Hasil untuk "Balance of trade"

Tuong Linh Nha, Duc Anh Nguyen, Phan Anh Trinh et al.

This study investigates rim seal flow in axial turbine configurations through a combined experimental–numerical approach, with the objective of identifying sealing-flow conditions that minimize ingestion while limiting aerodynamic losses. Experimental measurements from the University of BATH are used to validate computational methodology, ensuring consistency with established sealing-effectiveness trends. The work places particular emphasis on the influence of computational domain selection and interface treatment, which is shown to strongly affect the prediction of ingestion mechanisms. A key contribution of this study is the systematic assessment of multiple domain configurations, demonstrating that a frozen rotor MRF formulation provides the most reliable steady-state representation of pressure-driven ingress, whereas stationary and non-interface domains tend to overpredict sealing effectiveness. A simplified thin-seal model is also evaluated and found to offer an efficient alternative for global performance predictions. Furthermore, a statistical orifice-based model is introduced to estimate minimum sealing flow for different rim seal geometries, providing a practical engineering tool for purge-flow scaling. The effects of pre-swirl injection are examined and shown to substantially reduce rotor wall shear and moment coefficient, contributing to lower windage losses without significantly modifying sealing characteristics. Unsteady flow features are explored using a harmonic balance method, revealing Kelvin–Helmholtz-type instabilities that drive large-scale structures within the rim seal cavity, particularly near design-speed operation. Finally, results highlight a clear trade-off between sealing-flow rate and turbine isentropic efficiency, underlining the importance of optimized purge-flow management.

Romain Cosson, Federico Fusco, Anupam Gupta et al.

We study repeated bilateral trade when the valuations of the sellers and the buyers are contextual. More precisely, the agents' valuations are given by the inner product of a context vector with two unknown $d$-dimensional vectors -- one for the buyers and one for the sellers. At each time step $t$, the learner receives a context and posts two prices, one for the seller and one for the buyer, and the trade happens if both agents accept their price. We study two objectives for this problem, gain from trade and profit, proving no-regret with respect to a surprisingly strong benchmark: the best omniscient dynamic strategy. In the natural scenario where the learner observes \emph{separately} whether the agents accept their price -- the so-called \emph{two-bit} feedback -- we design algorithms that achieve $O(d\log d)$ regret for gain from trade, and $O(d \log\log T + d\log d)$ regret for profit maximization. Both results are tight, up to the $\log(d)$ factor, and implement per-step budget balance, meaning that the learner never incurs negative profit. In the less informative \emph{one-bit} feedback model, the learner only observes whether a trade happens or not. For this scenario, we show that the tight two-bit regret regimes are still attainable, at the cost of allowing the learner to possibly incur a small negative profit of order $O(d\log d)$, which is notably independent of the time horizon. As a final set of results, we investigate the combination of one-bit feedback and per-step budget balance. There, we design an algorithm for gain from trade that suffers regret independent of the time horizon, but \emph{exponential} in the dimension $d$. For profit maximization, we maintain this exponential dependence on the dimension, which gets multiplied by a $\log T$ factor.

P A Robinson, Alexander McInnes, Najmeh Sajedianfard et al.

A model of time-dependent trade of goods between spatial locations is formulated via an electric circuit analogy, in which goods are analogous to charge and price to voltage, while producers and consumers are represented by sources and sinks of goods flow, which is represented by current, located at the nodes of a trade network. The core ansatz is that the flow of goods along each network link is driven by the voltage difference across that link, opposed by resistance that represents trade friction. Market prices are then determined indirectly by internal balances of flows, subject to external constraints on supply and demand. The model yields multiple outcomes that support its validity and applicability, including price setting via emergent balance of supply and demand, price fluctuations, traditional and generalized elasticities, network structure-flow relations, competition between producers, and substitution between suppliers, between consumers, and/or between trade links. All these results prove to be consistent with observed features of trade dynamics, thereby supporting the validity of the model. The new model is then used to derive the widely used gravity model of international trade from a mechanistic basis, yielding exponents consistent with published data and leading naturally to core-periphery structure, as observed in real trade networks. The analysis also implies that trade flows self-organize to minimize trade friction in the system as a whole, an emergent global outcome from the purely local dynamics of the populations of producers, consumers, and traders. Possible generalizations and further applications are outlined, including incorporation of asymmetry and capacity limits of trade links, constraints on supply and demand, behavioral responses, and coupling to models of investment strategies.

Hu Zhengxi

China is currently in an important period of strategic opportunities, experiencing rapid economic development, gradual social transformation, and increasingly prominent environmental issues.Achieving sustainable development of the economy, society, and environment is currently the most important task. At the same time, as the world’s largest net exporter of carbon emissions, the huge trade carbon deficit has also had serious negative impacts on the country. In a diverse and complex context, China faces dual pressures of carbon emission reduction and economic development. This article analyzes and decomposes key indicators of carbon emissions in a certain province through logistic regression analysis, which helps to better balance the relationship between local production and consumption sides, total carbon emissions and emission intensity, emission reduction and economic growth, thereby achieving more comprehensive environmental, economic, and social benefits.

Abdallah Rabee, Zakaria Anwar, Ahmed AbdelMoety et al.

Abstract Automated foul detection in football represents a challenging task due to the dynamic nature of the game, the variability in player movements, and the ambiguity in differentiating fouls from regular physical contact. This study presents a comprehensive comparative evaluation of eight state-of-the-art Deep Learning (DL) architectures — EfficientNetV2, ResNet50, VGG16, Xception, InceptionV3, MobileNetV2, InceptionResNetV2, and DenseNet121 — applied to the task of automated foul detection in football. The models were trained and evaluated using a curated dataset comprising 7000 images, which was split into 70% for training (4,900 images), 20% for validation (1,400 images), and 10% for testing (700 images). To ensure fair evaluation, the test set was balanced to contain 350 images depicting foul events and 350 images representing non-foul scenarios, although perfect balance was subject to class distribution constraints. Performance was assessed across multiple metrics, including test accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC). The results demonstrate that InceptionResNetV2 achieved the highest test accuracy of 87.57% and a strong F1-score of 0.8966, closely followed by DenseNet121, which attained the highest precision of 0.9786 and an AUC of 0.9641, indicating superior discriminatory power. Lightweight models such as MobileNetV2 also performed competitively, highlighting their potential for real-time deployment. The findings highlight the strengths and trade-offs between model complexity, accuracy, and generalizability, underscoring the viability of integrating DL architectures into existing football officiating systems, such as the Video Assistant Referee (VAR). Furthermore, the study emphasizes the importance of model explainability through techniques such as Gradient-weighted Class Activation Mapping++ (GradCAM++), ensuring that automated decisions can be accompanied by interpretable visual evidence. This comparative evaluation serves as a foundation for future research aimed at enhancing real-time foul detection through multimodal data fusion, temporal modeling, and improved domain adaptation techniques.

Umm E. Salma, Muhammad Abdul Haq, Syed Arsalan Ali et al.

The present study proposes a composite packaging system comprising two pouches: an outer layer made of moisture-resistant biaxially oriented polypropylene (BOPP) film and an inner layer using oxygen-barrier biodegradable polymer films, such as starch or polyvinyl alcohol (PVA). The biodegradable inner pouch enhances sustainability by reducing plastic waste, while the single-polymer composition of the outer pouch facilitates recyclability. The formulation of the inner biodegradable pouches was refined using starch/PVA, with glycerol as a plasticizer and tannic acid (TA) as a crosslinking agent. Results revealed that glycerol and TA concentrations significantly affected the film properties, and optimal ranges were identified to balance flexibility and barrier performance. Among the two biodegradable options, PVA films demonstrated superior packaging characteristics. A pouch-in-pouch system was developed, characterized, and tested for preserving red chili powder and deshelled peanuts stored under daylight at 40°C for seven weeks. Of the ten packaging configurations evaluated, the PVA-TA/BOPP combination showed exceptional preservation performance, with the lowest oxygen transmission rate and the ability to maintain 95% of chili powder pungency and American Spice Trade Association (ASTA) color value. Similarly, favorable moisture content and peroxide values were observed in deshelled peanuts. This research highlights the potential of biodegradable packaging systems, optimized through material selection and additive incorporation, to enhance food preservation in packaging applications.

Ali Haider, Ghulam Muhammad, Talha Ahmed Khan et al.

Abstract Camouflaged object detection particularly is considered as challenging and crucial because these objects are designed to either mimic their environment or be completely hidden within it. The goal of camouflage patterns utilization is to help objects blend into their surroundings, making them harder to detect. One of the biggest hurdles is distinguishing the object from the background. Many efforts have been made to tackle this problem all around the globe, and this research builds on those advancements. The focus is on developing methods for detecting camouflaged targets in military settings, including materials, operations, and personnel using convolutional neural network. A key contribution of this work is the MSC1K dataset, which includes 1,000 images of camouflaged people with detailed annotations for object-level and bounding-box segmentation. This dataset can also support broader computer vision tasks like detection, classification, and segmentation. Additionally, this research introduces the Dynamic Feature Aggregation Network (DFAN), a method inspired by previous studies that uses multi-level feature fusion to detect camouflaged soldiers in various conditions. Extensive testing shows that DFAN and SINet-V2 (Search and identification network) achieved the highest accuracy with the least error, while SINet struggles the most. Notably, DFAN shines with its precision-recall balance, while SINET lags behind, potentially due to difficulties in handling intricate saliency patterns. The most intriguing contrast arises in the third setting (MSC1K + CPD), where DFAN remarkably excels, displaying superior structural similarity, strong human-perception alignment, and optimal precision-recall trade-offs. DFAN emerges as the top performer in terms of error minimization, achieving the lowest MAE values: 0.051 for MSC1K, 0.004 for CPD, and 0.028 for the combined dataset. In contrast, SINet shows the highest error rates, making it the Least reliable model, with MAE values of 0.079, 0.157, and 0.049 respectively. ZoomNet and SINetV2 delivered moderate performance; ZoomNet records MAEs of 0.056, 0.005, and 0.029, whereas SINetV2 reports 0.051, 0.005, and 0.027 in the same settings. These results indicated that DFAN and SINetV2 consistently produced more accurate predictions, while SINet has less precision. Overall, the comparative assessment sheds light on how each model adapts to varying datasets, revealing key insights into their performance robustness.

Daniel Young, Olivier Francon, Elliot Meyerson et al.

How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance and, therefore, climate change. Based on available historical data on land-use changes and a simulation of the associated carbon emissions and removals, a surrogate model can be learned that makes it possible to evaluate the different options available to decision-makers efficiently. An evolutionary search process can then be used to discover effective land-use policies for specific locations. Such a system was built on the Project Resilience platform and evaluated with the Land-Use Harmonization dataset LUH2 and the bookkeeping model BLUE. It generates Pareto fronts that trade off carbon impact and amount of land-use change customized to different locations, thus providing a proof-of-concept tool that is potentially useful for land-use planning.

Abbas Fadhil Khalaf, Farhan Lafta Rashid, Mudhar A. Al-Obaidi et al.

Abstract Phase change materials (PCMs) suffer from slow melting rates due to their low thermal conductivity, limiting their efficiency in thermal energy storage systems. This study numerically investigates the novel use of copper rods as conductive enhancers to accelerate PCM melting in a horizontally placed hemispherical cell. Using the ANSYS/FLUENT 16 with an enthalpy-porosity model, the impact of rod integration is examined to determine the optimal rod configuration for maximising heat transfer while minimising melting time. The results indicate that copper rods dramatically improved melting performance: a 20 mm rod can reduce total melting time by 70% (from 300 to 90 min), while 10 mm and 15 mm rods achieve reductions of 40% (to 180 min) and 50% (to 150 min), respectively. Clearly, the 20 mm rod enables 70% liquid fraction in 30 min, showing a melting speed four times faster than the no-rod case. Nonlinear scaling reveals diminishing returns beyond 15 mm, suggesting a cost-performance trade-off at this length. The 15 mm rod emerged as a practical balance between attaining 85% of maximum gain with a 50% reduction in melting time while utilising 25% less copper than 20 mm rod. Accordingly, this research provides critical insights for designing high-efficiency thermal storage systems, offering a roadmap to optimise conductive enhancements for real-world applications. By bridging the gap between material properties and system-level performance, the findings advance the deployment of PCMs in renewable energy and waste heat recovery systems.

Alviarez Vanessa, Fioretti Michele, Kikkawa Ken et al.

This paper derives a closed-form expression linking aggregate markups on imported inputs to concentration in a model of firm-to-firm trade with two-sided market power. Our theory extends standard oligopoly insights in two dimensions. First, it reveals that markups increase with exporter concentration and decrease with importer concentration, reflecting the balance of oligopoly and oligopsony forces. Second, it adapts conventional market definitions to reflect rigid trading relationships, yielding new concentration measures that capture competition in firm-to-firm trade. Analysis of Colombian transaction-level import data shows these differences are key to understanding markup dynamics in international trade.

Dongliang Guo, Mengxuan Hu, Zihan Guan et al.

Large multi-modal models inevitably decay over time as facts update and previously learned information becomes outdated. Traditional approaches such as fine-tuning are often impractical for updating these models due to their size and complexity. Instead, direct knowledge editing within the models presents a more viable solution. Current model editing techniques, however, typically overlook the unique influence ranges of different facts, leading to compromised model performance in terms of both generality and locality. To address this issue, we introduce the concept of the generality-locality trade-off in multi-modal model editing. We develop a new model editing dataset named OKEDIT, specifically designed to effectively evaluate this trade-off. Building on this foundation, we propose \textbf{BalancEdit}, a novel method for balanced model editing that dynamically achieves an optimal balance between generality and locality. BalancEdit utilizes a unique mechanism that generates both positive and negative samples for each fact to accurately determine its influence scope and incorporates these insights into the model's latent space using a discrete, localized codebook of edits, without modifying the underlying model weights. To our knowledge, this is the first approach explicitly addressing the generality-locality trade-off in multi-modal model editing. Our comprehensive results confirm the effectiveness of BalancEdit, demonstrating minimal trade-offs while maintaining robust editing capabilities. Our code and dataset are available at https://github.com/donglgcn/BalancEdit/tree/MMOKVQA.

Borin Keo, Bin Li, Waqas Younis

High trade costs pose substantial barriers to the process of trade liberalization. This study aims to measure trade costs and explore the driving forces behind the growth of bilateral trade between Cambodia and its top 30 trading partners from 1993 to 2019. Using a micro-founded measure of trade costs derived from the gravity model, we find that Cambodia's average trade costs decreased by 35.43 percent between 1993 and 2019. Fluctuations in average trade costs persisted until 2014, despite Cambodia's accession to the World Trade Organization (WTO) in 2004. Since then, these costs have declined more rapidly. Cambodia's bilateral trade costs are lower with its major trading partners in Southeast Asia and East Asia than with those in South Asia, Oceania, Europe, and North America. Cambodia's average trade costs with developing and emerging economies are lower than those with developed economies. Between 2014 and 2019, Cambodia experienced a notable decline in average trade costs with trading partners along the Belt and Road Initiative (BRI) corridors by 34.78 percent, twice as fast as with non-BRI trading partners. Regarding the decomposition of trade growth, we find that the expansion of Cambodian trade over the period from 1993 to 2019 was driven by three factors: the rise in income (59.65 percent), the decline in trade costs (56.69 percent), and the decline in multilateral resistance (minus 16.34 percent). The findings of this study have significant implications for a better understanding of Cambodia's development process toward global trade integration over the past two decades. Our results suggest that Cambodia can optimize its trade expansion potential by focusing on its relations with trading partners exhibiting high economic growth potential and those showing substantial reductions in trade costs.

Samuel Hardwick

Trade agreements are often understood as shielding commerce from fluctuations in political relations. This paper provides evidence that World Trade Organization membership reduces the penalty of political distance on trade at the extensive margin. Using a structural gravity framework covering 1948 to 2023 and two measures of political distance, based on high-frequency events data and UN General Assembly votes, GATT/WTO status is consistently associated with a wider range of products traded between politically distant partners. The association is strongest in the early WTO years (1995 to 2008). Events-based estimates also suggest attenuation at the intensive margin, while UN vote-based estimates do not. Across all specifications, GATT/WTO membership increases aggregate trade volumes. The results indicate that a function of the multilateral trading system has been to foster new trade links across political divides, while raising trade volumes among both close and distant partners.

Alex J. Hoogerbrugge, Christoph Strauch, Sanne Böing et al.

Humans maintain an intricate balance between storing information in visual working memory (VWM) and just-in-time sampling of the external world, rooted in a trade-off between the cost of maintaining items in VWM versus retrieving information as it is needed. Previous studies have consistently shown that one prerequisite of just-in-time sampling is a high degree of availability of external information, and that introducing a delay before being able to access information led participants to rely less on the external world and more on VWM. However, these studies manipulated availability in such a manner that the cost of sampling was stable and predictable. It is yet unclear whether participants become less reliant on external information when it is more difficult to factor in the cost of sampling that information. In two experiments, participants copied an example layout from the left to the right side of the screen. In Experiment 1, intermittent occlusion of the example layout led participants to attempt to encode more items per inspection than when the layout was constantly available, but this did not consistently result in more correct placements. However, these findings could potentially be explained by inherent differences in how long the example layout could be viewed. Therefore in Experiment 2, the example layout only became available after a gaze-contingent delay, which could be constant or variable. Here, the introduction of any delay led to increased VWM load compared to no delay, although the degree of variability in the delay did not alter behaviour. These results reaffirm that the nature of when we engage VWM is dynamical, and suggest that any disruption to the continuous availability of external information is the main driver of increased VWM usage relative to whether availability is predictable or not.

Zijie Pan, Stepan Gordeev, Jiahui Zhao et al.

This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global trade landscape. Acknowledging the limitations of traditional models like the Gravity Model of Trade, this study introduces a two-stage approach combining explainable machine learning and factorization models. The first stage employs SHAP Explainer for effective variable selection, identifying key provisions in PTAs, while the second stage utilizes Factorization Machine models to analyze the pairwise interaction effects of these provisions on trade flows. By analyzing comprehensive datasets, the paper demonstrates the efficacy of this approach. The findings not only enhance the predictive accuracy of trade flow models but also offer deeper insights into the complex dynamics of international trade, influenced by specific bilateral trade provisions.

E. Mesagan, O. Alimi, X. Vo

J. Odionye, Augustine C. Odo, Marius Ikpe et al.

ABSTRACT This study sought to ascertain relatively the asymmetric reactions of trade balances to currency devaluation and non-devaluation regimes in sub-Saharan African (SSA) countries between 1981 and 2021 using the smooth transition regression (STR) model. The outcome indicates that, in Ghana, Malawi, and Mozambique, currency devaluation as a change in policy has a major influence on the trade balance; however, in Nigeria, Kenya, and Tanzania, this impact is negligible. Nigeria had the highest gamma coefficient but insignificant, suggesting that policy change has not significantly impacted the country’s trade balance despite the high transition rate. Findings from the devaluation regime revealed that, with the exception of Ghana, all other nations’ real exchange rates are inversely and significantly related to the trade balance. Additionally, it displayed an average threshold parameter of 0.147, indicating that a devaluation of more than 14.7% within a year will deteriorate the trade balance in SSA. The results indicate that the devaluation effects hinge on the structure, macroprudential policies, and infrastructural growth of the nation. The study recommended amongst other things, (i) a robust structural transformation in key sectors (ii) judicious investment in infrastructural development to address the key bottleneck in the quality and quantity of domestic production.

Shahida Suleman, Hassanudin Mohd Thas Thaker, M. Ariff

PurposeThe purpose of this research is to systematically scrutinize the influence of macroeconomic determinants on trade openness, through the lens of various trade theories, with a particular focus on the economies of the GIPSI countries – Greece, Ireland, Portugal, Spain and Italy.Design/methodology/approachThis study investigates the macroeconomic factors influencing trade openness in the GIPSI economies from 1995 to 2020. Methods include stepwise regression (SR) for model selection, Pedroni panel cointegration test and panel regression results. The analysis uses advanced panel regressions, including FMOLS, Panel OLS and FEM. The long-term dynamics were tested using Pedroni cointegration, while Granger causality testing was used to examine the causal direction between the trade openness ratio and trade determinant.FindingsThe results show both long-term and short-term relationships between trade openness and (1) foreign direct investment, (2) labor force participation rate, (3) trade reserves and (4) trade balance. The researchers also detected unidirectional and bidirectional causality relationships between trade openness and these four factors. The study also revealed that trade reserves (TR) emerge as the most influential determinant of trade openness, and per capita income does not exhibit economic significance concerning the trade openness of GIPSI economies.Research limitations/implicationsThis research is conducted within the context of the GIPSI nations (Greece, Ireland, Portugal, Spain and Italy). As such, the outcomes may not be universally applicable to other economic systems due to the distinct institutional settings and governance structures across different economic groups. Future investigations may explore the relationship between trade openness and its determinants by incorporating different variables.Originality/valueTo the best of the authors' knowledge, this is the first study investigating the theory that suggested trade drivers drive the trade openness of GIPSI countries context. By focusing on GIPSI countries, the study offers a unique perspective on the dynamics of trade openness in economies that have experienced financial crises and stringent austerity measures.

Shiji Zhao, Xizhe Wang, Xingxing Wei

Adversarial Training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performance towards clean examples is negatively affected after Adversarial Training, which means a trade-off exists between accuracy and robustness. Recently, some studies have tried to use knowledge distillation methods in Adversarial Training, achieving competitive performance in improving the robustness but the accuracy for clean samples is still limited. In this paper, to mitigate the accuracy-robustness trade-off, we introduce the Balanced Multi-Teacher Adversarial Robustness Distillation (B-MTARD) to guide the model's Adversarial Training process by applying a strong clean teacher and a strong robust teacher to handle the clean examples and adversarial examples, respectively. During the optimization process, to ensure that different teachers show similar knowledge scales, we design the Entropy-Based Balance algorithm to adjust the teacher's temperature and keep the teachers' information entropy consistent. Besides, to ensure that the student has a relatively consistent learning speed from multiple teachers, we propose the Normalization Loss Balance algorithm to adjust the learning weights of different types of knowledge. A series of experiments conducted on three public datasets demonstrate that B-MTARD outperforms the state-of-the-art methods against various adversarial attacks.

R. A. Guzmán, M. Barbato, Daniel Sznycer et al.

Significance Intuitions about right and wrong clash in moral dilemmas. We report evidence that dilemmas activate a moral trade-off system: a cognitive system that is well designed for making trade-offs between conflicting moral values. When asked which option for resolving a dilemma is morally right, many people made compromise judgments, which strike a balance between conflicting moral values by partially satisfying both. Furthermore, their moral judgments satisfied a demanding standard of rational choice: the Generalized Axiom of Revealed Preferences. Deliberative reasoning cannot explain these results, nor can a tug-of-war between emotion and reason. The results are the signature of a cognitive system that weighs competing moral considerations and chooses the solution that maximizes rightness.