Hasil untuk "Trade associations"

M. Flannery, C. James

S. Díaz, M. Cabido

R. Bénabou

Louis K. C. Chan, Josef Lakonishok

P. Segerstrom, T. Anant, E. Dinopoulos

P. Bolton, Ernst-Ludwig von Thadden

B. Barber, T. Odean

Vasileios Greveniotis, Elisavet Bouloumpasi, Adriana Skendi et al.

Cotton (<i>Gossypium</i> spp.) seeds are a valuable source of protein, oil, and minerals; however, seed-quality traits have received less attention than fiber traits, particularly in partially interspecific germplasm. This study evaluated the performance and stability of five cottonseed quality traits (1000-seed weight, crude protein, oil, ash, and crude fiber) in four partially interspecific Pa7 cotton lines (<i>G. hirsutum</i> × <i>G. barbadense</i>) and one commercial cultivar, grown under three irrigation levels and two nitrogen fertilization regimes across two Mediterranean growing seasons in Northern Greece. A strip–split plot factorial design with three replications was used, and year × irrigation combinations were treated as six distinct environments. Trait responses were analyzed using multi-way ANOVA, stability metrics (stability index and coefficient of variation), correlation analysis, principal component analysis (PCA), and genotype × environment interaction models (AMMI and GGE biplots). Multi-way ANOVA revealed significant effects of genotype, environment, and management practices, as well as their interactions, indicating complex regulation of cottonseed composition. Genotypic effects were significant for all traits, while environmental effects were particularly strong for protein content. The greater environmental sensitivity of protein content highlights the key role of nitrogen-related processes and indicates that optimized fertilization can partially offset environmentally induced variability in seed protein accumulation. Stability analysis showed that storage-related traits (protein, oil, ash, and crude fiber) were generally more stable across environments than 1000-seed weight. Among the genotypes, M4 consistently combined high trait performance with broad stability across environments, whereas M1 exhibited the greatest stability for 1000-seed weight. Multivariate and GEI analyses complemented univariate results by revealing trait associations, physiological trade-offs, and crossover responses among genotypes. Overall, using both stability indices and multivariate analyses enabled a detailed evaluation of cottonseed quality in partially interspecific material, supporting the identification of suitable genotypes and optimization of management practices under varying Mediterranean conditions.

Francis F. Kilkenny, Jeffrey E. Ott, Elizabeth A. Leger et al.

ABSTRACT Information on climate‐associated phenotypic variation is essential for sourcing seed that matches restoration site conditions. Spatially explicit seed transfer models can effectively deliver this information. However, standard modeling approaches often do not provide flexibility for practical considerations and may not capture highly complex trait‐climate associations. We characterized climate‐associated variation in growth, reproduction, morphology, phenology, and survival across 98 source populations at 3 common gardens for the grass Elymus elymoides (bottlebrush squirreltail), an important restoration species in the Intermountain Region of the western USA. We developed fixed‐boundary seed zones and focal‐point seed transfer models using non‐standard methods (regression trees and random forests). In general, source populations with larger plant sizes and later flowering originated from cooler and wetter or milder climates than those with smaller sizes and earlier flowering, though some associations were more complex. Populations from milder climates also had higher trait plasticity than populations from other climates, except for plasticity in seed maturation, which was highest in populations from warmer and drier climates. Seed zones identified through our approach consisted of three major zones with 2–7 subzones each (13 seed zones in total). Two subspecies groups had distinct trait‐climate associations, and separate seed zone models were developed for each. Our modeling approach provides a hierarchical structure that partitions predictor variables based on their importance. This doubles as a prioritization framework that assists in navigating trade‐offs between risk avoidance and practical constraints by explicitly defining how zones can be combined or subdivided in response to user needs. Our approach also captures trait‐climate association nuances missed by standard approaches, increasing the precision of our focal‐point seed transfer zones. Our findings emphasize the multifaceted nature of trait‐climate associations and highlight the importance of seed transfer modeling to seed‐sourcing decisions in a time of global change.

Leyla Roksan Caglar, Pedro A. M. Mediano, Baihan Lin

Generalization to novel visual conditions remains a central challenge for both human and machine vision, yet standard robustness metrics offer limited insight into how systems trade accuracy for robustness. We introduce a rate-distortion-theoretic framework that treats stimulus-response behavior as an effective communication channel, derives rate-distortion (RD) frontiers from confusion matrices, and summarizes each system with two interpretable geometric signatures - slope ($β$) and curvature ($κ$) - which capture the marginal cost and abruptness of accuracy-robustness trade-offs. Applying this framework to human psychophysics and 18 deep vision models under controlled image perturbations, we compare generalization geometry across model architectures and training regimes. We find that both biological and artificial systems follow a common lossy-compression principle but occupy systematically different regions of RD space. In particular, humans exhibit smoother, more flexible trade-offs, whereas modern deep networks operate in steeper and more brittle regimes even at matched accuracy. Across training regimes, robustness training induces systematic but dissociable shifts in beta/kappa, revealing cases where improved robustness or accuracy does not translate into more human-like generalization geometry. These results demonstrate that RD geometry provides a compact, model-agnostic lens for comparing generalization behavior across systems beyond standard accuracy-based metrics.

Nadir Sella, Florent Guinot, Nikita Lagrange et al.

Abstract Generating synthetic data from medical records is a complex task intensified by patient privacy concerns. In recent years, multiple approaches have been reported for the generation of synthetic data, however, limited attention was given to jointly evaluate the quality and the privacy of the generated data. The quality and privacy of synthetic data stem from multivariate associations across variables, which cannot be assessed by comparing univariate distributions with the original data. Here, we introduce a novel algorithm (MIIC-SDG) for generating synthetic data from electronic records based on a multivariate information framework and Bayesian network theory. We also propose a new metric to quantitatively assess the trade-off between the Quality and Privacy Scores (QPS) of synthetic data generation methods. The performance of MIIC-SDG is demonstrated on different clinical datasets and favorably compares with state-of-the-art synthetic data generation methods, based on the QPS trade-off between several quality and privacy metrics.

Maria do Rosário Cameira, Antónia Ferreira, Luis Boteta et al.

The HubIS project, through a participatory approach with stakeholders, identified the need to assess the hydraulic and energy performance of the Lucefecit Collective Irrigation System (LCIS) for co-designed future scenarios. With this aim, a tool was developed integrating on-farm demand driven water transport and distribution with energy use. The tool results from a participatory modelling process based on the SIGOPRAM software parameterized and tested for the LCIS current conditions (2022) using a comprehensive data set. The current scenario, in which only 48 % of the command area is irrigated, is characterized by good hydraulic performance, although pressure surpluses were detected in most outlets. Co-designed scenarios anticipate an increase in the irrigated area and a shift towards more water-intensive crops, which could result in up to a 163 % in the peak demand flow compared to the current situation. Even in the most demanding scenario, only a few irrigation outlets experience pressure deficits, accounting for 10 % of the irrigable area. Collaborative discussions with stakeholders resulted in a trade-off strategy between hydraulic reliability and energy efficiency. The tool provides the water users associations with an important basis for decision-making supported by system performance assessment, to ensure sustainability in water and energy use while taking in account future climate and agricultural changes.

William Manga Mokofe

This article explored the dynamics of the voice of non-union workers within South Africa’s informal economy, a vital but institutionally marginalised sector. It investigated how informal workers, excluded from formal structures such as trade unions and bargaining councils, create alternative forms of collective representation, resistance, and negotiation. Using a qualitative, interdisciplinary approach that blends labour studies, socio-legal analysis, and grounded case studies, the research uncovered diverse mechanisms of worker voice, including informal associations, faith-based initiatives, digital platforms, and NGO-led advocacy. The findings revealed that while these forms of expression demonstrate adaptability and contextual sensitivity, they are hampered by legal invisibility, weak institutional support, and fragmented structures. Despite these challenges, they highlight innovative pathways through which labour agencies can thrive under conditions of informality and state disengagement. The article contributes to debates on inclusive labour governance by proposing a hybrid model that moves beyond union-centric paradigms. It calls for the formal recognition of alternative voice mechanisms, the integration of grassroots organising into labour policy, and broader multi-stakeholder support. These recommendations seek to foster a more equitable and representative industrial relations framework that aligns with the lived realities of informal workers in South Africa and comparable global contexts.

Pritish Yuvraj, Siva Devarakonda

Accurate classification of products under the Harmonized Tariff Schedule (HTS) is a critical bottleneck in global trade, yet it has received little attention from the machine learning community. Misclassification can halt shipments entirely, with major postal operators suspending deliveries to the U.S. due to incomplete customs documentation. We introduce the first benchmark for HTS code classification, derived from the U.S. Customs Rulings Online Search System (CROSS). Evaluating leading LLMs, we find that our fine-tuned Atlas model (LLaMA-3.3-70B) achieves 40 percent fully correct 10-digit classifications and 57.5 percent correct 6-digit classifications, improvements of 15 points over GPT-5-Thinking and 27.5 points over Gemini-2.5-Pro-Thinking. Beyond accuracy, Atlas is roughly five times cheaper than GPT-5-Thinking and eight times cheaper than Gemini-2.5-Pro-Thinking, and can be self-hosted to guarantee data privacy in high-stakes trade and compliance workflows. While Atlas sets a strong baseline, the benchmark remains highly challenging, with only 40 percent 10-digit accuracy. By releasing both dataset and model, we aim to position HTS classification as a new community benchmark task and invite future work in retrieval, reasoning, and alignment.

Ailiya Borjigin, Cong He

We present a cross-market algorithmic trading system that balances execution quality with rigorous compliance enforcement. The architecture comprises a high-level planner, a reinforcement learning execution agent, and an independent compliance agent. We formulate trade execution as a constrained Markov decision process with hard constraints on participation limits, price bands, and self-trading avoidance. The execution agent is trained with proximal policy optimization, while a runtime action-shield projects any unsafe action into a feasible set. To support auditability without exposing proprietary signals, we add a zero-knowledge compliance audit layer that produces cryptographic proofs that all actions satisfied the constraints. We evaluate in a multi-venue, ABIDES-based simulator and compare against standard baselines (e.g., TWAP, VWAP). The learned policy reduces implementation shortfall and variance while exhibiting no observed constraint violations across stress scenarios including elevated latency, partial fills, compliance module toggling, and varying constraint limits. We report effects at the 95% confidence level using paired t-tests and examine tail risk via CVaR. We situate the work at the intersection of optimal execution, safe reinforcement learning, regulatory technology, and verifiable AI, and discuss ethical considerations, limitations (e.g., modeling assumptions and computational overhead), and paths to real-world deployment.

Wenhao Guo, Yuda Wang, Zeqiao Huang et al.

In the complex landscape of traditional futures trading, where vast data and variables like real-time Limit Order Books (LOB) complicate price predictions, we introduce the FutureQuant Transformer model, leveraging attention mechanisms to navigate these challenges. Unlike conventional models focused on point predictions, the FutureQuant model excels in forecasting the range and volatility of future prices, thus offering richer insights for trading strategies. Its ability to parse and learn from intricate market patterns allows for enhanced decision-making, significantly improving risk management and achieving a notable average gain of 0.1193% per 30-minute trade over state-of-the-art models with a simple algorithm using factors such as RSI, ATR, and Bollinger Bands. This innovation marks a substantial leap forward in predictive analytics within the volatile domain of futures trading.

Alina Brad, Tobias Haas, Etienne Schneider

Net zero targets have rapidly become the guiding principle of climate policy, implying the use of carbon dioxide removal (CDR) to compensate for residual emissions. At the same time, the extent of (future) residual emissions and their distribution between economic sectors and activities has so far received little attention from a social science perspective. This constitutes a research gap as the distribution of residual emissions and corresponding amounts of required CDR is likely to become highly contested in the political economy of low-carbon transformation. Here, we investigate what function CDR performs from the perspective of sectors considered to account for a large proportion of future residual emissions (cement, steel, chemicals, and aviation) as well as the oil and gas industry in the EU. We also explore whether they claim residual emissions to be compensated for outside of the sector, whether they quantify these claims and how they justify them. Relying on interpretative and qualitative analysis, we use decarbonization or net zero roadmaps published by the major sector-level European trade associations as well as their statements and public consultation submissions in reaction to policy initiatives by the EU to mobilize CDR. Our findings indicate that while CDR technologies perform an important abstract function for reaching net zero in the roadmaps, the extent of residual emissions and responsibilities for delivering corresponding levels of negative emissions remain largely unspecified. This risks eliding pending distributional conflicts over residual emissions which may intersect with conflicts over diverging technological transition pathways advocated by the associations.

Camilla Silva Geraldello

Abstract The 21st century provided opportunities for Brazilian agribusiness with changes in the state structure that enabled the participation of the business sector in the formulation of foreign trade policy. The objective of this article is to analyze the strategies adopted by interest groups of citrus processing industries to influence the decision-making in foreign trade policy in favor of to the segment between 2001 and 2018. The political investment portfolio of the interest representation associations of the citrus industries was assessed in three ministries with competencies in foreign trade policy - MAPA, MDIC, and MRE, which were frequently used by the sector.

Mushoni Bulagi, Tshepo Maxwell Lebepe, Jan Johannes Hlongwane

The purpose of the study was to analyse the determinants of South African citrus exports post era of trade liberalisations using secondary data from 1996 to 2018. The Johansen Cointegration model was used to test the long-run relationship between the citrus export and the determinants in the post era of trade liberalisation and Ordinary Least Squares regression was used to determine the relationship between citrus export in South Africa and the selected determinants post the era of trade liberalisation. The results of the Johansen Cointegration model show the existence of a long-run equilibrium relationship between citrus exports and the determinants of South African citrus exports. The Ordinary Least Squares regression results provided evidence that citrus production and citrus world market prices are the major influencers of citrus export in South Africa. The Department of Agriculture and the Citrus Associations should make initiatives to ensure an increase in citrus production by promoting development programmes for the citrus producers.

Yuanxi Wu, Zhi Wu, Wei Gu et al.

With the sustained attention on carbon neutrality, the personal carbon trading (PCT) scheme has been embraced as an auspicious paradigm for scaling down carbon emissions. To facilitate the simultaneous clearance of energy and carbon allowance inside the energy community while hedging against uncertainty, a joint trading framework is proposed in this article. The energy trading is implemented in a peer-to-peer (P2P) manner without the intervention of a central operator, and the uncertainty trading is materialized through procuring reserve of conventional generators and flexibility of users. Under the PCT scheme, carbon allowance is transacted via a sharing mechanism. Possible excessive carbon emissions due to uncertainty balance are tackled by obliging renewable agents to procure sufficient carbon allowances, following the consumption responsibility principle. A two-stage iterative method consisting of tightening McCormick envelope and alternating direction method of multipliers (ADMM) is devised to transform the model into a mixed-integer second-order cone program (MISOCP) and to allow for a fully decentralized market-clearing procedure. Numerical results have validated the effectiveness of the proposed market model.