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Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

The chapter contains a methodology for formalized evaluation of the public fiscal policy from the view point of its impact on the financial stability of a national economy using the example of the Russian Federation and taking into account the fiscal multipliers’ effects. The significance of this problem is predetermined by recent trends in Russia’s development, when the national economy legs twice behind the world indicators. Taking into account the importance of the Russian budget system as a mechanism for the redistribution of gross domestic product (GDP), the financial stability safeguarding has been connected with the public finance sustainability and with the federal budget revenues and expenditures equilibrium. There are used the methodology of analysis of economic systems’ dynamic factors of financial stability as well as fiscal multipliers’ effects, aiming at managing national economy’s long-term development with the ultimate purpose to maintain the GDP growth rates. Taking into account the fiscal multipliers’ values, the model comparisons of the macroeconomics and budget parameters’ dynamics prove the necessity of the budget consolidation policy in 2018–2020 provided that the budget expenditures efficiency increases. The latter has been proved by modeling dependences represented by the fiscal multipliers’ effects in terms of national financial stability.

The study examines the process of international market expansion by a relatively new and small franchisor. Particular emphasis is placed on the company‘s efforts to identify a suitable partner in the host country, the adaptation of the concept to address differences in the new market, and the multitude of critical decisions that need to be made when franchising in international markets. The authors also discuss the role that strategic alliances play in the success of international franchising efforts. The paper concludes with the implications of this case for both researchers and practitioners.

The purpose of this paper is to evaluate the nature of emotion, self-esteem and life satisfaction tendencies amongst Iranian Muslim consumers when making impulse purchases.

Questionnaires were distributed amongst female Muslim participants at a shopping centre in Yazd, Iran – each of which were selected using cluster and random sampling methods. Data were analysed using descriptive statistics and structural equation modelling techniques, where LISREL software was used to measure the direct and indirect relationships between variables.

Within the sample, there was a direct causal relationship between impulse buying tendencies, impulse buying behaviour and purchasing. Second, there was a negative relationship between self-esteem and life-satisfaction within impulse buying tendencies. Finally, a positive relationship exists between emotion and impulse buying tendencies, which elicits impulse buying behaviour culminating in purchases. Emotion drives these consumers towards dissonance-reducing behaviour, which mediates low self-esteem and life satisfaction – through consumerism as a form of retail therapy. Some of the items purchased on impulse, that fulfilled this role, were hijabs (headscarves) and mantos (a type of tunic/shirt-dress/coat common in Iran).

The hijab is worn by Muslim females across the globe. However, the manto is an item of clothing worn almost exclusively by Iranian females. Therefore, it is likely that Muslim females in different geographies may exhibit similar behavioural traits, but their consumption patterns would substitute this item with a different one, such as an abaya or jilbab, for example.

Whilst the notion of retail therapy is widely understood, the novel contribution of this study lies in highlighting that the purchase of clothing such as hijabs and mantos by Iranian Muslim females is not just driven by rational and emotional decision-making seeking to fulfil religious obligations. There are cases where these religious artefacts are used to raise feelings of self-esteem and life satisfaction within the same individuals.

The objective of this research is to investigate various neural network (NN) observer techniques for sensors fault identification and diagnosis of nonlinear system in consideration of numerous faults, failures, uncertainties and disturbances. For the importunity of increasing the faults diagnosis and reconstruction preciseness, a new technique is used for modifying the weight parameters of NNs without enhancement of computational complexities.

Various techniques such as adaptive radial basis functions (ARBF), conventional radial basis functions, adaptive multi-layer perceptron, conventional multi-layer perceptron and extended state observer are presented. For increasing the fault detection preciseness, a new technique is used for updating the weight parameters of radial basis functions and multi-layer perceptron (MLP) without enhancement of computational complexities. Lyapunov stability theory and sliding-mode surface concepts are used for the weight-updating parameters. Based on the combination of these two concepts, the weight parameters of NNs are updated adaptively. The key purpose of utilization of adaptive weight is to enhance the detection of faults with high accuracy. Because of the online adaptation, the ARBF can detect various kinds of faults and failures such as simultaneous, incipient, intermittent and abrupt faults effectively. Results depict that the suggested algorithm (ARBF) demonstrates more confrontation to unknown disturbances, faults and system dynamics compared with other investigated techniques and techniques used in the literature. The proposed algorithms are investigated by the utilization of quadrotor unmanned aerial vehicle dynamics, which authenticate the efficiency of the suggested algorithm.

The proposed Lyapunov function theory and sliding-mode surface-based strategy are studied, which shows more efficiency to unknown faults, failures, uncertainties and disturbances compared with conventional approaches as well as techniques used in the literature.

For improvement of the system safety and for avoiding failure and damage, the rapid fault detection and isolation has a great significance; the proposed approaches in this research work guarantee the detection and reconstruction of unknown faults, which has a great significance for practical life.

In this research, two strategies such Lyapunov function theory and sliding-mode surface concept are used in combination for tuning the weight parameters of NNs adaptively. The main purpose of these strategies is the fault diagnosis and reconstruction with high accuracy in terms of shape as well as the magnitude of unknown faults. Results depict that the proposed strategy is more effective compared with techniques used in the literature.

The purpose of this paper is to provide a conceptual overview of linkages between buen vivir and social enterprise as emerging from a review of the literature regarding indigenous development approaches in Latin America. As reported in the literature such approaches aim to reduce poverty and affirm indigenous cultural identities through the sustainable use of natural resources.

The conceptual arguments build on a review of literature regarding social enterprise, mainly in its European conception, on social and solidarity economy according to several Latin American scholars, and on streams of literature related to indigenous development and indigenous entrepreneurship.

Against the failure of externally‐driven developmental policies, social enterprise can be considered as a useful vehicle for indigenous peoples to establish direct control and management of natural resources and territories that constitute an important step towards their self‐determination and self‐managed development.

An empirical validation of the presented argument is lacking in this paper and further empirical work is needed.

The paper is an attempt to provide a general conceptualization of social enterprise as a meaningful tool for the development of indigenous peoples in Latin America, bringing together different concepts borrowed from theories on social enterprise, social and solidarity economy and indigenous development.

Artificial intelligence (AI) is experiencing growth and prosperity worldwide because of its convenience and other benefits. However, AI faces challenges related to consumer resistance. Thus, drawing on the user resistance theory, this study explores factors that influence consumers’ resistance to AI and suggests ways to mitigate this negative influence.

This study tested four hypotheses across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI’s “substitute” image leads to consumer resistance to AI; Study 2 focused on the role of perceived threat as an underlying driver of resistance to AI. Studies 3–4 provided process evidence by the way of a measured moderator, testing whether AI with servant communication style and literal language style is resisted less.

This study showed that AI’s “substitute” image increased users' resistance to AI. This occurs because the substitute image increases consumers’ perceived threat. The study also found that using servant communication and literal language styles in the interaction between AI and consumers can mitigate the negative effects of AI-substituted images.

This study reveals the mechanism of action between AI image and consumers’ resistance and sheds light on how to choose appropriate image and expression styles for AI products, which is important for lowering consumer resistance to AI.

The purpose of this paper is to examine if credit rationing persists even in the era of financial liberalization, the extent to which individual, firm and loan characteristics influence the rationing behavior of commercial banks and whether the agricultural sector is discriminated against in the commercial bank credit market.

The study employed a probit model with marginal effects and a generalized Blinder-Oaxaca decomposition estimation on a randomly selected data of 1,239 entrepreneurs from eight commercial banks’ credit records about their individual, firm and loan characteristics.

The study revealed that credit rationing persists and that applying for a relatively longer payment period, providing collateral and guarantor, being illiterate, being relatively older and being in the agricultural sector increases the likelihood of being credit rationed, while having some relationship with the bank, having non-mandatory savings and applying from a bank with relatively high interest rates reduce the likelihood of being credit rationed. The study also revealed a credit gap of 17.77 percent and a positive discrimination against borrowers in the agricultural sector as the gap was largely being influenced by unexplained factors.

The research was intended to cover a large number of commercial banks in Ghana. However, most of the banks were unwilling to provide such information about their borrowers; hence, the research was limited to only eight commercial banks who provided the author with the information needed for the study.

The study concludes that policies that enhance human capital, women, and older access to credit and agricultural-oriented financial services and others, will go a long way to reduce rationing and increase access to credit, especially to the agricultural sector.

The research proposes the use of group lending as a form of collateral and monitoring to ease risks and default, and hence supports sustainable funding to increase access and outreach.

The paper looks at the comprehensive way about the various factors determining credit rationing in that it considers not only the individual, economic/firm and loan characteristics but also the extent to which discrimination toward the agricultural sector exists in the commercial banks credit market.