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This paper aims to study boundary and interior layer phenomena in coupled multiscale parabolic convection–diffusion interface problems and to present their efficient numerical resolution and analysis.

This study includes cases in which the diffusion parameters are small, distinct and can differ in order of magnitude. The source term is considered to be discontinuous. The asymptotic behavior of the solution is examined. The layer structure is analyzed, leading to the development of a variant of layer-resolving Shishkin mesh. For efficient numerical resolution, two methods are developed by combining additive schemes on a uniform mesh to discretize in time and an upwind difference scheme away from the line of discontinuity and a specific upwind difference scheme along the line of discontinuity, defined on a variant of layer resolving Shishkin mesh, to discretize in space. The analysis of the numerical resolution is discussed using the barrier function approach. Numerical simulations provide a verification of the theory and efficiency of the approach.

The discontinuity in the source term, along with the inclusion of small and distinct diffusion parameters, results in multiple overlapping and interacting boundary and interior layers. The work demonstrates that the present approach is robust in resolving boundary and interior layers. From a computational cost perspective, the numerical resolution presented in the paper is more efficient than conventional approaches.

Efficient numerical resolution and analysis of boundary and interior layer phenomena in coupled multiscale parabolic convection–diffusion interface problems are provided. The discretization of the coupled system in the approach incorporates a distinctive feature, wherein the components of the approximate solution are decoupled at each time level, resulting in tridiagonal linear systems to be solved, in contrast to large banded linear systems with conventional approaches.

The purpose of this study is to critically review consumer knowledge in marketing and propose a future research agenda. Despite the many works that have examined this variable, given its strong influence on behaviour, it has generally been studied in association with other constructs, and no studies have focused on it in a specific way. Its definition, measurement and approaches to its role and usefulness are superficial and underdeveloped. After structuring and analysing the existing literature, the authors establish, (I) which aspects are of little use to the discipline, and (II) which research lines have the most potential and should be developed and studied in greater depth, to advance and complete the existing consumer knowledge framework.

A search was undertaken for documents in the main databases in which the term “consumer knowledge” appears in a marketing or consumer context, and a critical and reflexive approach was taken to analyse the main contributions and to structure them by content blocks.

Five main content blocks were identified. A set of research gaps were detected, mainly related to the lax conceptualisation of the topic, measurement problems and the scarcity of more useful works connected with business management, and several research lines are proposed that complement the existing framework to make it more complete and operational.

This paper offers a critical review and proposes a research agenda for one of the most used but little studied variables in the field of marketing, which may help academics and professionals in the discipline to continue developing useful theories and models.

El objetivo de este trabajo es revisar críticamente el conocimiento del consumidor en marketing y proponer una agenda de investigación futura. A pesar de los numerosos trabajos que han examinado esta variable, dada su fuerte influencia en el comportamiento, generalmente se ha estudiado en asociación con otros constructos, y ningún estudio se ha centrado en ella de manera específica. Su definición, medición y aproximaciones sobre su papel y utilidad son superficiales y poco desarrollados. Después de estructurar y analizar la literatura existente, establecemos (I) qué aspectos tienen poco uso para la disciplina y (II) qué líneas de investigación tienen más potencial y deben ser desarrolladas y estudiadas con mayor profundidad; para avanzar y completar el marco existente sobre conocimiento del consumidor.

Se realizó una búsqueda de documentos en las principales bases de datos en las que aparece el término “conocimiento del consumidor” en un contexto de marketing o consumo, y se adoptó un enfoque crítico y reflexivo para analizar las principales contribuciones y estructurarlas por bloques de contenido.

Se identificaron cinco bloques principales de contenido. Se detectó un conjunto de huecos de investigación, principalmente relacionados con la laxa conceptualización del tema, problemas de medición y la escasez de trabajos más útiles conectados con la gestión empresarial; y se proponen varias líneas de investigación que complementan el marco existente para hacerlo más completo y operativo.

Este documento ofrece una revisión crítica y propone una agenda de investigación para una de las variables más utilizadas pero poco estudiadas en el campo del marketing, lo que puede ayudar a académicos y profesionales en la disciplina a continuar desarrollando teorías y modelos útiles.

本文旨在对市场营销中的消费者知识进行批判性审视, 并提出未来的研究议程。虽然已有许多研究检验了该变量, 但由于其对行为产生强大影响, 通常会与其他结构变量一起研究, 而没有以特定方式专注于该变量。对其定义、测量以及其作用和用途的方法仍旧存在研究空白。通过对现有文献进行结构化分析后, 确定了以下两个方面：（I）哪些方面对该学科意义不大, （II）哪些研究方向最具研究潜力, 并且应该进一步深入发展和研究, 以推进和完善现有的消费者知识框架。

通过主要数据库检索市场营销或消费者背景下涉及“消费者知识”一词的文献, 采取批判性和反思性方法来分析其主要贡献, 并通过内容块对其进行结构化。

识别了五个主要内容块, 并发现存在一定程度的研究空白, 主要涉及该主题的概念松散化、测量问题以及与商业管理相关的有效研究的稀缺性。此外, 本文提出了几个研究线索, 这些线索为现有框架补充了信息, 使其更加完整且具备更强的操作性。

本文对市场营销领域中广泛使用但研究较少的变量进行了批判性评述, 并提出了相关研究议程。这一工作有助于学术界和专业人士继续发展实用的理论和模型。

The substantial rise in tax evasion raises concerns about its adverse impact on the tax system’s integrity. This article aims to empirically investigate the factors affecting income tax evasion behavior among individuals in India’s National Capital Region (NCR).

A cross-sectional design was applied to gather primary data from (N = 548) taxpayers using a “self-administered survey questionnaire.” The hybrid “partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA)” approach was applied to analyze the data.

This research presents a comprehensive model that explains 53.4% of the variance in behavioral intention and accounts for 51.4% in the actual behavior of individuals who participate in income tax evasion. The outcomes show that high tax burden, corruption and complexity of the tax system significantly influence behavioral intention. On the contrary, digitalization and tax morale insignificantly influence behavioral intention. Notably, behavioral intention is significantly associated with the actual behavior of individuals engaging in income tax evasion.

The outcomes offer valuable implications for practitioners, including policymakers, governments and tax authorities, to effectively curb income tax evasion behavior and help them make informed decisions.

The innovative research model enhances prevailing knowledge by providing empirical insights into the effect of income tax evasion behavior among individuals in India. This study also contributes methodologically by combining PLS (linear) and fsQCA (nonlinear) techniques, demonstrating that both methodologies offer a deeper comprehension of the factors affecting individual behavior to engage in income tax evasion.

This research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.

To obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.

The study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.

The study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.

The research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.

This work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.

Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.

Research indicates that integral control parameters (K_i) affect outputs substantially more than proportional control parameters (K_p). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.

This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

This study aims to examine the intentions of sexual/gender minority travelers to choose LGBTQ+ friendly hotels based on their self-concepts and branding approaches.

An online survey is conducted, and 295 participants are recruited through a consumer panels firm. Hypotheses are tested through a mixed ANOVA.

Sexual orientation/gender identity openness and collective self-esteem increase the intentions to choose LGBTQ+ friendly hotels. Also, the intentions are higher for a hotel with a well-known LGBTQ+ friendly reputation than for a hotel with LGBTQ+ friendly advertising and promotions; however, there is no difference found between traveling domestically and traveling abroad.

This research demonstrates that the intentions of sexual/gender minority travelers’ to choose LGBTQ+ friendly hotels are driven by collective self-esteem, while sexual/gender openness and branding approaches play a smaller role.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

The main cause of boiler tube failure in high-temperature thermal power plants is molten sulphate-vanadate-induced hot corrosion. Applying thermal spray coatings on alloy steels can reduce hot corrosion. This study aims to examine the impact of nano yttria stabilized zirconia (YSZ) reinforcement on Ni-20Cr composite coatings on hot corrosion behaviour of T22 steel in a corrosive environment of Na₂SO₄-60%V₂O₅ at 650 °C over 50 cycles.

The coatings were deposited using a high velocity oxyfuel technique. The samples were subjected to hot corrosion in a Silicon tube furnace at 650 °C for 50 cycles. Weight gain data after each cycle were used to analyse the kinetics of corrosion behaviour. Corrosion products were examined using X-ray diffraction, scanning electron microscopy, energy dispersive and cross-sectional analytical methods.

During investigation, nano YSZ-reinforced Ni-20Cr composite coatings on T22 steel were discovered to give superior corrosion resistance in a molten salt environment at 650 °C. Throughout the experiment, the coatings gained less weight and formed protective oxide scales. Increased YSZ concentration in the coating matrix resulted in better protection against hot corrosion.

The inclusion of nano YSZ reduced porosity in Ni-20Cr coatings by filling voids and interlocking particles, as well as blocking the penetration of corroding species, hence improving the corrosion resistance of composite coatings. The corrosion rate decreased with increasing YSZ content in the coating matrix.

It should be noted that the high temperature corrosion behaviour of thermally sprayed nano YSZ-Ni-20Cr composite coatings has never been investigated and is not documented in the literature. As a result, the current study can provide useful information for the application of nano YSZ-reinforced coatings in high-temperature fuel combustion conditions.

In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

Electricity theft is a chronic issue that undermines the power quality and reduces the financial stability of power distribution companies. This study aims to empirically investigate the factors affecting electricity theft and its impact on power quality in rural and urban regions of India.

This research involves two cross-sectional surveys using self-administered questionnaires: Study R comprises 480 rural respondents, while Study U comprises 461 urban respondents to assess the causes of electricity theft. Data analysis was conducted using the statistical package for social science and the partial least squares-structural equation modelling technique.

The findings revealed that corruption, economic and government factors significantly influence electricity theft, while technology factors insignificantly influence electricity theft in both studies. However, in Study R, social factors had a statistically significant impact, but in Study U, it had an insignificant impact on electricity theft. Furthermore, the findings also reported a substantial link between electricity theft and power quality deterioration.

The study is unique and more generalisable, providing valuable insights to the government to develop a well-planned stringent law to enhance electricity service quality and reduce electricity theft cases that can benefit the entire community. Further, this study shed light on contributing factors of electricity theft to improve power quality that can assist in achieving the (sustainable development goal 7 – reliable, affordable and sustainable energy) mission of the Indian Government.

To the best of the authors’ knowledge, the present study represents the initial study to examine the notion of Indian electricity users regarding power theft and quality deterioration among India’s rural and urban regions. That would provide a base for the government and electricity board to combat power theft and provide a quality power supply. Moreover, this study offers a robust theoretical model with the new latent variable for a current and pertinent topic.