike the optimal balance in each intra‐dataset and cross‐dataset settings.

Robert C. Johnson

Khadija Khatun, Chen Shen, Jun Tanimoto et al.

Understanding how cooperation emerges in public goods games is crucial for addressing societal challenges. While optional participation can establish cooperation without identifying cooperators, it relies on specific assumptions -- that individuals abstain and receive a non-negative payoff, or that non-participants cause damage to public goods -- which limits our understanding of its broader role. We generalize this mechanism by considering non-participants' payoffs and their potential direct influence on public goods, allowing us to examine how various strategic motives for non-participation affect cooperation. Using replicator dynamics, we find that cooperation thrives only when non-participants are motivated by individualistic or prosocial values, with individualistic motivations yielding optimal cooperation. These findings are robust to mutation, which slightly enlarges the region where cooperation can be maintained through cyclic dominance among strategies. Our results suggest that while optional participation can benefit cooperation, its effectiveness is limited and highlights the limitations of bottom-up schemes in supporting public goods.

Yuchen SU, Changshe MA

Recently, the concept of partially precomputed indexing for Structured query language (SQL) join (PpSj), which was predicated on structured encryption, has been introduced. This approach employed partial precomputed index join and hash filter set technology to facilitate efficient execution of join queries and Boolean queries. However, the scheme has encountered some limitations, primarily characterized by excessive information leakage during the execution of Boolean queries and an inability to support range queries. To resolve these issues, an enhanced relation database encryption scheme, termed multi-function encrypted database (MFEDB), was proposed. This scheme incorporated a hybrid filter technique derived from the PpSj scheme, integrating two filtering methods. It aimed to minimize the information leakage associated with Boolean queries, expand the subset of supported SQL queries to include equivalent queries, join queries, Boolean queries, and range queries, and balance the trade-off between the server's storage costs and the communication overhead between the client and the server.

Leonardo Dias, Brigitte Jaumard, Lackis Eleftheriadis

The increasing use of renewable energies places new challenges on the balance of the electricity system between demand and supply, due to the intermittent nature of renewable energy resources. However, through frequency regulation (FR) services, owners of battery storage systems can become an essential part of the future smart grids. We propose a thorough first study on the use of batteries associated with base stations (BSs) of a cellular network, to participate in ancillary services with respect to FR services, via an auction system. Trade-offs must be made among the number of participating BSs, the degradation of their batteries and the revenues generated by FR participation. We propose a large-scale mathematical programming model to identify the best participation periods from the perspective of a cellular network operator. The objective is to maximize profit while considering the aging of the batteries following their usage to stabilize the electrical grid. Experiments are conducted with data sets from different real data sources. They not only demonstrate the effectiveness of the optimization model in terms of the selection of BSs participating in ancillary services and providing extra revenues to cellular network operators, but also show the feasibility of ancillary services being provided to cellular network operators.

Yingfei Zhang, Xiaobing Hu, Hang Li et al.

Ripple-spreading Algorithm (RSA) is a relatively new, nature-inspired, multi-agent based method for path optimization. This paper demonstrates that by modifying the micro-level behaviors of nodes and ripples, RSA achieves good scalability for solving the <i>k</i> shortest paths problem (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>−</mo><mi>SPP</mi></mrow></semantics></math></inline-formula>). Initially, each node may generate <i>k</i> or more ripples to guarantee optimality. To improve computational efficiency for large-scale problems, we propose an approximate RSA (ARSA), where nodes generate no more than <i>h</i> ripples (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>1</mn><mo>≤</mo><mi>h</mi><mo><</mo><mi>k</mi></mrow></semantics></math></inline-formula>). While this reduces optimality, it significantly improves efficiency. Further, we introduce a fuzzy variable <i>H</i> strategy, FVHSRSA, to strike a better balance between optimality and efficiency. The optimality/efficiency of ARSA may still suffer if it uses a constant h too small/large. This strategy allows nodes closer to the destination to generate more ripples, while nodes farther away use fewer ripples. By dynamically adjusting <i>h</i>, FVHSRSA achieves a better trade-off between optimality and efficiency. Comprehensive experiments on 4 common network categories validate the effectiveness and efficiency of FVHSRSA in solving the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>−</mo><mi>SPP</mi></mrow></semantics></math></inline-formula>.

Barbary Mahmoud Magdy

This study aims to analyze the expected impacts of Saudi Arabia’s accession to the BRICS bloc, which is intergovernmental organization comprising Brazil, Russia, India, China, and South Africa. By analyzing the intra-trade flows of Saudi Arabia with the BRICS countries from the implementation of the bloc until 2023 and measuring the impact of Saudi exports and imports with BRICS on the balance of merchandise trade, and to discuss the extent of the impact of the net intra-trade situation of Saudi Arabia with BRICS on the balance of merchandise trade. The most prominent Saudi export sectors were diagnosed, and their corresponding customs tariff averages in the BRICS countries were analyzed to determine the expected effects of implementing a regional trade agreement (RTA) between the BRICS bloc. The study is based on the ARDL model and Saudi data from (2003-2022), using three models by applying on exports, imports, and trade balance of Saudi Arabia, the findings suggests that there is no significant relationship between Saudi Arabia’s trade balance on the one hand, and exports, imports and the trade balance with BRICS on the other hand, which reduces the impact of Saudi Arabia’s accession to BRICS on Saudi exports. which requires further negotiation of mutual obligations and potential gains between the member states of BRICS.

V. T. Yungblyud

: Trade and scientific-technical cooperation were integral components of U.S.-Soviet relations during the détente of the 1970s. By the late 1960s, strategic parity had been achieved between the two superpowers, marking a turning point in their confrontation. The arms race came to be regarded as both costly and unsustainable. While the temporary balance of military capabilities reduced the immediate threat of war and eased direct confrontation, it also fostered the belief that strengthening commercial ties and advancing scientifictechnical programs and exchanges could provide a material foundation for lasting peace. In Moscow, this notion evolved into a programmatic directive, whereas Washington adopted a more pragmatic stance.Drawing on a broad historiographical foundation, archival documents from Russian repositories (RGANI, RGAE), published materials from the U.S. Department of State, and electronic collections of American intelligence and scientific communities, this article examines the evolving U.S. policy toward the Soviet Union in the 1970s. It focuses on the role assigned to trade, economic relations, and scientific-technical cooperation in the broader framework of superpower relations.The study concludes that the unraveling of détente in trade and scientific-technical cooperation occurred in three distinct stages. The first stage began with the passage of the JacksonVanik Amendment in 1974, which disrupted the principle of equality in bilateral relations and inflicted considerable economic losses on the Soviet side. The second stage was marked by the Carter administration's emphasis on human rights beginning in January 1977. This introduced the threat of sanctions and escalated tensions in U.S.-Soviet relations. The third stage, initiated in December 1979 and solidified by January 1980, witnessed the dismantling of the infrastructure supporting bilateral scientific and technical cooperation. These developments underscored the shifting priorities and eventual decline of détente as a defining feature of U.S.-Soviet relations during this period.

Haiyu Wu, Kevin W. Bowyer

The issue of demographic disparities in face recognition accuracy has attracted increasing attention in recent years. Various face image datasets have been proposed as 'fair' or 'balanced' to assess the accuracy of face recognition algorithms across demographics. These datasets typically balance the number of identities and images across demographics. It is important to note that the number of identities and images in an evaluation dataset are {\em not} driving factors for 1-to-1 face matching accuracy. Moreover, balancing the number of identities and images does not ensure balance in other factors known to impact accuracy, such as head pose, brightness, and image quality. We demonstrate these issues using several recently proposed datasets. To improve the ability to perform less biased evaluations, we propose a bias-aware toolkit that facilitates creation of cross-demographic evaluation datasets balanced on factors mentioned in this paper.