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Social determinants of stressful events (SE) play an important role in justifying the cause of inequality in the experience of SE. The purpose of this study is to investigate the determinants that impact on inequality in the experience of SE.

The statistical population of this study includes all residents of Tehran. The statistical sample was 5,895 people who were selected by multistage cluster method and were proportional to the population size. The research tool is a researcher-made questionnaire designed to measure SE in Tehran, which includes 11 psychological stressors. The Oaxaca–Blinder decomposing method was used to analyze data.

In a total of 11 psychological SE, in 6 of those events, there was significant inequality in the experience of SE. Concentration Index (CI) of political SE is (CI = −0.27, 95% CI: −0.47, −0.07) and in favor of the rich (pro-rich). Education (OR = 1.60) in poor group and region development in poor and rich (respectively in all of the following) (OR = 0.42–0.73) are the main determinants of inequality in this stressor. CI of neighborhood underdevelopment SE is (CI = −0.47, 95% CI: 0.66, −0.28) and pro-rich. Education (OR = 1.26–1.27) and region development (OR = 1.18–2.24) are the main determinants of inequality in this stressor. CI of livelihood problems SE is (CI = −0.58, 95% CI: 0.68, −0.32) and pro-rich. Education (OR = 1.40) and health status (OR = 1.63) in poor group are the main determinants of inequality in this stressor. CI of future uncertainty SE is (CI = −0.12, 95% CI: 0.34, −0.08) and pro-rich. Gender (OR = 1.22) in poor group and region development (OR = 0.24–0.58) are the main determinants of inequality in this stressor. CI of education problems (CI = 0.19, 95% CI: 0.05, 0.32) and pro-poor. Age (OR = 0.32–0.34) and education (OR = 3.65–3.30) are the main determinants of inequality in this stressor. CI of housing problems is (CI = −0.29, 95% CI: −0.49, −0.08) and pro-rich. Education (OR = 1.31) and region development (OR = 1.64) in poor group are the main determinants of inequality in this stressor.

The first limitation is related to the level of data analysis, and the second limitation is the lack of comprehensive data on social determinants.

Social determinants affecting the formation of inequality in the experience of SE. Some social determinants, such as the level of education and development of the region, play a more prominent role in justifying inequality in the experience of stress between rich and poor groups.

Inequality in the experience of SE is a serious threat to mental and social health. One of the ways to reduce the experience of psychological and social stress is to pay attention to social determinants that play a role in the formation of stress.

This original paper was conducted by examining the effect of social determinants on the formation of inequality in the experience of stress, which draws the serious attention of policymakers.

Brazil’s regional inequality is an important topic due to the large and persistent differences in development between states and the high levels of inequality in the country. These variations in development can potentially render survey data inaccurate since the significance of capital income varies across the states. Besides, previous studies incorporating tax and national accounts data globally have mainly focused on measuring the income distribution at the country-level. This approach can limit the understanding of inequality, especially when considering large countries such as Brazil.

The methodology used to construct these estimates follows the guidelines of the Distributional National Accounts, whose core goal is to provide income distribution measures consistent with macroeconomic aggregates and harmonized across countries and time. The procedure has three main steps: first, it corrects the survey’s underrepresentation of top incomes using tax data. Then, it accounts for national income items not included in the survey or tax data, such as imputed rents and undistributed profits. Finally, it ensures that all components match the national income.

Compared to survey-based estimations, the results reveal a new angle on the state-level inequality. This study indicates that Amazonas, Rio de Janeiro and São Paulo have a more concentrated income distribution. The top 1\% of earners in these states receives around 28\% of total pre-tax income, while the top 10\% receive nearly 60\%. On the other end, Amapá (AP), Acre (AC), Rondônia (RO) and Santa Catarina (SC) are the states where the income distribution is less concentrated. There were no significant changes in the income distribution across the states during the period analyzed.

This study combines survey, tax and national accounts data to construct new estimates of Brazil’s state-level income distribution from 2006 to 2019. Previous results only considered income captured in surveys, which usually misses a significant part of capital incomes. This limitation may bias comparisons as capital income has different importance across the states. The new estimates represent the income of top groups more accurately, account for the entire national income and enable to compare regional inequality levels consistently with other countries.

Limited research exists on the role of information access as a key success factor for rural entrepreneurs. The purpose of this study is to examine the importance of information access, among other inequality forms, in shaping entrepreneurial outcomes.

The authors used a qualitative, inductive research methodology. They conducted interviews with rural entrepreneurs and gained insights into the role of information access and basic tangible resources in shaping their ventures.

The authors identified two critical factors that impact rural entrepreneurs: inequalities in access to information and basic tangible resources. They found that inequalities in rural entrepreneurship are often interrelated, rather than isolated. The authors demonstrated that the relationship between entrepreneurship and inequality is not solely characterized by a positive or negative correlation but is a dynamic interplay where certain inequality forms may create barriers to opportunities for some individuals while creating opportunities for others.

The authors uncovered key barriers in rural entrepreneurship and constructed a roadmap to effectively address these challenges, providing valuable insights to policymakers and support initiatives and enabling the target of high-impact resources. This research supports efforts to provide rural entrepreneurs with equal opportunities to grow and succeed.

This research significantly contributes to the field by examining the complexities of entrepreneurship in emerging economies, with a particular focus on rural areas. The authors introduced a comprehensive conceptual model linking inequality and entrepreneurship, explored the challenges faced by rural entrepreneurs and offered strategic policy recommendations for development initiatives.

The study aims to optimize truck routes by minimizing social and economic costs. It introduces a strategy involving diverse drones and their potential for reusing at DNs based on flight range. In HTDRP-DC, trucks can select and transport various drones to LDs to reduce deprivation time. This study estimates the nonlinear deprivation cost function using a linear two-piece-wise function, leading to MILP formulations. A heuristic-based Benders Decomposition approach is implemented to address medium and large instances. Valid inequalities and a heuristic method enhance convergence boundaries, ensuring an efficient solution methodology.

Research has yet to address critical factors in disaster logistics: minimizing the social and economic costs simultaneously and using drones in relief distribution; deprivation as a social cost measures the human suffering from a shortage of relief supplies. The proposed hybrid truck-drone routing problem minimizing deprivation cost (HTDRP-DC) involves distributing relief supplies to dispersed demand nodes with undamaged (LDs) or damaged (DNs) access roads, utilizing multiple trucks and diverse drones. A Benders Decomposition approach is enhanced by accelerating techniques.

Incorporating deprivation and economic costs results in selecting optimal routes, effectively reducing the time required to assist affected areas. Additionally, employing various drone types and their reuse in damaged nodes reduces deprivation time and associated deprivation costs. The study employs valid inequalities and the heuristic method to solve the master problem, substantially reducing computational time and iterations compared to GAMS and classical Benders Decomposition Algorithm. The proposed heuristic-based Benders Decomposition approach is applied to a disaster in Tehran, demonstrating efficient solutions for the HTDRP-DC regarding computational time and convergence rate.

Current research introduces an HTDRP-DC problem that addresses minimizing deprivation costs considering the vehicle’s arrival time as the deprivation time, offering a unique solution to optimize route selection in relief distribution. Furthermore, integrating heuristic methods and valid inequalities into the Benders Decomposition approach enhances its effectiveness in solving complex routing challenges in disaster scenarios.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

This study aims to understand how perceived desirability and perceived feasibility influence Portuguese higher education students' social entrepreneurial intention and general entrepreneurial intention and explore how gender attitudes can affect these relationships.

This study's sample comprises 391 participants. The hypotheses formulated in the research model were tested through structural equation modelling, using the bootstrapping method to perform decomposition tests and multigroup analyses to assess the effect of gender on perceived desirability, perceived feasibility and social and general entrepreneurial intention.

The sample data reveal that women have a greater social entrepreneurial intention, and men have a greater general entrepreneurial intention. The results regarding the research model reveal that perceived desirability positively influences social and general entrepreneurial intention, with stronger relationships for men than for women. However, perceived feasibility positively influences overall entrepreneurial intention but is insignificant in explaining social entrepreneurial intention. When the differences between genders are analysed, the perceived feasibility by women is significant and positively influences the social entrepreneurial intention, not being significant when men are considered. Contrary to previous studies about higher education students, men have a higher perceived desirability in both general and social entrepreneurial intentions, while women have a stronger perceived feasibility in both general and social entrepreneurial intentions.

This paper contributes to the development of the social entrepreneurship literature, demonstrating that social entrepreneurial intention can be influenced by gender. The results show the importance of considering the spatial and organisational context in examining the relationships between perceived desirability/perceived feasibility and social entrepreneurial intentions of men and women.

This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income inequality.

Municipal data from the Annual Social Information Report, the National Electric Energy Agency and the National Institute of Meteorology spanning 2002 to 2020 are utilized. The Synthetic Difference-in-Differences methodology is employed for empirical analysis, and robustness checks are conducted using the Doubly Robust Difference in Differences and the Double/Debiased Machine Learning methods.

The findings reveal that biomass plant installations lead to an average annual increase of approximately R$688.00 in formal workers' wages and reduce formal income inequality, with notable benefits observed for workers in the industry and agriculture sectors. The robustness tests support and validate the primary results, highlighting the positive implications of renewable energy integration on economic development in the studied municipalities.

This article represents a groundbreaking contribution to the existing literature as it pioneers the identification of the impact of biomass plant installation on formal employment income and local economic development in Brazil. To the best of our knowledge, this study is the first to uncover such effects. Moreover, the authors comprehensively examine sectoral implications and formal income inequality.

This paper aims to study the issues of output reachable set estimation for the linear singular Markovian jump systems (SMJSs) with time-varying delay based on a proportional plus derivative (PD) bumpless transfer (BT) output feedback (OF) control scheme.

To begin with, a sufficient criterion is given in the form of a linear matrix inequality based on the Lyapunov stability theory. Then, a PD-BT OF controller is designed to keep all the output signs of the system are maintain within a predetermined ellipsoid. Finally, numerical and practical examples are used to demonstrate the efficiency of the approach.

Based on PD control and BT control method, an OF control strategy for the linear SMJSs with time-varying delay is proposed.

The output reachable set synthesis of linear SMJSs with time-varying delay can be solved by using the proposed approach. Besides, to obtain more general results, the restrictive assumptions of some parameters are removed. Furthermore, a sufficiently small ellipsoid can be obtained by the design scheme adopted in this paper, which reduces the conservatism of the existing results.

This paper aims to propose a new linear parameter varying (LPV) controller for the robot tracking control problem. Using the identification of the robot dynamics in different work space points about modeling trajectory based on the least square of error algorithm, an LPV model for the robotic arm is extracted.

Parameter set mapping based on parameter component analysis results in a reduced polytopic LPV model that reduces the complexity of the implementation. An approximation of the required torque is computed based on the reduced LPV models. The state-feedback gain of each zone is computed by solving some linear matrix inequalities (LMIs) to sufficiently decrease the time derivative of a Lyapunov function. A novel smoothing method is used for the proposed controller to switch properly in the borders of the zones.

The polytopic set of the resulting gains creates the smooth switching polytopic LPV (SS-LPV) controller which is applied to the trajectory tracking problem of the six-degree-of-freedom PUMA 560 robotic arm. A sufficient condition ensures that the proposed controller stabilizes the polytopic LPV system against the torque estimation error.

Smoothing of the switching LPV controller is performed by defining some tolerances and creating some quasi-zones in the borders of the main zones leading to the compressed main zones. The proposed torque estimation is not a model-based technique; so the model variation and other disturbances cannot destroy the performance of the suggested controller. The proposed control scheme does not have any considerable computational load, because the control gains are obtained offline by solving some LMIs, and the torque computation is done online by a simple polytopic-based equation.

In this paper, a new SS-LPV controller is addressed for the trajectory tracking problem of robotic arms. Robot workspace is zoned into some main zones in such a way that the number of models in each zone is almost equal. Data obtained from the modeling trajectory is used to design the state-feedback control gain.