Hasil untuk "Balance of trade"

Jonathan Huang, V. Rathod, Chen Sun et al.

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-toapples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [30], R-FCN [6] and SSD [25] systems, which we view as meta-architectures and trace out the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.

Quan Lin, Jing Wang, Zhenbiao Fu et al.

Cyclocarya paliurus (Batal.) Iljinskaja is a prominent medicinal plant for edible and pharmaceuticals, yet its practical application is highly constrained due to the limited production of secondary metabolites in the leaves and the growth-secondary metabolism trade-off. Inoculation with soil beneficial microorganisms is a sustainable strategy to stimulate the production of secondary metabolites and balance such trade-off. The pot experiment and field trials were assigned to investigate how Bacillus velezensis FZB42 affects the growth and production of secondary metabolites in C. paliurus by integrated underground profiles (native microbial community, nutrient availability, and hormone levels in the rhizosphere) and aboveground responses (nutrient acquisition, biomass, photosynthesis, and secondary metabolites in plants). The results from pot experiment presented the improvements not only in the plant biomass but also in secondary metabolites driven by FZB42. The field trials further preliminary substantiated the effects of inoculant on the growth-promotion and secondary-metabolism-enhancement. Based on potted plants, significant increments in root and leaf biomass of the inoculated plants were 1.75 and 1.52 times of the control, while the contents of flavonoids and triterpenoids reached 10.89 mg g−1 and 43.66 mg g−1, respectively, apparently higher than 7.90 mg g−1 and 35.91 mg g−1 in the control. Inoculation enhanced soil nutrient availability through increased extracellular enzyme activities, resulting in 190 % and 62.98 % increases in available nitrogen and phosphorus, respectively. Moreover, FZB42 application enriched specific native bacterial taxa (e.g., Gammaproteobacteria) and reshaped rhizosphere hormone profiles, including indole-3-acetic acid (IAA), salicylic acid (SA), and jasmonates (JA). Structural equation modeling further demonstrated that alterations in rhizosphere hormone levels mediated by specific native bacterial taxa exerted significant effects on plant secondary metabolism. These findings offer insights into the regulations of FZB42 on growth–metabolism trade-off in medicinal plants, and its potential application as a bio-inoculant in plantations of C. paliurus for medicinal use.

Taiwo A. Muritala

West Africa’s economic trajectory over the last decade has been shaped by persistent trade imbalances, heavy reliance on a narrow range of primary commodity exports, and heightened vulnerability to global shocks. This paper examines the nexus between trade balance, export diversification, and economic resilience in West Africa, from 2014 to 2024, a period marked by the oil price crash, the COVID-19 pandemic, the war in Ukraine, and the rollout of the African Continental Free Trade Area (AfCFTA). Drawing on secondary data from the World Bank, UNCTAD, ECOWAS Commission, and national statistical offices, the study employs descriptive trend analysis and empirical evidence from recent literature to assess how diversification patterns influence macroeconomic stability. The findings show that West Africa’s economies remain highly commodity-dependent, with over 70% of export earnings concentrated in hydrocarbons, minerals, and agricultural products. However, countries with broader export baskets-such as Côte d’Ivoire, Ghana, and Senegal-exhibited relatively greater resilience during external shocks, as reflected in smaller trade deficits and faster post-crisis recovery. The evidence further indicates that structural trade imbalances in Nigeria, the subregion’s largest economy, magnify regional vulnerabilities due to overdependence on crude oil exports and high import bills. The paper concludes that export diversification is a critical driver of resilience, as it reduces exposure to global commodity cycles, strengthens intra-African trade, and supports sustainable growth. Policy recommendations emphasize the need to deepen industrialization, leverage AfCFTA for regional value chains, invest in transport and digital infrastructure, and expand access to finance for small and medium-sized enterprises (SMEs).

Szu-Yung Wang, Nian-Zu Ye

IntroductionScope 3 greenhouse gas emissions are critical to firms’ carbon footprints yet are often difficult to quantify due to limited direct data, motivating predictive modeling approaches.MethodsWe developed and compared four machine learning algorithms (K-nearest neighbors, random forest, AdaBoost, and XGBoost) to estimate corporate Scope 3 emissions using readily available financial and sustainability performance data. We leverage 10,449 listed firm-level data from 2014 to 2023, covering major industries such as semiconductor, steel, textile, and building materials, evaluating performance of each model by a held-out test set with metrics including R2, mean absolute percentage error (MAPE), and root mean squared logarithmic error (RMSLE).ResultsXGBoost achieved the highest accuracy (R2 = 0.85, MAPE = 15%, RMSLE = 0.20), outperforming random forest (R2 = 0.80, MAPE = 20%) and AdaBoost (R2 = 0.78), while K-NN had the lowest accuracy (R2 = 0.60). The results demonstrate that ensemble tree-based models substantially improve Scope 3 emission prediction accuracy over simpler models.DiscussionNotably, random forest’s interpretable feature importance provided insight into key emission drivers with only a slight accuracy trade-off, highlighting the balance between predictive accuracy and model interpretability.

Waruni Hewage, R. Sinha, M. Asif Naeem

Abstract Data stream mining is a critical process utilized by organizations to derive insights from real-time data. Consequently, preserving the privacy of sensitive information while maintaining high accuracy remains a persistent challenge. Privacy-preserving data mining techniques modify data to increase privacy, a process that invariably decreases the accuracy of data mining algorithms. Though different techniques have been proposed to preserve privacy, there is a lack of well-formulated frameworks to optimize the trade-off between accuracy and privacy. This paper introduces a novel Accuracy-Privacy Optimization Framework (APOF) that allows users to define privacy requirements and predicts achievable accuracy levels, enabling fine-tuning of this balance. The logistic cumulative noise addition was used as the data perturbation method that has experimentally shown better performance and Hoeffding trees as the classifier. Additionally, a data fitting module using kernel regression is integrated, a unique approach that predicts accuracy levels based on user-defined privacy thresholds. Experimental results show that the proposed framework archives an optimal privacy level above 97% while minimising the accuracy loss across various datasets. By addressing critical gaps in privacy-preserving data mining, this study offers significant contributions to real-world applications, facilitating secure and efficient data utilization in dynamic environments.

Shuo Zhao, Xinghua Shan, Jinfei Wu et al.

Stop planning is aimed to provide proper services for passenger demand, but diverse stop patterns lead to differences in stop density and travel speeds, impacting the utilization of line capacity. This paper incorporates capacity utilization into stop planning in the strategic line planning stage to trade off the matching between supply and demand and the stop distribution balance among trains. A bi-level programming model is established to formulate the Stackelberg game relation between supply and demand, where the stop distribution imbalance and the passenger travel inefficiency are measured. An adaptive hybrid solving algorithm combined with Genetic Algorithm and Simulated Annealing Algorithm is proposed, with several adaptive operations according to the problem characteristics and optimization degree to improve searching efficiency. A case study on the local network of Beijing–Shanghai High-speed Railway Line demonstrates that the proposed approach can not only mitigate the stop distribution imbalance, but also improve the travel efficiency of passengers, indicating that it can benefit the simultaneous improvement of capacity utilization and service level.

Stine Braaen, Øystein Wessel, Håvard Bjørgen et al.

<b>Background</b>: Pancreas disease (PD) is a serious disease in European salmonid aquaculture caused by salmonid alphavirus (SAV), of which six genotypes (SAV1–6) have been described. The use of inactivated virus and DNA PD vaccines is common in marine salmonid aquaculture and has contributed to a reduction of the occurrence of disease; however, outbreaks are still frequent. <b>Methods</b><i>:</i> In this study, we compared the long-term protection after immunization of Atlantic salmon (<i>Salmo salar</i>) with three different clones of attenuated infectious SAV3. The clones were made by site-directed mutagenesis targeting the glycoprotein E2 to disrupt the viral attachment and/or nuclear localization signal (NLS) of the capsid protein to disrupt the viral suppression of cellular nuclear-cytosol trafficking. The resulting clones (Clones 1–3) were evaluated after injection of Atlantic salmon for infection dynamics, genetic stability, transmission, and protection against a subsequent SAV3 challenge. <b>Results</b>: Attenuated clones demonstrated reduced virulence, as indicated by lower viral RNA loads, diminished transmission to cohabitant fish, and minimal clinical symptoms compared to the virulent wild-type virus. The clones mutated in both capsid and E2 exhibited the most attenuation, observed as rapid clearing of the infection and showing little transmission, while the clone with glycoprotein E2 mutations displayed greater residual virulence but provided stronger protection, seen as reduced viral loads upon subsequent challenge with SAV3. Despite their attenuation, all viral clones caused significant reductions in weight gain. <b>Conclusions</b><i>:</i> Despite promising attenuation and protection, this study highlights the trade-offs between virulence and immunogenicity in live vaccine design. Concerns over environmental risks, such as the shedding of genetically modified virus, necessitate further evaluation. Future efforts should optimize vaccine candidates to balance attenuation, immunogenicity, and minimal side effects.

somayeh Shirzadi Laskookalayeh

Extended Abstract Background: The inadequacy of the supply of agricultural inputs with the demand for various products of this sector reveals the need for the optimal use of resources and increasing productivity. In this regard, addressing the issue of productivity in rice production is very important due to its essential role in feeding different sections of society, providing food security, reducing dependence on imports and foreign exchange, strengthening trade interactions with other countries, generating income, creating employment, creating balance in the business and capital market, and many other issues. In 2022, Mazandaran Province produced 1.6 million tons of paddy as a strategic product, responsible for 44.47% of Iran's paddy production, and in this sense, it has been ranked first in the country. This province has long been known as the hub of rice production, and this user product, having about 76% of Mazandaran's irrigated crop area, has always made an important contribution to the province's employment. For this purpose, the present study aimed to identify factors affecting the prosperity of the rice production business in Mazandaran Province, focusing on measuring the inefficiency of various production inputs, especially the labor force. Methods: Three institutional, managerial, and policy-market criteria effective in the prosperity of rice production business were extracted in this study. The input criterion includes all production factors affecting the productivity of this product, which includes eight subcriteria as water, labor, land, fertilizer, poison, machinery, capital, and seed. The management criterion is all management actions by relevant organizations and bodies (Jahad Keshavarzi, Regional Water, Room of Commerce), which includes six regulatory, executive, organizational, service, and innovation options. The political-market criterion also covered the macro-government policies that can affect the productivity of rice, and there are six financial, economic, structural, commercial, marketing, and strategic development options. Thus, 19 effective options in the productivity of rice production were considered in this study. In this study, factors affecting the productivity of this product were exracted and prioritized using the Analytical Hierarchy Process (AHP) method, measuring the production efficiency of important cultivars of this product (high-quality rice and high-yielding rice) using the data envelopment analysis method (DEA), and then examining productivity changes over time using the Malmquist Index (MI). The data needed for identifying and prioritizing factors in this research were collected by designing a questionnaire, which was completed based on the opinions of 18 experts, including those from the Agricultural Jihad Organization of Mazandaran Province and Sari City, as well as the academic community. The statistics and information of the Agricultural Jahad Organization of the province were used to complete the data in measuring the productivity of production and efficiency of inputs. Results: The results indicate that among the eight production factors, water, mechanization, and land are the most important input factors in rice production with weights of 0.36, 0.2, and 0.14, respectively. Among the five management factors, benefiting from the opinions of agricultural experts, implementing the optimal cultivation pattern of crops according to the climatic conditions and the status of water resources in the province, and using new technologies in agricultural operations with weights of 0.40, 0.25, and 0.14, respectively, were known as three important and superior factors for the management of rice production business. In addition, the financial, economic options, and improvement of the structure of the rice product marketing system were determined with the weights of 0.30, 0.22, and 0.19, respectively, as three policy-market subcriteria affecting the rice productivity of this province. Based on the findings in the agricultural year of 2017-2018 in the east of this province, Qaemshahr City, the land, machinary, poison, and fertilizer inputs were inefficient at 52.68%, 48.26%, 34.37%, and 33.16%, respectively. In 2018, the inefficiency rates in the use of land, labor, and poison inputs were 71.36%, 15.09%, and 4.46%, respectively. In the production of high-yielding rice in the east of the province, there has been inefficiency in the use of land, machinary, seed, water, and fertilizer inputs. Accordingly, Behshahr City acted inefficiently in consuming the mentioned inputs by 68.29, 52.60, 16.65, 12.63, and 7.55%, respectively. In 1998, the cities of Behshahr and Neka acted inefficiently in the consumption of all the investigated inputs, except for machinery. The percentages of inefficiency in the labor input are 16.14 and 42.07%, respectively. In addition, the productivity growth index values of Malmquist in the production of high-quality rice and high-yielding rice are 1.155 and 1.094, respectively. Hence, it can be concluded that the production productivity of this product has increased in this province. Conclusion: The results indicate that the productivity of different rice varieties has increased during the studied period. In the case of high-yielding rice, however, the technical efficiency of producers in newer technology is lower than in older technology. Therefore, it is necessary for trustee organizations and knowledge-based companies to invest in the research, innovation, and promotion of new technology in training to use this technology. In this study, "water" has been determined as the most important input affecting the productivity of this product; therefore, it is recommended to take necessary measures to promote water storage and reduce its consumption. It is also suggested to provide financial support to rice farmers and the development of knowledge-based companies to provide new irrigation systems. Referring to the results of this study, the use of "machinery" is considered the second most effective factor in increasing productivity. In addition to reducing the cost of manpower and saving time, the uniformity and accuracy of the work are increased with mechanized cultivation, and seedlings are exposed to less damage. However, this issue does not mean to ignore the role and importance of the workforce in the production and elimination of job opportunities. Rather, it is recommended to train skilled and specialized human resources to benefit from mechanization for the long-term stability of the rice production business and stable rural employment.

Xianglong Liu, Desheng Hong, Hongyang Dong et al.

Abstract The eastern margin of the Qinghai-Xizang Plateau, as a critical transition zone between the plateau and the Sichuan Basin, poses substantial challenges for geographic regionalization, primarily due to its intricate terrain and climatic heterogeneity. Traditional spatial clustering methods often struggle to balance spatial continuity and attribute similarity, suffering from subjectivity and inadequate representation of topographic complexity. This study proposes a novel mountainous geographic regionalization framework that integrates topographic and climatic characteristics, using Kangding county as a typical case. Principal Component Analysis (PCA) was employed to perform dimensionality reduction on multiple environmental variables and assign relative weights. A Gaussian-weighted function was further applied to adjust attribute distances to capture spatial non-stationarity, while the geographic distance weight was systematically optimized. The partitioning outcomes were evaluated using clustering quality indicators (Davies-Bouldin index, Silhouette index, Calinski-Harabasz index) and spatial autocorrelation indicators (Moran’s I index, Moran’s Z-score). Results indicated that when the number of clusters was set to five and the geographic distance weight was 0.5, the clustering algorithm optimized the trade-off between spatial continuity and attribute similarity (Davies-Bouldin index = 1.14, Silhouette index = 0.30, Calinski-Harabasz index = 25150.91, Moran’s I = 0.97, Moran’s Z-score = 292.28). Compared to the traditional K-means clustering, this approach enhanced intra-cluster similarity (Sil) by 259% and improved spatial continuity (Moran’s I, Moran’s Z-score) by approximately 44%. This method effectively addresses the challenge of coordinating spatial constraints with attribute heterogeneity in mountainous environmental zoning, in a county scale, providing an automated, data-driven approach for geographic partitioning in complex terrains. The findings offer valuable insights for mountain ecosystem management and regional geographic studies. Our study provides a set of effective methods of demarcation of regional boundaries based on raster data, offering important insights for ecological zoning management and regional studies in mountainous environments at a small scale.

Liuyun Deng, Zhiguo Du, Xiaoyong Liu et al.

Accurate identification of grape leaf varieties is an important prerequisite for effective viticulture management, contributing to breeding programs, cultivation strategies, and precision field operations. However, reliable recognition in complex field environments remains challenging. Subtle interclass morphological variations among leaves, background interference under natural conditions, and the need to balance recognition accuracy with computational efficiency for mobile applications represent key obstacles that limit practical deployment. This study proposes an improved lightweight convolutional neural network, termed ICS-MobileNetV3-Small (ICS-MS), specifically designed for grape leaf variety recognition. The model’s core innovations, detailed in Key Innovations of the Proposed ICS-MS Model section, include three key components: First, a coordinate attention mechanism is embedded to enhance the network’s ability to capture spatially distributed features while suppressing irrelevant background noise. Second, a multi-branch ICS-Inception structure is integrated to accomplish excellent multi-scale feature fusion, allowing the model to discern minute textural variations among types. Moreover, the feature representation is further optimized by adopting a joint loss function, which improves feature space distribution and enhances classification robustness. Experimental evaluations were conducted on a dataset comprising eleven grape leaf varieties. The proposed ICS-MS model achieves a recognition accuracy of 96.53% with only 1.17 M parameters. Experimental results demonstrate that, compared with the baseline MobileNetV3-Small model, the standalone integration of the Coordinate Attention (CA) mechanism improves accuracy by 0.17% while reducing the number of parameters by 10.4%. Furthermore, incorporating the ICS-Inception structure leads to an additional 4.78% accuracy improvement with only a marginal increase in parameter count. Finally, the introduction of a joint loss function provides an extra 0.23% gain in accuracy, resulting in an overall parameter reduction of approximately 23.5% compared with the baseline model. Three core contributions are highlighted as follows: (1) the construction of an integrated technical framework of “spatial feature enhancement—multi-scale fusion—feature distribution optimization” to systematically address the key issues of insufficient fine-grained feature extraction and the balance between lightweight design and accuracy; (2) the design of a lightweight CA-Block module that reduces parameters by 18.7% while enhancing spatial feature discrimination; (3) the achievement of superior performance with fewer parameters, providing a practical solution for mobile deployment in precision viticulture. Values for precision, recall, and F1-score were continuously near 96%, suggesting a good trade-off between efficiency and accuracy. These findings suggest that ICS-MS provides a practical and reliable approach for grape leaf identification and may serve as a useful tool to support intelligent management in precision viticulture.

Emma R. Dodoo, Tamara Nelson-Fromm, Mark Guzdial

K-12 computing teachers must navigate complex trade-offs when selecting programming languages and instructional materials for classrooms with emergent bilingual students. While they aim to foster an inclusive learning environment by addressing language barriers that impact student engagement, they must also align with K-12 computer science curricular guidelines and prepare students for industry-standard programming tools. Because programming languages predominantly use English keywords and most instructional materials are written in English, these linguistic barriers introduce cognitive load and accessibility challenges. This paper examines teachers' decisions in balancing these competing priorities, highlighting the tensions between accessibility, curriculum alignment, and workforce preparation. The findings shed light on how our teacher participants negotiate these trade-offs and what factors influence their selection of programming tools to best support EB students while meeting broader educational and professional goals.

Malin Niemi, Nicklas Nordfors, Anna Tompsett

What happens to pollution when developing countries open their borders to trade? Theoretical predictions are ambiguous, and empirical evidence remains limited. We study the effects of the 1991 Indian trade liberalization reform on water pollution. The reform abruptly and unexpectedly lowered import tariffs, increasing exposure to trade. Larger tariff reductions are associated with relative increases in water pollution. The estimated effects imply a 0.11 standard deviation increase in water pollution for the median district exposed to the tariff reform.

Froylan E Sifuentes, Sophie C Major, Ben McNett et al.

This paper presents the development and application of the equity and climate impacts optimization in community energy (ECOCE) model, a mixed integer linear tool designed to optimize equity-driven investment in community solar projects (CSPs). By providing insight on a variety of impacts of CSP investments, including electricity bill savings, solar production, and greenhouse gas emissions reduction, the model assists policymakers in understanding the possible trade-offs in benefits and impacts of CSP investments. Using a detailed case study of a one-time $100 million investment in CSPs in Washington State, the model evaluates potential scenarios under various funding distributions to determine the optimal allocation of state funds. The study highlights considerations of geographical equity and participation, trade-offs between maximizing greenhouse gas reductions and minimizing energy burden, and the potential to use the model in different regional contexts. The findings suggest that targeted investments can provide significant electricity bill savings ($6.5–8.5 million annually) for low-income communities while contributing to state decarbonization goals (4–42 kTons of avoided emissions annually) in Washington State, though political and practical considerations may influence the feasibility of these optimized allocations. The ECOCE model provides a robust framework for decision-makers aiming to balance a variety of political, equity, and climate change mitigation considerations in the transition to renewable energy.

Nicola Borri, Yukun Liu, Aleh Tsyvinski et al.

The European Union Emission Trading System is a prominent market-based mechanism to reduce emissions. While the theory is well understood, we are the first to study the whole cap-and-trade mechanism as a financial market. Analyzing the universe of transactions in 2005-2020 (more than one million records of granular transaction data), we show that this market features significant inefficiencies undermining its goals. First, about 40% of firms never trade in a given year. Second, many firms only trade during surrendering months, when compliance is immediate and prices are predictably high. Third, a number of operators engage in speculative trading, exploiting private information.

Catalina M Jaramillo, Paul Squires, Julian Togelius

Fairness,the impartial treatment towards individuals or groups regardless of their inherent or acquired characteristics [20], is a critical challenge for the successful implementation of Artificial Intelligence (AI) in multiple fields like finances, human capital, and housing. A major struggle for the development of fair AI models lies in the bias implicit in the data available to train such models. Filtering or sampling the dataset before training can help ameliorate model bias but can also reduce model performance and the bias impact can be opaque. In this paper, we propose a method for visualizing the biases inherent in a dataset and understanding the potential trade-offs between fairness and accuracy. Our method builds on quality-diversity optimization, in particular Covariance Matrix Adaptation Multi-dimensional Archive of Phenotypic Elites (MAP-Elites). Our method provides a visual representation of bias in models, allows users to identify models within a minimal threshold of fairness, and determines the trade-off between fairness and accuracy.

Lasha Beridze

Just when the whole world, which was already almost stopped and reduced as a result of the pandemic, was starting the process of restoring the global economy, the Russian Federation invaded Ukraine, which further stopped the process of economic recovery and deepened the world economic crisis, which was previously dictated by the breakdown of the supply chains. Russia and Ukraine, with their economic scale, are important players in ensuring the world trade balance, both jointly and separately, so the start of the war led to an increase in the prices of a number of goods and services, which particularly affected strategic goods such as oil products, gas, wheat, corn, oil and others. The increase in the prices of strategic goods was quite large-scale at the world level, so in the most countries of the world the rate of inflation increased, which led to the correction of various macroeconomic indicators, while such corrections turned out to be more sensitive for the countries of the European continent, which depended on Russian energy resources. Although the economic and financial sanctions were quite immediate, some countries still failed to join the sanctions and plans were developed to reduce their dependence on Russian energy resources and goods, because joining the sanctions completely would cause the collapse of the economy of such countries, since some countries are completely dependent on Russian resources. Russia and Ukraine are one of the most important trade and partner countries for Georgia, that's why the war had a significant impact on the economy of Georgia. Despite the fact that Georgia largely joined the sanctions imposed by the European Union, it still maintained its openness to the Russian economy and trade, which was reflected in the Georgian economy by the inflow of remittances from Russia and the increase by the flow of travelers.

Natallia Bandarenka

Background and Objective: Foreign economic flows reflected in the country’s balance of payments are an important factor in economic stability. At the same time, export and import flows of goods, services and incomes largely depend on external factors, such as closed borders, the existence of economic sanctions, financial interdependence between countries, etc. The purpose of the study is to assess the state of current operations of the balance of payments of the Republic of Belarus in the current conditions of foreign trade. Materials and methods: The analysis is based on data of the National Statistical Committee and the National Bank of the Republic of Belarus. Methods of analysis and synthesis are used; comparative, systemic and statistical analysis; method of generalizations; graphical and tabular method, etc. Results: The components of the current account of the balance of payments of the Republic of Belarus for 2015–2021 are analyzed. The factors influencing the dynamics of exports of goods and services as well as primary and secondary incomes of Belarus for 2015–2021 are summarized. The impact of the pandemic and external sanctions in the short term on the dynamics of the country’s export–import flows is identified. Practical implication: The results of the study can be used to improve the system of analysis, assess the country’s balance of payments; identify the interdependence of the country’s foreign economic activity on extraordinary external factors, to forecast the ability of the internal reserves of the economy to withstand the impact of external shocks both in the short and long term. Conclusion and summary: The balance of payments of the Republic of Belarus is characterized mainly by a negative value for current transactions. The main reason is the negative balance of goods, which is partly reduced by active balance of services. The impact of external shocks in the form of border closures between countries and the existence of economic sanctions was offset in 2021 by a sharp recovery and the global economy growth. Growth in demand and prices in the world market for raw materials and food provided the country with a positive current account balance.

Lida Kouhalvandi, Osman Ceylan, Serdar Ozoguz et al.

In this study, we focus on automated optimization design methodologies to concurrently trade off between power gain, output power, efficiency, and linearity specifications in radio frequency (RF) high-power amplifiers (HPAs) through deep neural networks (DNNs). The RF HPAs are highly nonlinear circuits where characterizing an accurate and desired amplitude and phase responses to improve the overall performance is not a straightforward process. For this case, we propose a <i>coarse and fine modeling</i> approach based on firstly modeling the involved transistor and then selecting the best configuration of HAP along with optimizing the involved input and output termination networks through DNNs. In the <i>fine phase</i>, we firstly construct the equivalent modeling of the GaN HEMT transistor by using X-parameters. Then in the <i>coarse phase</i>, we utilize hidden layers of the modeled transistor and replace the HPA’s DNN to model the behavior of the selected HPA by using S-parameters. If the suitable accuracy of HPA modeling is not achieved, the hyperparameters of the fine model are improved and re-evaluated in the HPA model. We call the optimization process coarse and fine modeling since the evaluation process is performed from S-parameters to X-parameters. This stage of optimization can ensure modeling the nonlinear HPA design that includes a high number of parameters in an effective way. Furthermore, for accelerating the optimization process, we use the classification DNN for selecting the best topology of HPA for modeling the most suitable configuration at the coarse phase. The proposed modeling strategy results in relatively highly accurate HPA designs that generate post-layouts automatically, where multi-tone harmonic balance specifications are optimized once together without any human interruptions. To validate the modeling approach and optimization process, a 10 W HPA is simulated and measured in the operational frequency band of 1.8 GHz to 2.2 GHz, i.e., the L-band. The measurement results demonstrate a drain efficiency higher than 54% and linear gain performance more than 12.5 dB, with better than 50 dBc adjacent channel power ratio (ACPR) after DPD.

Sungyong Kim, Jinhyuk Yun

An economic system is an exemplar of a complex system in which all agents interact simultaneously. Interactions between countries have generally been studied using the flow of resources across diverse trade networks, in which the degree of dependence between two countries is typically measured based on the trade volume. However, indirect influences may not be immediately apparent. Herein, we compared a direct trade network to a trade network constructed using the personalized PageRank (PPR) encompassing indirect influences. By analyzing the correlation of the gross domestic product (GDP) between countries, we discovered that the PPR trade network has greater explanatory power on the propagation of economic events than direct trade by analyzing the GDP correlation between countries. To further validate our observations, an agent-based model of the spreading economic crisis was implemented for the Russia-Ukraine war of 2022. The model also demonstrates that the PPR explains the actual impact more effectively than the direct trade network. Our research highlights the significance of indirect and long-range relationships, which have often been overlooked

Rohit Lamba

This paper analyzes repeated version of the bilateral trade model where the independent payoff relevant private information of the buyer and the seller is correlated across time. Using this setup it makes the following five contributions. First, it derives necessary and sufficient conditions on the primitives of the model as to when efficiency can be attained under ex post budget balance and participation constraints. Second, in doing so, it introduces an intermediate notion of budget balance called interim budget balance that allows for the extension of liquidity but with participation constraints for the issuing authority interpreted here as an intermediary. Third, it pins down the class of all possible mechanisms that can implement the efficient allocation with and without an intermediary. Fourth, it provides a foundation for the role of an intermediary in a dynamic mechanism design model under informational constraints. And, fifth, it argues for a careful interpretation of the "folk proposition" that less information is better for efficiency in dynamic mechanisms under ex post budget balance and observability of transfers.