The application of plastic film mulching combined with drip irrigation can significantly alter the soil and water conditions for crop development. However, existing stomatal conductance models fail to adequately incorporate the effects of this practice on the physiological development of crops. This study employs three stomatal conductance models: Ball-Woodrow-Berry (BWB) model, Ball-Berry-Leuning (BBL) model, and Unified Stomatal Optimization (USO) model. This study introduces two model correction factors: the water response function (f(θ)) and the leaf-air temperature difference (∆T). These factors are utilized to simulate soybean stomatal conductance under various conditions, including plastic film mulching with drip irrigation, plastic film mulching without irrigation, drip irrigation without mulching, and control. The findings demonstrate that the USO model achieves superior performance, followed by the BBL and BWB models. Furthermore, the f(θ) correction factor outperforms the ∆T correction factor in enhancing model performance. The determination coefficients of the corrected BWB, BBL, and USO models increased by 15.2 %-102.2 %, 16.7 %-75.2 %, and 11.6 %-61.0 %, respectively. Meanwhile, the relative errors decreased by 7.5 %-43.2 %, 9.4 %-36.7 %, and 8.3 %-36.6 %, respectively. Additionally, the root mean square errors decreased by 8.2 %-27 %, 6.7 %-32.8 %, and 12.3 %-33.3 %. The corrected model exhibits strong reliability and universality across various soil water relative content and ∆T conditions, as evidenced by comparisons with the 95 % confidence intervals of observational data. The results of this study establish a theoretical foundation for the rational selection of stomatal conductance models in the northeast black soil region, thereby enhancing the simulation accuracy of water and carbon cycle processes under complex hydrothermal conditions.
Cap-and-trade regulation is a strategy to reduce carbon emissions (CEs). During production, CEs are reduced by green technology. In a dual-channel supply chain (DCSC), customers try a product at an offline store but purchase it online (showrooming effect). Additionally, using internet information services, some customers purchase offline (ropo effect). Due to demand uncertainty, neutrosophic fuzzy sets are used to appropriately express a parameter’s impreciseness. We develop a game-theoretic model where a manufacturer produces non-green and green products using carbon reduction technology, sells the products through a traditional retailer (offline), and owns an online channel for imprecise market demands. Customers free-ride from both the channels. The CE from transportation and the non-green products are considered. For carbon costs, a cap and trade policy is adopted. The neutrosophic fuzzy variables indicate the impreciseness of the demand, bidirectional free-riding, and product greenness. These variables determine channel members’ truth, indeterminacy, and falsity degrees. Different models with some prices (inconsistent and consistent) and service efforts as decision variables are analyzed using the Stackelberg game approach. After the derivation of the corresponding equilibrium equations, numerical experiments are presented to verify the validity of our conclusions. The findings show that although free-riding is detrimental to the retailer, it becomes advantageous if its direction is altered. The profit of the retailer with consistent prices is higher than the inconsistent one. Opposite outcomes are observed for the manufacturer. The channel members’ profits are more under the neutrosophic fuzzy environment than deterministic/fuzzy. Some managerial insights and conclusions are presented.
Marketing. Distribution of products, Management. Industrial management
Abstract South Africa joined BRICS with the aim of benefiting from enhanced trade with the grouping, which encompasses four of the largest economies in the world. This article undertook an empirical review to determine an answer to the following research question (RQ): whether South Africa’s exports to the original four BRIC/BRICs member countries had grown and diversified following its membership over the first fourteen-year timeframe (2010–2024)? Across these, decline was identified in the findings, demonstrating that South Africa’s participation in the group has performed below its potential and stated rationale. The article notes a growing trade deficit and lack of industrialised imports from South Africa, especially when compared with the EU and the US. This is shown to be mainly due to South Africa’s asymmetrical openness towards the BRICs, including having the single-lowest tariff rates towards the other four members at 4.9 to 5.3%, while the next lowest BRICs’ general tariff is at 10.3%. Against these findings, the article makes the case for a BRICS Plus treaty in order to eliminate any tariff and non-tariff barriers, as well as formulate realistic expectations and obligations for internal cohesion and external engagement based on credible commitment.
Political institutions and public administration - Asia (Asian studies only), Economic growth, development, planning
Mateusz Zajac, Tomislav Rožić, Justyna Swieboda-Kutera
et al.
<i>Background</i>: The increasing complexity of global supply chains and environmental expectations has highlighted the strategic importance of digital transformation in the transport, forwarding, and logistics (TFL) sector. Despite a growing portfolio of available tools, adoption rates—particularly among small and medium-sized enterprises (SMEs) in Central and Eastern Europe—remain low. This study investigates the barriers and motivations related to the implementation of digital planning tools supporting intermodal transport planning. <i>Methods</i>: A structured online survey was conducted among 80 Polish TFL enterprises, targeting decision-makers responsible for operational and digital strategies. The questionnaire included 17 closed and semi-open questions grouped into three thematic sections: tool usage, implementation barriers, and digital readiness. <i>Results</i>: The findings indicate that only 20% of respondents use dedicated route planning tools, and merely 10% report satisfaction with their performance. Key barriers include lack of awareness, organizational inertia, and the prioritization of other initiatives, with financial cost cited less frequently. While environmental sustainability is declared as a priority by most enterprises, digital support for emission tracking is limited. The results highlight the need for targeted education, integration support, and differentiated platform functionalities for SMEs and larger firms. <i>Conclusions</i>: This study offers evidence-based recommendations for developers, policymakers, and logistics managers aiming to accelerate digital adoption in the intermodal logistics landscape.
Transportation and communication, Management. Industrial management
Sofiane Bouaziz, Adel Hafiane, Raphael Canals
et al.
Urban heatwaves, droughts, and land degradation are pressing and growing challenges in the context of climate change. A valuable approach to studying them requires accurate spatio-temporal information on land surface conditions. One of the most important variables for assessing and understanding these phenomena is Land Surface Temperature (LST), which is derived from satellites and provides essential information about the thermal state of the Earth's surface. However, satellite platforms inherently face a trade-off between spatial and temporal resolutions. To bridge this gap, we propose FuseTen, a novel generative framework that produces daily LST observations at a fine 10 m spatial resolution by fusing spatio-temporal observations derived from Sentinel-2, Landsat 8, and Terra MODIS. FuseTen employs a generative architecture trained using an averaging-based supervision strategy grounded in physical principles. It incorporates attention and normalization modules within the fusion process and uses a PatchGAN discriminator to enforce realism. Experiments across multiple dates show that FuseTen outperforms linear baselines, with an average 32.06% improvement in quantitative metrics and 31.42% in visual fidelity. To the best of our knowledge, this is the first non-linear method to generate daily LST estimates at such fine spatial resolution.
Annalena Daniels, Sebastian Kerz, Salman Bari
et al.
Autonomous robotic grasping of uncertain objects in uncertain environments is an impactful open challenge for the industries of the future. One such industry is the recycling of Waste Electrical and Electronic Equipment (WEEE) materials, in which electric devices are disassembled and readied for the recovery of raw materials. Since devices may contain hazardous materials and their disassembly involves heavy manual labor, robotic disassembly is a promising venue. However, since devices may be damaged, dirty and unidentified, robotic disassembly is challenging since object models are unavailable or cannot be relied upon. This case study explores grasping strategies for industrial robotic disassembly of WEEE devices with uncertain vision data. We propose three grippers and appropriate tactile strategies for force-based manipulation that improves grasping robustness. For each proposed gripper, we develop corresponding strategies that can perform effectively in different grasping tasks and leverage the grippers design and unique strengths. Through experiments conducted in lab and factory settings for four different WEEE devices, we demonstrate how object uncertainty may be overcome by tactile sensing and compliant techniques, significantly increasing grasping success rates.
The Index of Dissimilarity (ID), widely utilized in economic literature as a measure of segregation, is inadequate for cross-country or time series studies due to its failure to account for structural variations across countries' labor markets or changes over time within a single country's labor market. Building on the works of Karmel and MacLachlan (1988) and Blackburn et al. (1993), we propose a new measure - the standardized ID - that isolates structural differences from true differences in segregation across space or time. A key advantage of our proposed measure lies in its ease of implementation and interpretation, even when working with datasets encompassing a large number of countries or time periods. Moreover, our measure can be consistently applied in the case of lumpy sectors or occupations that account for a large fraction of the workforce. We illustrate the new measure in an analysis of the cross-country relationship between economic development (as measured by GDP per capita) and occupational and sectoral gender segregation. Comparing the crude ID with the standardized ID, we show that the crude ID overestimates the positive correlation between income and segregation, especially between low- and middle-income countries. This suggests that analyses relying on the crude ID risk overestimating the importance of income differentials in explaining cross-country variation in gender segregation.
Claudia Liz García Aleaga, Arletis Cruz Llerena, Osney Pérez Ones
et al.
Introducción:
El aceite de fusel actualmente constituye un desecho que posee en su composición una amplia gama de alcoholes que lo posicionan como un producto versátil para diversos sectores industriales.
Objetivo:
Obtener productos de alto valor comercial a partir de la caracterización de muestras de aceite de fusel y la simulación de procesos en el Aspen Hysys v10.0.
Materiales y Métodos:
Se caracterizaron tres muestras de aceite de fusel de una destilería cubana mediante cromatografía de gases. Se realizó un análisis estadístico de distribución normal entre las composiciones de las muestras y una prueba de hipótesis a partir del Statgraphics Centurion XVII. Se modeló una tecnología de destilación reactiva mediante el simulador Aspen Hysys v10.0 y los resultados fueron validados con los datos reportados en la literatura.
Resultados y Discusión:
En las caracterizaciones realizadas para las tres muestras de aceite de fusel hay presencia de alcohol isoamílico como componente mayoritario. Se obtuvo que las composiciones de estas no presentan diferencias significativas entre ellas. Con la tecnología de destilación reactiva se obtiene una mezcla de ésteres compuesta por 65,66 % vol. de acetato de isoamilo, 11,05 % vol. de acetato de isobutilo y en el destilado 40,16 % vol. de acetato de etilo. Se consumen 14 040 m3/año de agua de enfriamiento, 82 280 kg/año de vapor y 5 328 kg/año de fuel oil.
Conclusiones:
Con la tecnología de destilación reactiva de aceite de fusel se obtiene más del 45% de productos de gran valor agregado para la industria del bioetanol.
María Isabel Díaz-Molina, Luis Andrés Gómez Rodríguez, Zenaida Rodríguez-Negrín
Introducción:
El balance de materiales en la obtención del producto intermedio G-0 garantiza que el producto sea consistentemente producido y controlado.
Objetivo:
Realizar los balances de materiales en las operaciones mecánicas y térmicas en la obtención del producto intermedio G-0.
Materiales y Métodos:
Se realizó el análisis de la masa de materiales de entrada y salida para los sistemas centrifugación de G-0 crudo, filtración de la suspensión a purificar de G-0 crudo, centrifugación y secado del PI G-0 y se determinó la composición mediante las técnicas disponibles y trabajos de investigación realizados con anterioridad. Para el cálculo de los resultados promedios de las corrientes con el programa Microsoft Office Excel® se tuvieron en cuenta 25 lotes producidos en los años 2016, 2017, 2018 y 2019.
Resultados y Discusión:
Los balances de materiales por lote en la obtención del PI G-0 en los sistemas Centrifugación G-0 Crudo, Centrifugación del PI G-0, Filtración y Secado, permitieron conocer el rendimiento real del producto deseado para 14 mole de furfural y las pérdidas en las corrientes residuales y por manufactura. En la operación de secado se cuantificó la eliminación de agua y solvente base húmeda y seca.
Conclusiones:
Los balances de materiales en las operaciones mecánicas y térmicas del proceso de obtención del producto intermedio G-0 permiten asegurar la consistencia del proceso. Los ensayos realizados a las especificaciones: características organolépticas, identificación por ultrafotometría UV VIS, intervalo de fusión y pureza del producto declaran la conformidad para su uso en la producción de la Furvina.
By employing the GMM panel VAR framework, we examine the interplay among natural resource rents, technological innovation, financial development, and energy consumption in the BRICS from 1990 to 2020 on an annual basis. The findings of the study demonstrate a significant negative association between natural resources and technical innovation, as well as a negative relationship with financial development. While the notion of the natural resource curse is deemed invalid, the present study asserts that natural resources do indeed cause financial development. There is an insignificantly positive relationship between natural resources and energy use. There exists a significant negative association between financial development and technical innovation, while a positive association is shown between financial development and energy use. Primary energy consumption is negative (positive) and statistically significantly associated with natural resources (financial development), although that link is simply negative in the case of technological innovation. Technological innovation is positive and significantly related to variables (natural resources and energy consumption), while the link is insignificantly positive to financial development. The results of the causality test reveal a bidirectional relationship between energy consumption and technological innovation, with all variables showing a significant influence on each parameter. There exists a unidirectional causal relationship wherein natural resources influence financial development, natural resources influence technological innovation, and financial development influences technological innovation. Moreover, there is a unidirectional correlation that may be observed from energy use towards natural resources, financial progress, and technological innovation. The findings from the impulse response function indicate that there is a substantial increase in the proportion of each variable that can be explained by other parameters as we transition from the short-term to the long-term. The implications of the study findings are also presented.
Iacob Postavaru, Emilia Bunea, Crina Pungulescu
et al.
This paper explores the potential of large language models to enhance economics education through computational humor. We employ OpenAI’s GPT-4 model to infuse humor into summaries of three Nobel laureates’ contributions to economics and conduct a small empirical exercise with undergraduate students to test the pedagogical efficacy of computational humor. The results suggest that computer-generated humor may be an effective learning aid: the results of the students who rate the humorous versions of the instructional texts as genuinely funny are significantly better than the results of their peers who are not amused. Encouragingly for teachers who try to be funny but fail, we do not find evidence that ineffectual humor is detrimental to learning.
Agustin G. Bonifacio, Nadia Guiñazu, Noelia Juarez
et al.
We study a one-to-one labor matching market. If a worker considers resigning from her current job to obtain a better one, how long does it take for this worker to actually get it? We present an algorithm that models this situation as a re-stabilization process involving a vacancy chain. Each step of the algorithm is a link of such a chain. We show that the length of this vacancy chain, which can be interpreted as the time the worker has to wait for her new job, is intimately connected with the lattice structure of the set of stable matchings of the market. Namely, this length can be computed by considering the cardinalities of cycles in preferences derived from the initial and final stable matchings involved.
This paper presents the preliminary design of the descent and landing trajectory of the ESA Argonaut lunar lander. The mission scenario and driving system constraints are presented and accounted for in the design of a fuel-optimal trajectory that includes divert capabilities, as required to achieve a safe landing. A sub-optimal descent and landing trajectory is then presented and computed from the optimal one, and the related on-board guidance algorithms are derived. The proposed end-to-end guidance solution represents an easily implementable alternative to on-board optimization, minimizing the verification & validation effort, computational footprint, and programmatic risk in the development of the related GN&C capabilities. A dedicated off-line optimization process is also outlined, and exploited to optimize the propellant consumption of the sub-optimal trajectory and to ensure the fulfillment of system constraints despite the use of simple algorithms on-board. The sub-optimal trajectory is compared to the optimal baseline, and conclusions are drawn on the applicability of the proposed approach to the Argonaut mission.
Maksim Papenkov, Chris Meredith, Claire Noel
et al.
Accurate industry classification is a critical tool for many asset management applications. While the current industry gold-standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry model, in which every firm is assigned to exactly one group - regardless of how diversified that firm may be. This approach breaks down for large conglomerates like Amazon, which have risk exposure spread out across multiple sectors. We attempt to overcome these limitations by developing MIS (Multi-Industry Simplex), a probabilistic model that can flexibly assign a firm to as many industries as can be supported by the data. In particular, we utilize topic modeling, an natural language processing approach that utilizes business descriptions to extract and identify corresponding industries. Each identified industry comes with a relevance probability, allowing for high interpretability and easy auditing, circumventing the black-box nature of alternative machine learning approaches. We describe this model in detail and provide two use-cases that are relevant to asset management - thematic portfolios and nearest neighbor identification. While our approach has limitations of its own, we demonstrate the viability of probabilistic industry classification and hope to inspire future research in this field.
Jana Kolassa, Manisha Ganeshan, Erica McGrath-Spangler
et al.
Soil moisture conditions can influence the evolution of a tropical cyclone (TC) that is partially or completely over land. Hence, better constraining soil moisture initial conditions in a numerical weather prediction model can potentially improve predictions of TC evolution near or over land. This study examines the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). Two sets of retrospective forecasts of TC Idai are compared in an Observing System Experiment framework: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis and (ii) forecasts initialized from an analysis that additionally assimilates SMAP brightness temperature observations over land. Results indicate that SMAP assimilation leads to pronounced improvements in the representation of TC Idai structure and prediction of its intensity and track. The wind speed radius (a measure for TC compactness) is reduced by up to 18% in the analysis with SMAP assimilation relative to the control experiment without SMAP assimilation. The forecast intensity error, measured against the observed intensity, is reduced by up to 23%. The forecast along-track error is reduced by up to 34%, indicating a more accurate propagation speed, while the impact of SMAP assimilation on the forecast cross-track error is neutral. These results provide a valuable demonstration that SMAP assimilation can have a highly beneficial impact on TC prediction in global weather forecast models.
O objetivo deste artigo consiste em reconstituir o processo de revitalização sindical, ocorrido entre 2012 e 2017, devido à ampliação intensa e veloz da categoria de metalúrgicos no Polo Naval e Offshore de Rio Grande. O referido processo foi marcado por intensas lutas entre os trabalhadores e o sindicato, devido à existência anterior de um ativismo social por parte dos trabalhadores. A metodologia utilizada consistiu na realização de entrevistas com dirigentes sindicais, trabalhadores, a consulta ao material disponibilizado pelo sindicato e revisão bibliográfica atinente ao recorte proposto. Através da abordagem teórica da construção de classe enquanto um “fazer-se” na experiência compartilhada, seguimos os principais momentos da revitalização sindical e os conflitos com a sua base, que obliteravam, em parte a sua representação legítima em torno do sindicato institucionalizado. Depreendemos que a consciência de classe, nos termos aqui tratados, conciliou-se com o sindicalismo de movimento já no começo da crise do Polo, mesmo assim esse momento foi importante para a legitimação de pautas em torno do desemprego. Contudo, diante do desmonte da indústria naval, o sindicato viu sua base erodir e, a partir de então, tem atuado com empresas de pequeno porte, as quais garantem um patamar mínimo de experiência adquirida de organização sindical para futuros ou prováveis empreendimentos no município.
Special aspects of education, Labor. Work. Working class
Labor economists regularly analyze employment data by fitting predictive models to small, carefully constructed longitudinal survey datasets. Although machine learning methods offer promise for such problems, these survey datasets are too small to take advantage of them. In recent years large datasets of online resumes have also become available, providing data about the career trajectories of millions of individuals. However, standard econometric models cannot take advantage of their scale or incorporate them into the analysis of survey data. To this end we develop CAREER, a foundation model for job sequences. CAREER is first fit to large, passively-collected resume data and then fine-tuned to smaller, better-curated datasets for economic inferences. We fit CAREER to a dataset of 24 million job sequences from resumes, and adjust it on small longitudinal survey datasets. We find that CAREER forms accurate predictions of job sequences, outperforming econometric baselines on three widely-used economics datasets. We further find that CAREER can be used to form good predictions of other downstream variables. For example, incorporating CAREER into a wage model provides better predictions than the econometric models currently in use.