Solange de Cassia Inforzato de Souza, Lucas Eduardo Martins, Magno Rogério Gomes
Este artigo tem como objetivo investigar as repercussões salariais da orientação sexual homoafetiva e minorias múltiplas de gênero e cor no Brasil. Para isso, foram utilizados os microdados do Censo Demográfico de 2010 e efetuadas as decomposições de Oaxaca (1973) e Blinder (1973) e as decomposições quantílicas de Koenker e Basset (1978). Os resultados mostram que, em geral, os trabalhadores autodeclarados homossexuais são mais jovens, escolarizados, formalizados e urbanos, trabalham no comércio e serviços e em ocupações qualificadas, e de mais altos salários, comparados aos heterossexuais. As desigualdades salariais por orientação sexual são explicadas, em sua maior parte, pelas características produtivas dos trabalhadores, mais favoráveis aos gays brancos, e pela discriminação positiva para homossexuais, melhor para lésbicas brancas. A cor não branca incrementa o benefício decorrente da homossexualidade para homens e reduz para mulheres. Maiores são os ganhos pela discriminação salarial positiva para o homossexual nas faixas salariais mais elevadas, no entanto, ao interseccionar as minorias, sexo feminino, cor de pele não branca e homossexualidade, na comparação com o homem branco heterossexual, confirma-se a discriminação salarial negativa para as lésbicas não brancas, que se agrava nos maiores quantis da distribuição salarial no Brasil.
Economic growth, development, planning, Economics as a science
The development of human capital in the Eurasian area is the basis for effective cooperation between states. Aim. To define the role of human capital in the framework of the Greater Eurasian Partnership concept and its impact on integration processes in the Eurasian space, as well as to assess the significance of human capital development for sustainable economic growth and social progress of member states and potential partners of the Eurasian Economic Union. Methods. A combination of qualitative and quantitative analysis of Human Development Index statistics, comparative analysis of interpersonal trust, and research on long-term economic planning and deferred benefits. Results. In the countries mentioned in the research, there are considerable differences in human development index indicators, in the level of trust between different social groups, which affects economic integration. The development of human capital in the Eurasian space is the basis for effective cooperation between states. Conclusions. For the successful implementation of the concept of the Greater Eurasian Partnership, it is necessary to develop human capital in a comprehensive way, including improvement of access to education, health care and creation of conditions for free movement of labor. The level of trust between states and their citizens plays a key role in the integration process, facilitating successful cooperation and reducing barriers to joint projects. The Eurasian Economic Union countries should focus on socio-economic initiatives aimed at increasing the level of trust and enhancing economic activity in order to achieve sustainable growth.
Giuseppe Silano, Daniel Bonilla Licea, Hajar El Hammouti
et al.
A new class of Multi-Rotor Aerial Vehicles (MRAVs), known as omnidirectional MRAVs (o-MRAVs), has gained attention for their ability to independently control 3D position and orientation. This capability enhances robust planning and control in aerial communication networks, enabling more adaptive trajectory planning and precise antenna alignment without additional mechanical components. These features are particularly valuable in uncertain environments, where disturbances such as wind and interference affect communication stability. This paper examines o-MRAVs in the context of robust aerial network planning, comparing them with the more common under-actuated MRAVs (u-MRAVs). Key applications, including physical layer security, optical communications, and network densification, are highlighted, demonstrating the potential of o-MRAVs to improve reliability and efficiency in dynamic communication scenarios.
Valerio De Stefano, Maddalena Mula, Manuel Sebastian Mariani
et al.
A rich theoretical and empirical literature investigated the link between export diversification and firm performance. Prior theoretical works hinted at the key role of capability accumulation in shaping production activities and performance, without however producing product-level indicators able to forecast corporate growth. Building on economic complexity theory and the corporate growth literature, this paper examines which characteristics of a firm's export basket predict future performance. We analyze a unique longitudinal dataset that covers export and financial data for 12,852 Italian firms. We find that firms exporting products typically exported by wealthier countries -- a proxy for greater product sophistication and market value -- tend to experience higher growth and profit per employee. Moreover, we find that diversification outside of a firm's core production area is positively associated with future growth, whereas diversification within the core is negatively associated. This is revealed by introducing novel measures of in-block and out-of-block diversification, based on algorithmically-detected production blocks. Our findings suggest that growth is driven not just by how many products a firm exports, but also by where these products lie within the production ecosystem, at both local and global scales.
We present a framework for uncovering and exploiting dependencies among tools and documents to enhance exemplar artifact generation. Our method begins by constructing a tool knowledge graph from tool schemas,including descriptions, arguments, and output payloads, using a DeepResearch-inspired analysis. In parallel, we derive a complementary knowledge graph from internal documents and SOPs, which is then fused with the tool graph. To generate exemplar plans, we adopt a deep-sparse integration strategy that aligns structural tool dependencies with procedural knowledge. Experiments demonstrate that this unified framework effectively models tool interactions and improves plan generation, underscoring the benefits of linking tool graphs with domain knowledge graphs for tool-augmented reasoning and planning.
The goal of this study is to propose a new concept, Sustainable Economic Value, to define it logically, and to build a simplified model for its evaluation.
Daniel M. Cherenson, Devansh R. Agrawal, Dimitra Panagou
Mission planning can often be formulated as a constrained control problem under multiple path constraints (i.e., safety constraints) and budget constraints (i.e., resource expenditure constraints). In a priori unknown environments, verifying that an offline solution will satisfy the constraints for all time can be difficult, if not impossible. We present ReRoot, a novel sampling-based framework that enforces safety and budget constraints for nonlinear systems in unknown environments. The main idea is that ReRoot grows multiple reverse RRT* trees online, starting from renewal sets, i.e., sets where the budget constraints are renewed. The dynamically feasible backup trajectories guarantee safety and reduce resource expenditure, which provides a principled backup policy when integrated into the gatekeeper safety verification architecture. We demonstrate our approach in simulation with a fixed-wing UAV in a GNSS-denied environment with a budget constraint on localization error that can be renewed at visual landmarks.
In the context of global efforts to address climate change, research into regional carbon neutrality strategies has become especially critical. For developing countries and regions, it is essential to scientifically and rationally assessing the paths for small-scale regional transformations under carbon neutrality imperatives to effectively implement low-carbon transition measures. This study utilizes Chongming District in Shanghai of China as a case to establish a framework for forecasting carbon emission and sink from a multi-dimensional natural-social perspective. This facilitates the simulation and optimization of pathways for carbon neutrality transformation. The results indicate: (1) From 2000 to 2020, the total regional carbon emission exhibited a rising trend, while the total carbon sink initially declined then increased, indicating potential enhancement zone with significant potential and space for carbon neutrality development. (2) Enhanced management of ecological spaces and land use planning result in notable increases in carbon sink. Strategic measures such as emission and consumption reductions, alongside energy transitions, effectively controlled carbon emission growth and facilitated comprehensive decarbonization. (3) By combining ecological priority with enhanced control and balanced development with enhanced control, the region can achieve carbon neutrality. This showcases the effective role of policy regulation in facilitating high-quality carbon–neutral transformations. (4) Effective ecosystem management along with robust reduction and transition strategies enable county-level carbon–neutral transformations, offering a model and methodological support for other developing regions facing the twin challenges of economic growth and environmental sustainability.
River, lake, and water-supply engineering (General), Water supply for domestic and industrial purposes
We explore the usage of large language models (LLM) in human-in-the-loop human-in-the-plant cyber-physical systems (CPS) to translate a high-level prompt into a personalized plan of actions, and subsequently convert that plan into a grounded inference of sequential decision-making automated by a real-world CPS controller to achieve a control goal. We show that it is relatively straightforward to contextualize an LLM so it can generate domain-specific plans. However, these plans may be infeasible for the physical system to execute or the plan may be unsafe for human users. To address this, we propose CPS-LLM, an LLM retrained using an instruction tuning framework, which ensures that generated plans not only align with the physical system dynamics of the CPS but are also safe for human users. The CPS-LLM consists of two innovative components: a) a liquid time constant neural network-based physical dynamics coefficient estimator that can derive coefficients of dynamical models with some unmeasured state variables; b) the model coefficients are then used to train an LLM with prompts embodied with traces from the dynamical system and the corresponding model coefficients. We show that when the CPS-LLM is integrated with a contextualized chatbot such as BARD it can generate feasible and safe plans to manage external events such as meals for automated insulin delivery systems used by Type 1 Diabetes subjects.
In the face of a substantial and uncertain growth of behind-the-meter Distributed Energy Resources (DERs), utilities and regulators are currently in the search for new network planning strategies for facilitating an efficient Transmission & Distribution (T&D) coordination. In this context, here we propose a novel distribution system planning methodology to facilitate coordinated planning exercises with transmission system planners through the management of long-term DER growth uncertainty and its impact on the substation netload. The proposed approach is based on the design of a transmission-aware distribution planning model embedding DER growth uncertainty, which is used to determine a "menu" of secure distribution network upgrade options with different associated costs and peak netload guarantees observed from the transmission-side, referred here as Netload Range Cost Curves (NRCCs). NRCCs can provide a practical approach for coordinating T&D planning exercises, as these curves can be integrated into existing transmission planning workflows, and specify a direct incentive for distribution planners to evaluate peak netload reduction alternatives in their planning process. We perform computational experiments based on a realistic distribution network that demonstrate the benefits and applicability of our proposed planning approach.
سیاست خرید تضمینی بعنوان یک سیاست حمایتی دولت که از سال 1368 در ایران برای برخی از محصولات به اجرا درآمده است با مسائل و مشکلاتی در اجرای این سیاست طی سالیان متمادی مواجه بوده است که لزوم بررسی و ارزیابی عملکرد سازمان تعاون روستایی را بهعنوان مجری این سیاست ضروری میسازد. لذا هدف این تحقیق شناسایی مسائل و مشکلات قانون خرید تضمینی گندم در استان فارس در سال 1396 است. برای رسیدن به این هدف از روش تکمیل پرسشنامه و مدل لاجیت ترتیبی استفاده شد. طبق نتایج بدست آمده، متغیرهای رضایت از شغل کشاورزی، سرعت پرداخت، برخورد مناسب کارکنان و مسئولین، کیفیت نهادههای در دسترس، نحوه سنجش پاکی و درصد افت محصول و در نظر گرفتن کیفیت محصول تولید شده دارای ضریب مثبت و معنیدار هستند. بنابراین، افزایش هر یک از این متغیرها باعث افزایش رضایت از طرح خرید تضمینی خواهد شد. با توجه به این که متغیر کیفیت نهادههای دردسترس معنیدار شده است لذا پیشنهاد میشود سازمانهای مربوطه بر نهادههای مورد استفاده برای تولید محصولات مورد نظر نظارت داشته باشند. که نهادهها از سطح کیفیت بالایی برای تولید محصول مرغوبتر برخوردار باشد. همچنین دولت شرایط و قوانینی تعیین کند که طرح خرید تضمینی شامل محصولات با کیفیت بالا خواهد بود. این امر میتواند انگیزه کشاورزان را به تولید محصولات با کیفیت بالا ترغیب نماید. از طرفی کشاورزانی که دارای محصولات با کیفیتتری هستند از سوی دولت تشویق شوند تا الگویی برای سایر کشاورزان باشند.
We present a dynamic physico-economic model of Earth orbit use with endogenous satellite collision risk to study conditions under which debris-producing collisions between orbiting bodies result in debris growth that may render Earth's orbits unusable, an outcome known as Kessler Syndrome. We characterize the dynamics of objects in orbit under open access as well as when external costs -- the impact of an additional satellite launch on the collision risk faced by all satellites -- are internalized, and we show that Kessler Syndrome can emerge in both cases. Finally, we show that once the economic incentives of satellite launching are modeled, for Kessler Syndrome to emerge, autocatalytic debris growth is essential. In our main calibration, Kessler Syndrome can emerge anytime between the year 2040 and the year 2184, with the precise date being very sensitive to the calibration of autocatalytic debris growth parameters.
Objective: Sirjan city has increased the economic importance of Kerman province. Sirjan is the main transit point for goods to eastern Iran, as well as Europe and the Persian Gulf and the return route of all commercial goods is from Shahid Rajaei port of Hormozgan to Central Asian countries, Caucasus and Russia. This city has a significant role and position as a special economic hub based on the advantages of docking, in the economic structure of Kerman province and in the future, the importance of this economic position will increase. Also, Golgohar mining region with iron ore rich mines as one of the most important active mining and industrial hubs in the Middle East, has many potentials to become a large and competitive region in Iran and even in the world. Golgohar Mining and Industrial Company, as one of the main players in the region, with a production capacity of 8.5 million tons of concentrate and 5 million tons of pellets, has highlighted its role in the country's steel industry and as a special economic hub based on the advantages of docking, it has a significant role and position in the economic structure of Kerman province. also in the future, the importance of this economic position will increase. Whereas the role of transport in economic development and the creation of incentives to increase investment in this regard is undeniable; The process of selecting different transportation systems and combining them with each other is an important matter in planning the optimal development of Kerman province, because in the absence of the necessary information, transportation decisions regarding the existing demand are made based on experience. Methods: In this study, demand function of carrying minerals in the rail transportation system of Golgohar mine estimated with econometrics technique and panel data model during the period 2011-2017. And while introducing the factors affecting the demand for mineral transportation by rail, using the neural network, the future trend of demand for rail transportation of minerals has been predicted. Findings: The results indicate, value added of the mining sector, tonnage cargo of cargo road transported , cost of freight by train and cargo revenue are the most important variables and they have significant impact and effective on the demand for rail transportation of Golgohar minerals. The inelasticity of the demand for rail transportation of minerals in relation to freight costs by train was also confirmed in this study. Variables not only affect demand for rail transportation, but also a significant impact on the income of producers, industrialists, railroad and eventually to consumer prices. Because minerals as a factor in generating transportation costs has a high share in the cost price. Conclusion: Care should be taken to determine the choice of shipping method according to the time. Therefore, due to the lack of rail facilities and side lines in all areas of demand, at present, only the proposed solutions can be satisfied in the short term.
Economic history and conditions, Economic growth, development, planning
This study aims to analyze the effect of imports, foreign exchange reserves, foreign debt, and interest rates on the currency exchange rates against the United States Dollar in Southeast Asia countries. The study results found that from 2010 to 2017, the currency exchange rates against the United States Dollar in Southeast Asian countries tended to weaken (depreciate). The highest growth in the exchange rate against the United States dollar was in Indonesia, while the lowest was in Singapore. Foreign exchange reserves negatively affect foreign debt, and imports positively affect countries' exchange rates in the Southeast Asia region against the United States dollar. On the other hand, interest rates do not show a significant effect.
This research gave the empirical verification that orange’s agribusiness can be a source of new growth for its center production region, as in case the Gerga’s orange agribusiness for Bengkulu province in Indonesia. It is verified this kind of agribusiness benefited its largest stakeholder, namely farmers, based on parameters such as B/C ratio, farmer's share, and marketing margin. From a macroeconomic perspective, Gerga's agribusiness also can solve macroeconomic problems such as poverty, unemployment, basic needs, and regional minimum wages.
We consider the growth rate of the Mahler measure in discrete dynamical systems with the Laurent property, and in cluster algebras, and compare this with other measures of growth. In particular, we formulate the conjecture that the growth rate of the logarithmic Mahler measure coincides with the algebraic entropy, which is defined in terms of degree growth. Evidence for this conjecture is provided by exact and numerical calculations of the Mahler measure for a family of Laurent polynomials generated by rank 2 cluster algebras, for a recurrence of third order related to the Markoff numbers, and for the Somos-4 recurrence. Also, for the sequence of Laurent polynomials associated with the Kronecker quiver (the cluster algebra of affine type $\tilde{A}_1)$ we prove a precise formula for the leading order asymptotics of the logarithmic Mahler measure, which grows linearly.