Hasil untuk "Balance of trade"

Yvonne Tsiane, Bhekisisa Dlamini, Prudence Seema et al.

Blood sausages are made from edible by-products of the slaughterhouse and other ingredients. Ingredients in the formulation influence the quality of the final product; therefore, this study aimed to determine the nutritional, textural and sensory characteristics of pig blood sausages prepared with distinct meat and cereal fillers to ascertain a trade-off between technological benefits with nutritional quality and consumer satisfaction. Four versions of pig blood sausages were developed, each with varying amounts of meat and cereal fillers. The addition of fillers resulted in a significant decrease in protein, fat, ash contents and certain amino acids (leucine, phenylalanine, aspartic acid and serine) while increasing the moisture and carbohydrate content. The fatty acid profile was dominated by oleic acid. The sodium content of the sausages significantly decreased when the fillers were added. Iron was the highest trace mineral found in the sausages and was not affected by the addition of filler ingredients. The meat filler increased the meaty aroma, hardness and chewiness of the sausages, while cereal fillers decreased these parameters. Both the cereal and meat fillers reduced the salty and peppery flavours and the slippery texture of the sausages. The study indicates that sausages without filler ingredients offer a superior nutritional quality by providing higher essential nutrients. Sausages made with higher cereal content could be a more favourable option regarding texture and flavour compared to sausages formulated with meat fillers only. These findings highlight a trade-off; fillers can improve texture and certain sensory attributes, but they dilute essential nutrients and alter the traditional flavour balance of blood sausage.

Valeeprakhon Tamnuwat, Zhen Zhang, Makoto Iwata

Asynchronous processors adopt diverse pipeline styles, including linear, frequency-adaptive, selective, reduced, and combined styles, to balance performance, energy, and area. However, prior studies remain fragmented because benchmarks, tool support, and design flows differ. Those make fair comparison difficult and prevent a clear understanding of the trade-offs required for choosing pipeline styles for specific applications. Moreover, existing delay-placement flows rely on iterative synthesis or manual engineering change orders (ECOs), which are time-consuming, error-prone, and inefficient. To overcome these limitations, we introduce a morphological delay-placement constraint (MDPC), an automated delay-placement flow for bundled-data (BD) circuits on FPGAs. Compared with conventional flows, MDPC achieves single-pass timing closure with reduced slack deviation by up to 31.66% and LUT utilization by up to 5.56X, thereby enhancing performance by up to 11.94% and energy efficiency by up to 10.42%. Using MDPC, we systematically evaluate five asynchronous RISC-V pipeline styles against a synchronous baseline with typical benchmarks. The frequency-adaptive pipeline achieves the highest speed gains of 1.11%. The selective pipeline delivers the strongest energy savings of up to 65.85%. The reduced pipeline optimizes resource utilization, lowering area by up to 3.12%. The combined pipeline provides moderate improvements with energy reductions of up to 64.40%. In contrast, the linear pipeline exhibits the lowest overall efficiency, with execution slowdowns of up to 37.76%. The results reveal distinct trade-offs: the frequency-adaptive pipeline maximizes speed, the selective pipeline minimizes energy, the reduced pipeline achieves superior resource efficiency, the combined pipeline offers moderate overall gains, while the linear pipeline remains the least competitive.

Yaxi LIU, Xulong LI, Jiahao HUO et al.

Spatiotemporal crowdsourcing involves the use of various Internet of Things (IoT) devices distributed across industrial environments to collect and transmit spatiotemporal data related to industrial operations. Unmanned aerial vehicles (UAVs) play a crucial role in further collecting this data from IoT devices, especially in spatiotemporal crowdsourcing tasks. In the realm of industrial IoT energy management, allocating spatiotemporal crowdsourcing resources to UAVs poses substantial challenges. Traditional approaches to this problem have focused on optimizing the Age of Information (AoI) to ensure timely and equitable data updates. However, these methods often overlook critical operational constraints such as UAV no-fly zones and the risk of data interception by eavesdroppers. These issues can adversely affect the freshness and integrity of the information being gathered and transmitted. To address these shortcomings, this paper presents a novel deep reinforcement learning-based framework for UAV spatiotemporal crowdsourcing resource allocation. This approach aims to minimize the average AoI across the network while also reducing the energy consumption of IoT devices. It incorporates spatial constraints imposed by UAV no-fly zones and actively manages the transmission of jamming signals to mitigate the threat posed by eavesdroppers, thus ensuring data security. However, the complexity of allocating spatiotemporal crowdsourcing resources to UAVs is notable owing to numerous decision variables, which increase linearly with the duration of the service. Furthermore, the relationship between performance metrics and decision variables is intricate, requiring adherence to quality of service requirements. This problem is formalized as a Markov decision process (MDP), providing a structured approach to model the decision-making scenario faced by UAVs in a dynamic environment. To solve this MDP, we employ the soft actor critic (SAC) algorithm, an advanced deep reinforcement learning method known for its sample efficiency and stability. The SAC algorithm is adept at handling the continuous action spaces typical of UAV flight paths and power control problems, making it particularly well-suited for our application. We rigorously test our proposed methods in scenarios involving multiple UAVs, demonstrating the algorithm’s effectiveness in managing the spatiotemporal allocation of resources. Our results show that the SAC algorithm achieves faster convergence speed and better solutions than existing state-of-the-art methods, such as the twin delayed deep deterministic policy gradient (TD3) and the deep deterministic policy gradient (DDPG) algorithms. Furthermore, the paper delves into the strategic selection of the optimal number of UAVs to balance the trade-offs between coverage, energy consumption, and operational efficiency. By analytically and empirically examining the impact of the UAV fleet size on system performance, we provide insights into configuring UAV networks to achieve optimal outcomes in terms of AoI, energy management, and security. In conclusion, our research introduces a robust and intelligent framework for UAV resource allocation. The demonstrated efficacy of the SAC algorithm in this context paves the way for its future application in other domains where secure, efficient, and intelligent resource management is paramount.

Diem Ngoc Le, Duyen Ngoc Thi Nguyen

In recent years, global economic fluctuations have intensified, and Vietnam has increasingly integrated into the international market, highlighting the country’s growing economic integration. In the context of global trade tensions, the COVID-19 pandemic, and shifting international trade policies, Vietnam’s export and import activities have faced both significant challenges and new opportunities, directly impacting its trade balance. This study focuses on analyzing growth in Export-Import Turnover, export market size, and export commodity structure from 2016 to August 2024. The primary objective of this research is to assess the key trends in Vietnam’s trade, evaluate the opportunities and challenges affecting import-export activities, and provide actionable recommendations to improve the effectiveness of these activities in supporting national economic growth. The research methodology combines quantitative analysis of official government data with qualitative insights drawn from policy reports and academic studies. The findings show that Vietnam’s exports, especially in the manufacturing and agricultural sectors, have seen robust growth, while imports have mainly concentrated on raw materials and production equipment. Despite this, Vietnam’s heavy reliance on a limited number of key markets and exposure to global price fluctuations have created economic challenges, influencing its trade balance. The study then presented the advantages, limitations, and solutions for Vietnam’s import-export activities. The conclusion suggests that diversifying export markets and increasing added value in exports are essential to ensure sustainability and stable long-term development, contributing to economic growth. The findings offer valuable insights for both businesses and government agencies, enabling them to make informed decisions and develop effective policies to improve Vietnam’s international trade activities and enhance its economic growth.

Tao Qi, Wenhui Liu, Xiao Chang

This study focuses on the impact of China’s digital economy on sustainable modernization from 2011 to 2021, using provincial panel data for empirical analysis. By applying threshold and mediation models, we find that the digital economy promotes modernization through industrial upgrading (with a mediating effect of 38%) and trade openness (coefficient = 0.234). The research reveals “U-shaped” nonlinear threshold effects at specific levels of digital development (2.218), market efficiency (9.212), and technological progress (12.224). Eastern provinces benefit significantly (coefficient ranging from 0.12 to 0.15 ***), while western regions initially experience some inhibition (coefficient = −0.08 *). Industrial digitalization (coefficient = 0.13 ***) and innovation ecosystems (coefficient = 0.09 ***) play crucial roles in driving eco-efficiency and equity, in line with Sustainable Development Goals 9 and 13. Meanwhile, the impacts of infrastructure (coefficient = 0.07) and industrialization (coefficient = 0.085) are delayed. Economic modernization improves (coefficient = 0.37 ***), yet social modernization declines (coefficient = −0.12 *). This study not only enriches economic theory but also extends the environmental Kuznets curve to the digital economy domain. We propose tiered policy recommendations, including the construction of green digital infrastructure, carbon pricing, and rural digital transformation, which are applicable to China and offer valuable references for emerging economies aiming to achieve inclusive low-carbon growth in the digital era. Future research could further explore the differentiated mechanisms of various digital technologies in the modernization process across different regions and how to optimize policy combinations to better balance digital innovation with sustainable development goals.

Chokri Zehri

Modernizing and diversifying industries have become essential in recent years, particularly with the shift toward new energy sources to boost the global economy. Despite widespread initiatives, the economic impact of these reforms remains uncertain. This study examines the effects of Saudi Arabia’s renewable energy and industrial innovation efforts on key economic variables, aligning with the UN Sustainable Development Goal 8 (SDG 8), which emphasizes inclusive and sustainable economic growth, full and productive employment, and decent work for all. Using an ARDL model, we analyze data from 95 firms operating in the renewable energy sector from 2000 to 2023. The findings reveal that renewable energy investments significantly enhance long-term economic growth, trade balance, and FDI inflows, though their impact on employment and foreign assets is weaker. Industrial innovation also promotes growth and trade, but less so than renewable energy, with sales growth driving foreign asset accumulation. In the short term, both sectors have limited effects on employment and foreign assets. However, when combined, renewable energy investments and industrial innovation amplify their positive influence on GDP and trade, underscoring the need for long-term strategies to sustain economic growth.

Muzhou Yu, Zihan Zhong, Xingang Zhou et al.

Abstract Computational pathology leverages advanced deep-learning techniques to analyze medical images with high resolution. However, a trade-off exists between model lightweight, interpretability, and task performance in such real-world scenarios. Knowledge distillation (KD) is widely applied to compress deep learning models while preserving high performance. Despite this, deep learning-based KD often lacks interpretable design, leading to inaccurate attention to images. Inspired by human vision processing, we developed a human vision attention-inspired knowledge distillation (HVisKD) strategy that captures local and global patch relations to construct differentiated features. We employed it in pathological analysis to balance the tradeoff. HVisKD improves performance across various lightweight models in segmentation tasks. More importantly, the attention map of HVisKD showed promoted consistency with human expert-labeled segmentation. Furthermore, we examined HVisKD in a real-world intraoperative pathological diagnosis scenario and achieved an interpretable and fast analysis. Together, HVisKD offers a lightweight and interpretable strategy for computational pathology, aligning deep learning with brain-like information processing for more dependable output.

Muretijiang Muhetaer, Ailimulati Yusupu, Wang Yifan et al.

Abstract This study evaluates the effectiveness of Chinese doctor–patient dialogues as retrieval sources for Retrieval-Augmented Generation (RAG) in clinical question answering. Using ChatGPT-3.5 as a baseline and extending to GPT-4o and GPT-5, we compare multiple retrieval pipelines, including dense retrieval, Cross-Encoder reranking, Reciprocal Rank Fusion (RRF), and Cascade RRF→Rerank. Experimental results show that dialogue-based retrieval significantly improves generation quality relative to direct prompting (e.g., ROUGE-1-f: +12.6%, BERTScore_F1: +1.5%, p < 0.05). Among retrieval strategies, Rerank-only provides the best accuracy–latency balance, while the cascade pipeline introduces noise and yields no additional benefit. Under identical retrieval settings, GPT-4o achieves stronger automatic metrics and 4–5× lower latency, whereas GPT-5 receives slightly higher human preference scores (+ 0.08, p < 0.001), indicating a trade-off between efficiency and perceived coherence. Expert evaluation further confirms improvements in readability, accuracy, and authenticity (all p < 0.001). These findings highlight that data representation and metadata structure have a greater impact on RAG performance than retrieval algorithm complexity, offering practical guidance for reliable medical QA deployment.

Kawsar Nassereddine, Marek Turzynski

Abstract Traditional controllers frequently perform poorly when managing complex industrial systems because they must balance multiple goals like cost, pollution, and efficiency. For higher performance and efficiency, model predictive controller addresses this but necessitates efficient cost function tuning, which is currently frequently enhanced utilising metaheuristic algorithms. This work aims to develop and validate a data-driven weight optimisation method for multivariable model predictive controller in a DC microgrid comprising photovoltaic panels, battery, supercapacitor, grid, and load—by systematically balancing control effort and accuracy. Four algorithms—particle swarm optimization, genetic algorithm, pareto search, and pattern search-selected for their capacity to resolve complex, multi-objective problems are used in this study’s automated, set-point tracking-based weight optimisation approach. The results show that incorporating parameter interdependency reduces genetic algorithm’s power load tracking error from 16% to 8%, while particle swarm optimization achieves an error of under 2% even without considering interdependency. Fast convergence and trade-offs are supported by pareto and pattern search, but they are less responsive to sudden changes. These methods provide a feasible, repeatable way to boost model predictive controller performance.

Chengtian Ma, Yunyue Wei, Chenhui Zuo et al.

Balance control is important for human and bipedal robotic systems. While dynamic balance during locomotion has received considerable attention, quantitative understanding of static balance and falling remains limited. This work presents a hierarchical control pipeline for simulating human balance via a comprehensive whole-body musculoskeletal system. We identified spatiotemporal dynamics of balancing during stable standing, revealed the impact of muscle injury on balancing behavior, and generated fall contact patterns that aligned with clinical data. Furthermore, our simulated hip exoskeleton assistance demonstrated improvement in balance maintenance and reduced muscle effort under perturbation. This work offers unique muscle-level insights into human balance dynamics that are challenging to capture experimentally. It could provide a foundation for developing targeted interventions for individuals with balance impairments and support the advancement of humanoid robotic systems.

Sicheng Wang, Shuhao Chen, Jingran Zhou et al.

Global food trade plays a crucial role in ensuring food security and maintaining supply chain stability. However, its network structure evolves dynamically under the influence of geopolitical, economic, and environmental factors, making it challenging to model and predict future trade links. Effectively capturing temporal patterns in food trade networks is therefore essential for improving the accuracy and robustness of link prediction. This study introduces IVGAE-TAMA-BO, a novel dynamic graph neural network designed to model evolving trade structures and predict future links in global food trade networks. To the best of our knowledge, this is the first work to apply dynamic graph neural networks to this domain, significantly enhancing predictive performance. Building upon the original IVGAE framework, the proposed model incorporates a Trade-Aware Momentum Aggregator (TAMA) to capture the temporal evolution of trade networks, jointly modeling short-term fluctuations and long-term structural dependencies. A momentum-based structural memory mechanism further improves predictive stability and performance. In addition, Bayesian optimization is used to automatically tune key hyperparameters, enhancing generalization across diverse trade scenarios. Extensive experiments on five crop-specific datasets demonstrate that IVGAE-TAMA substantially outperforms the static IVGAE and other dynamic baselines by effectively modeling temporal dependencies, while Bayesian optimization further boosts performance in IVGAE-TAMA-BO. These results highlight the proposed framework as a robust and scalable solution for structural prediction in global trade networks, with strong potential for applications in food security monitoring and policy decision support.

Ritwik Kulkarni, WU Hanqin, Enrico Di Minin

Unsustainable trade in wildlife is a major threat to biodiversity and is now increasingly prevalent in digital marketplaces and social media. With the sheer volume of digital content, the need for automated methods to detect wildlife trade listings is growing. These methods are especially needed for the automatic identification of wildlife products, such as ivory. We developed machine learning-based object recognition models that can identify wildlife products within images and highlight them. The data consists of images of elephant, pangolin, and tiger products that were identified as being sold illegally or that were confiscated by authorities. Specifically, the wildlife products included elephant ivory and skins, pangolin scales, and claws (raw and crafted), and tiger skins and bones. We investigated various combinations of training strategies and two loss functions to identify the best model to use in the automatic detection of these wildlife products. Models were trained for each species while also developing a single model to identify products from all three species. The best model showed an overall accuracy of 84.2% with accuracies of 71.1%, 90.2% and 93.5% in detecting products derived from elephants, pangolins, and tigers, respectively. We further demonstrate that the machine learning model can be made easily available to stakeholders, such as government authorities and law enforcement agencies, by developing a smartphone-based application that had an overall accuracy of 91.3%. The application can be used in real time to click images and help identify potentially prohibited products of target species. Thus, the proposed method is not only applicable for monitoring trade on the web but can also be used e.g. in physical markets for monitoring wildlife trade.

Julia Ackermann, Thomas Kruse, Mikhail Urusov

We analyze a continuous-time optimal trade execution problem in multiple assets where the price impact and the resilience can be matrix-valued stochastic processes that incorporate cross-impact effects. In addition, we allow for stochastic terminal and running targets. Initially, we formulate the optimal trade execution task as a stochastic control problem with a finite-variation control process that acts as an integrator both in the state dynamics and in the cost functional. We then extend this problem continuously to a stochastic control problem with progressively measurable controls. By identifying this extended problem as equivalent to a certain linear-quadratic stochastic control problem, we can use established results in linear-quadratic stochastic control to solve the extended problem. This work generalizes [Ackermann, Kruse, Urusov; FinancStoch'24] from the single-asset setting to the multi-asset case. In particular, we reveal cross-hedging effects, showing that it can be optimal to trade in an asset despite having no initial position. Moreover, as a subsetting we discuss a multi-asset variant of the model in [Obizhaeva, Wang; JFinancMark'13].

Tong Zhang, Boyuan Zheng, Ruiqian Nai et al.

The human body demonstrates exceptional motor capabilities-such as standing steadily on one foot or performing a high kick with the leg raised over 1.5 meters-both requiring precise balance control. While recent research on humanoid control has leveraged reinforcement learning to track human motions for skill acquisition, applying this paradigm to balance-intensive tasks remains challenging. In this work, we identify three key obstacles: instability from reference motion errors, learning difficulties due to morphological mismatch, and the sim-to-real gap caused by sensor noise and unmodeled dynamics. To address these challenges, we propose HuB (Humanoid Balance), a unified framework that integrates reference motion refinement, balance-aware policy learning, and sim-to-real robustness training, with each component targeting a specific challenge. We validate our approach on the Unitree G1 humanoid robot across challenging quasi-static balance tasks, including extreme single-legged poses such as Swallow Balance and Bruce Lee's Kick. Our policy remains stable even under strong physical disturbances-such as a forceful soccer strike-while baseline methods consistently fail to complete these tasks. Project website: https://hub-robot.github.io

Alina BABA

The paper delves into the development and intricacies of international legal frameworks for intellectual property (IP) rights protection and commercialization, focusing on agreements like the Paris and Berne Conventions. It outlines the evolution from initial measures safeguarding industrial property to broader copyright and trademark protections, facilitated by entities such as the World Intellectual Property Organization (WIPO) and the World Trade Organization (WTO). The Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) is highlighted for establishing minimum standards for IP protection and enforcement, integrating provisions for dispute resolution, and managing the balance between IP holders’ interests and the public good. TRIPS’ role in addressing challenges posed by differing national norms and its efforts to mitigate IP rights abuse through limitations and exceptions, known as the Three-Step Test, are emphasized. This analysis underscores the importance of a fair and balanced IP system that promotes global innovation and creativity, acknowledging the need for a harmonious integration of varied national laws within the global trade and IP protection landscape.

Yu. S. Bogachev, S. R. Bekulova

In the context of the turbulence of the global economy, the relevance of research aimed at determining the ability to ensure the sustainable development of national economies within the current model of global economic development is increasing.   The purpose of this study is to obtain data characterizing the development potential of national economies.   The article analyzes the impact of globalization on the dynamics of development of the leading national economies in the ranking of countries in terms of GDP at PPP with a population of more than 50 million people. At the same time, the following characteristics were studied: labor productivity, the level of debt burden, the level of consumption of industrial products and services in the domestic market, the standard of living of the population, the ratio of income of the population and the level of per capita consumption. It is shown that in the analyzed countries the total per capita consumption is greater than per capita GDP. It was revealed that the differentiation of the debt burden in various segments of the economy is due to the difference in the dynamics of labor productivity. It is shown that within the framework of the current models of national economies, the conditions for the generation of structural problems and the decline in the level of consumption and the quality of life of the population have been formed.

Eduardo Soares Calixto, Isabela Cristina de Oliveira Pimenta, Denise Lange et al.

The net outcomes of mutualisms are mediated by the trade-offs between the costs and benefits provided by both partners. Our review proposes the existence of a trade-off in ant protection mutualisms between the benefits generated by the ants’ protection against the attack of herbivores and the losses caused by the disruption of pollination processes, which are commonly not quantified. This trade-off has important implications for understanding the evolution of extrafloral nectaries (EFNs), an adaptation that has repeatedly evolved throughout the flowering plant clade. We propose that the outcome of this trade-off is contingent on the specific traits of the organisms involved. We provide evidence that the protective mutualisms between ants and plants mediated by EFNs have optimal protective ant partners, represented by the optimum point of the balance between positive effects on plant protection and negative effects on pollination process. Our review also provides important details about a potential synergism of EFN functionality; that is, these structures can attract ants to protect against herbivores and/or distract them from flowers so as not to disrupt pollination processes. Finally, we argue that generalizations regarding how ants impact plants should be made with caution since ants’ effects on plants vary with the identity of the ant species in their overall net outcome.

Xialing Wen, Ying Yan

In recent years, there is a growing body of causal inference literature focusing on covariate balancing methods. These methods eliminate observed confounding by equalizing covariate moments between the treated and control groups. The validity of covariate balancing relies on an implicit assumption that all covariates are accurately measured, which is frequently violated in observational studies. Nevertheless, the impact of measurement error on covariate balancing is unclear, and there is no existing work on balancing mismeasured covariates adequately. In this article, we show that naively ignoring measurement error reversely increases the magnitude of covariate imbalance and induces bias to treatment effect estimation. We then propose a class of measurement error correction strategies for the existing covariate balancing methods. Theoretically, we show that these strategies successfully recover balance for all covariates, and eliminate bias of treatment effect estimation. We assess the proposed correction methods in simulation studies and real data analysis.

Rongxi Peng, Tao Liu, Guangzhong Cao

The concept of land use functions (LUFs) has been widely employed to study and manage sustainable development. However, its employment is barely based on actual land uses. Difficulties in the accessibility of data and comparability of results also hinder the wide application of contemporary LUF frameworks on sustainability analysis. To fill these gaps, this study improves the LUF framework in which the monetary value of economic, social, and environmental LUF is evaluated using land use data. This framework is then used to examine how different LUFs relate to each other in Shandong, China. Results show that, at the township level, monetary values of economic and social functions are positively correlated, but are both negatively correlated with environmental function. All three functions grew between 2009 and 2018 in Shandong. Results also suggest that a focus on quantitative trade-offs of these three LUFs is insufficient; rather, their spatial balance also requires attention.

Nicholas M. Wilkinson

Within minutes of birth a newborn gnu or giraffe works to stand and walk, asserting postural balance and organised animate behaviour in an apparently goal-directed manner. In contrast, robots learning to stand and walk from scratch begin with random flailing, the behaviour cohering over time as the robot internalises some reward or value signal. How does the newborn gnu innately know what goal to aim for, and decide to work towards it? How could similar goal-directed balance learning be implemented in robots? Currently, animate balance inherits its axiomatic definition from the Newtonian formulation for inanimate balance - static mechanical equilibrium. This is arguably inappropriate for animate balance, because animals need to move and are never in static mechanical equilibrium, giving rise to the posture-movement paradox. The present perspective article proposes a more fluid, dynamical axiomatic task definition and goal which (a) isolates resisting gravity, (b) admits and enables movement, and (c) subsumes static mechanical equilibrium as a special case. This novel definition is founded upon inevitable biophysical requirements and observable developmental process. The article explains how animals apprehend and embed this goal through prenatal development suspended in equidense amniotic fluid, and then are challenged to self-maintain it by the perinatal transition. The account entails a paradigmatic shift in putative physiological organisation and associated conceptual framework for balance from a subsidiary sensorimotor control task, to a vital mechano-regulation task organisationally akin to thermo-regulation. This vital mechano-regulation model of balance has practical implications and implies a range of predictions.