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Personal financial management is gaining momentum in modern society, as it plays a vital role in shaping the financial behaviour of people looking forward to adequate financial well-being. Hence, people in general and the weaker sections of society in particular are expected to show desirable financial behaviour for achieving individual as well as societal well-being. The present study investigates the impact of socio-economic characteristics on the financial behaviour of migrant workers who have migrated to the state of Tamil Nadu. The primary data were collected using the interview schedule from 200 migrant workers selected using the snowball sampling technique. The results show that migrant workers have mediocre financial behaviour. Furthermore, it is also found that migrant workers’ financial behaviour is not influenced by their socio-economic characteristics, such as gender, age, marital status, family type, and family income. However, the level of education has a significant positive role in shaping the financial behaviour of the migrant workers. The study outlines the need for specially engineered financial education for migrant workers.

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization.

The authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure.

The authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel.

To the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.

Existing deterministic models that predict the capacity of corroded reinforced concrete (RC) beams have limited applicability because they were based on accelerated tests that induce general corrosion. This research gap was addressed by performing a combined numerical and statistical analysis on RC beams, subjected to natural corrosion, to achieve a much better forecast.

Data of 42 naturally corroded beams were collected from the literature and analyzed numerically. Four constitutive models and their combinations were considered: the elastic-semi-plastic and elastic-perfectly-plastic models for steel, and two tensile models for concrete with and without the post-cracking stresses. Meanwhile, Popovics’ model was used to describe the behavior of concrete under compression. Corrosion coefficients were developed as functions of corrosion degree and beam parameters through linear regression analysis to fit the theoretical moment capacities with test data. The performance of the coefficients derived from different combinations of constitutive laws was then compared and validated.

The results showed that the highest accuracy (R² = 0.90) was achieved when the tensile response of concrete was modeled without the residual stresses after cracking and the steel was analyzed as an elastic-perfectly-plastic material. The proposed procedure and regression model also showed reasonable agreement with experimental data, even performing better than the current models derived from accelerated tests and traditional procedures.

This study presents a simple but reliable approach for quantifying the capacity of RC beams under more realistic conditions than previously reported. This method is simple and requires only a few variables to be employed. Civil engineers can use it to obtain a quick and rough estimate of the structural condition of corroding RC beams.

Informality in the tourism sector of the Philippines is a pervasive phenomenon, yet only few studies have explored the former's contribution to the latter, especially at the local level. Hence, this chapter utilizes a tourism value chain model developed by the United Nations World Tourism Organization and Directorate-General for Development and Cooperation (DEVCO) to determine the presence and examine the role of the informal sector in various spheres of Binondo's tourism industry. Findings reveal that informality is thriving in the areas of transportation, food and beverage, handicraft, leisure, excursion, tours, and support services. Conversely, they are inexistent or less visible in the accommodation and tourism assets in destination segments. Moreover, the fragmented tourism value chain of Binondo is heavily shaped by three primary challenges, namely, difference in perception between decision-makers and economic actors, resistance to change by the informal sector, and lack of relevant data to support evidence-based policymaking. To address these issues, the local government of Manila City may hence consider the following policy options: (i) mainstream a “whole-of-locality” approach, (ii) conduct an industry mapping of Binondo's tourism value chain, (iii) facilitate linkages between formal and informal businesses, (iv) develop shared infrastructure and extend capacity-building to the informal sector, and (v) employ a regulatory sandbox approach.

The purpose of this study is to provide evidence that government financing behavior has an impact on the outward foreign direct investment (OFDI) of enterprises.

This paper uses debt data from local government financing vehicles to measure the local government debt in China. Based on the data of listed manufacturing firms in China from 2010 to 2018, this paper uses the Tobit model to verify the impact of local government debt and firms' OFDI.

The results indicate that local government debt impedes firms' OFDI, with a more pronounced impact on state-owned enterprises (SOEs) and those with higher political connections. Furthermore, our study suggests that the dampening effect of local governments on firms' OFDI is mitigated in regions following the implementation of the Local Government Debt Management Act.

This study verifies the negative impact of local government debt activity on firms' overseas investments. This is not due to debt crowding out, but rather to the fact that local governments prefer to keep resources locally to stimulate the economy. This paper offers novel insights into the theoretical mechanisms by which local government behavior influences firms' investment activities in emerging markets.

The outbreak of COVID-19 has become a major public health emergency worldwide. How to effectively guide public opinion and implement precise prevention and control is a hot topic in current research. Mining the spatiotemporal coupling between online public opinion and offline epidemics can provide decision support for the precise management and control of future emergencies.

This study focuses on analyzing the spatiotemporal coupling relationship between public opinion and the epidemic. First, based on Weibo information and confirmed case information, a field framework is constructed using field theory. Second, SnowNLP is used for sentiment mining and LDA is utilized for topic extraction to analyze the topic evolution and the sentiment evolution of public opinion in each coupling stage. Finally, the spatial model is used to explore the coupling relationship between public opinion and the epidemic in space.

The findings show that there is a certain coupling between online public opinion sentiment and offline epidemics, with a significant coupling relationship in the time dimension, while there is no remarkable coupling relationship in space. In addition, the core topics of public concern are different at different coupling stages.

This study deeply explores the spatiotemporal coupling relationship between online public opinion and offline epidemics, adding a new research perspective to related research. The result can help the government and relevant departments understand the dynamic development of epidemic events and achieve precise control while mastering the dynamics of online public opinion.

This study aims to investigate the role of interfirm communication on the complementary effect between corporate political action (CPA) and research and development (R&D) investments under transaction costs.

A quasi-experiment study design was used, combining business game simulation and a public goods experiment with communication as a controlled variable. A fixed-effect regression analysis was performed on panel data collected from 72 students.

The findings indicate that collective CPA is positively and significantly associated with R&D investments, particularly when interfirm communication is present. Conversely, for non-communicating firms, the effect is limited to the relationship between individual CPA and R&D investment.

Despite advancing the research on political ties and innovation, the study acknowledges limitations related to framing effects and institutional variability.

The complementary effects indicate that institutional arrangements (i.e. business associations) may foster interfirm communication and cooperation in CPA efforts, mitigating opportunistic behaviours and legitimising CPA strategy towards innovation.

This study contributes to understanding how firms’ political and R&D decisions can positively impact innovation, despite the challenges of ex post transaction costs.

The positive effect of communication on political action and cooperation on firms’ innovation, even in weak institutional environments, is highlighted. An innovative methodological approach combining business games and economic experiments was used to examine participants’ decisions under transaction costs.

How does entrepreneurship flourish amidst persistent resource scarcity, market imperfections, underdeveloped infrastructure and institutional voids? In recent years, bricolage has emerged in the entrepreneurship literature as an effective form of resource mobilization in resource-constrained environments and crisis situations. The purpose of this study is to investigate the role of bricolage in new venture creation by examining the use of bricolage at each stage of the entrepreneurial process of opportunity discovery, development and exploitation.

The author conducted a qualitative analysis of 10 new business ventures established in the Philippines during the COVID-19 pandemic.

The author found a prevalence of bricolage at every stage of the entrepreneurial process in all cases, showing that bricolage was embedded in the behavior and decision-making of entrepreneurs throughout the process.

The finding have implications for policymakers aiming to support entrepreneurship in emerging economies.

This paper contributes to the literature by providing empirical evidence of bricolage behavior identified at every step of the entrepreneurial process in a specific emerging economy context.

Financial literacy, capability and behavior are crucial factors in personal financial management, which in turn plays a significant role in individual and societal financial well-being. The objective of this investigation is to explain critical factors and dimensions of personal financial management systems by employing a hybrid approach that encompasses a bibliometric analysis and a systematic review of the literature.

The research team carefully evaluated a selection of 606 scholarly articles from the Scopus database and studied the evolution of personal financial management behavior over 38 years (1986–2023). This research adopted several graphical representations and network structures to comprehend publishing tendencies, high-impact papers, theoretical frameworks, intellectual constructs as well as the current state of research collaboration.

Four major clusters were identified in the field of personal financial management behavior: the relationship between financial literacy and financial capability, factors influencing financial behavior, the impact of financial behavior on financial well-being and the financial behavior of different demographic groups. In addition, by performing content analysis on papers published within the last five years, new themes in personal financial management behavior were identified.

This investigation serves to equip financial advisors, policy architects and scholarly investigators with a deeper insight into the intricacies of personal financial management behavior and aids in pinpointing prospective domains for forthcoming research.

This study seeks to address a significant vacuum in the current body of research by providing a thorough bibliometric analysis that specifically examines financial literacy, ability and conduct. To the best of our knowledge, no previous research has conducted such a comprehensive investigation in this field. This research aims to identify important researchers and influential works in the subject by using a mixed-methods approach that combines qualitative and quantitative methodologies, including content analysis. The purpose of doing this is to provide exclusive insights and expertise that can be highly valuable to scholars, practitioners, policymakers and other stakeholders who are interested in furthering the comprehension and encouragement of financial literacy and responsible financial behavior.

The purpose of this study is to investigate the effect of catalyst distribution in the combustion catalytic layer on heat and mass transport characteristics of the auto-thermal methanol steam reforming microchannel reactor.

Computational fluid dynamics (CFD) method is used to study four different gradient designs. The corresponding distributions of temperature, species and chemical reaction rate are provided and compared.

The distributions of species, temperature and chemical reaction rate are significantly affected by the catalyst distribution in the combustion catalytic layer. A more uniform temperature distribution can be observed when the gradient design is used. Meanwhile, the methanol conversion rate is also improved.

This work reveals the effect of catalyst distribution in the combustion catalytic layer on heat and mass transport characteristics of the auto-thermal methanol steam reforming microchannel reactor and provides guidance for the design of reactors.

The temperature uniformity and hydrogen production performance can be improved by the gradient design in the combustion catalytic layer.