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The effects which are produced by pearlescent pigments are intimately connected to optics and the interaction of light with matter. Pearlescent pigments are optical filters which reflect and transmit light which falls upon them. In order to understand pearlescent pigments, therefore it is first necessary to understand some of the basic laws of optics. I would like to review some of these laws to talk about light and its interaction with matter, how it can be separated into its components, the laws of reflection and refraction and how all of these laws apply to pearlescent pigments.

County Durham‐based paint manufacturer DeSoto Titanine has appointed Peter Fisk as chairman, in addition to his role as managing director.

This study aims to compare the performance of stocks of companies with high brand equity with the stocks of other companies listed on the stock market of emerging countries of Latin America: Brazil, Chile, Colombia, Mexico and Peru.

The valuable brands (brands with high brand equity) considered were the most valuable Latin America brands according to the Millward Brown reports. Carhart four-factor model was used to analyze performance and the total sample included 732 stocks in the Latin American market collected at Economatica, monthly, for a period of 10 years.

The Valuable Brands Portfolio presents the lowest investment risk, suggesting that stocks of companies with valuable brands ensure lower risk investment to shareholders in these emerging markets.

This study is the first to associate brand equity with the creation of shareholder value in the context of emerging Latin American countries. In addition, the proposed method has also not been used previously to study emerging countries. The association found between a marketing asset (brand equity) and stock market performance contributes to improve the relationship between marketing and finance areas. The results of this study in emerging markets corroborate previous studies in developed markets, strongly suggesting the confirmation of the effect of brand equity on the reduction of risk stock.

This paper aims to study the application of reinforcement learning (RL) in the control of an output-constrained flapping-wing micro aerial vehicle (FWMAV) with system uncertainty.

A six-degrees-of-freedom hummingbird model is used without consideration of the inertial effects of the wings. A RL algorithm based on actor–critic framework is applied, which consists of an actor network with unknown policy gradient and a critic network with unknown value function. Considering the good performance of neural network (NN) in fitting nonlinearity and its optimum characteristics, an actor–critic NN optimization algorithm is designed, in which the actor and critic NNs are used to generate a policy and approximate the cost functions, respectively. In addition, to ensure the safe and stable flight of the FWMAV, a barrier Lyapunov function is used to make the flight states constrained in predefined regions. Based on the Lyapunov stability theory, the stability of the system is analyzed, and finally, the feasibility of RL in the control of a FWMAV is verified through simulation.

The proposed RL control scheme works well in ensuring the trajectory tracking of the FWMAV in the presence of output constraint and system uncertainty.

A novel RL algorithm based on actor–critic framework is applied to the control of a FWMAV with system uncertainty. For the stable and safe flight of the FWMAV, the output constraint problem is considered and solved by barrier Lyapunov function-based control.

The purpose of this study is to simulate the natural convection of a heated square shape embedded in a circular enclosure filled with nanofluid using an incompressible smoothed particle hydrodynamics (ISPH) method.

In the ISPH method, the evaluated pressure was stabilized by using a modified source term in solving the pressure Poisson equation. The divergence of the velocity was corrected, and the dummy particles were used to treat the rigid boundary. Dummy wall particles were initially settled in outer layers of the circular enclosure for preventing particle penetration and reducing the error of truncated kernel. The circular enclosure was partially filled with a porous medium near to the outer region. The single-phase model was used for the nanofluid, and the Brinkman–Forchheimer-extended Darcy model was used for the porous medium. Dummy wall particles were initially settled in outer layers of circular enclosure for preventing particle penetration and reducing error from the truncated kernel on the boundary.

The length of the inner square shape plays an important role in enhancing the heat transfer and reducing the fluid flow inside a circular enclosure. The porous layer represents a resistance force for the fluid flow and heat transfer, and, consequently, the velocity field and temperature distributions are reduced at the outer region of the circular cylinder. Then, the radius of the inner square shape, Darcy parameter and radius of the porous layer were considered the main factors for controlling the fluid flow and heat transfer inside a circular enclosure. The average Nusselt number decreases as the inner square length, radius of the porous layer and solid volume fraction increase.

The stabilized ISPH method is corrected for simulating the natural convection from an inner hot square inside a nanofluid-filled circular enclosure saturated with a partial layer of a porous medium.

Computed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.

Radiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.

The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.

The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

The past two decades have witnessed a surge in entrepreneurship training (ET) programmes among religious organisations in Ghana. Despite this, current studies on the topic have revealed a gap in the perception of ET initiatives and the actual needs and expectations of trainees. This paper aims to explore the characteristics of ET programmes in Christian organisations in Ghana and the perception of beneficiaries on the relevance of these schemes.

A qualitative research approach was used for the study. The study used interviews and document reviews as data collection instruments. In investigating research questions one and two for case study one, interviews were used. For case study two, data were collected mainly through a review of documents for research question one and interviews for research question two. The data were analysed using thematic analysis techniques.

The results show some similarities in ET characteristics in the two cases studied, specifically regarding objectives and target outcomes. Therefore, ET programmes identified in both cases were targeted at both potential and practicing entrepreneurs in the congregations. Further, content appears adequate with an emphasis on topics that develop business management skills. The teaching approach varied within the studied religious organisations and although some practices were similar to best practice guidelines suggested in literature, there are lapses in the system. Regarding respondents? perception on the relevance of the ET programmes, the authors observed a positive outlook among all respondents who argued that the scheme addressed key issues at individual, organisational and national levels. However, the study revealed that religious institutions were unwilling to provide seed funding to beneficiaries.

Further studies will need to validate findings in other Christian denominations not captured in the study. Furthermore, replicating this study among non-Christian religious institutions would be beneficial for policymaking.

This study suggests that managers of ET programmes in churches need to be helped to focus their programmes on developing business competencies, specifically technical skills.

There is a gap in understanding of the nature of ET programmes in religious institutions in Ghana and globally. This study provides insights on the characteristics of ET programmes in churches; thus allowing us to understand how they can be supported to deliver effectively.

This study aims to provide a review for scour in complex rivers and streams with coarser bed material, steep longitudinal bed slopes and dynamic environments, in the interest of the safety and the economy of hydraulic structures. The knowledge of scour in such geographical complexities is very crucial for a comprehensive understanding of scour failures and for establishing definitive criteria to bridge this major research gap.

The existing available literature shows significant work done in case of silt, sand and small sized coarser bed material but any substantial work for bed material of gravel size or above is lacking, resulting in a wide gap. Though some researchers have attempted to explore possibilities of refining the existing models by adding pier size, shape, sediment non-uniformity and armouring effects, which otherwise have been given a miss by the various researchers, including the pioneer in the field Lacey–Inglis (1930). But still, a rational model for scour estimation in such complex conditions for global use is yet to come. This is because all the parameters governing the scour have not been studied properly till date as is evident from the globally available literature and is witnessed in the field too, in recurrent failure of hydraulic structures especially bridges.

The researchers presume that the finer materials move only as a result of erosion. However, in actual field conditions, it has been observed that the large-sized stones also roll down and cause huge erosion along the river bed and damage the hydraulic structures, especially in the steep river/stream beds along hilly slopes. This fact has been overlooked in the models available globally and has been highlighted only in the current work in an attempt to recognize this major research gap. A study carried out on a number of streams globally and in Jammu and Kashmir, India also, has shown that in steep river and stream beds with bed material consisting of gravel size or greater than gravel, large scour holes ranging from 1 m to 5 m were created by furious floods, and due to other unknown forces along the channel path and near foundations of hydraulic structures.

To the best of the authors’ knowledge, this work is purely original.

Russia’s large-scale invasion of Ukraine has inflicted catastrophic consequences, profoundly impacting Ukrainian sustainability across the environment, society and economic dimensions. Despite these challenges, Ukraine’s civilian population has displayed remarkable resilience, emerging as a persistent stronghold amid the turbulence of conflict. In this study, the author has explored the key role of civil society in strengthening resilience and social justice during wartime.

This study uses a qualitative multi-method research design, combining a systematic literature review (SLR) and a single case study to understand and describe the phenomenon. The SLR provided a replicable, scientific and transparent conceptual framework, which the case study empirically validated and enriched. Additionally, the Delphi method gathered relevant insights from academic experts and Ukrainian nationals residing in the EU, enhancing this research.

This research revealed complex social dynamics and community resilience, highlighting various resilience-building efforts undertaken by voluntary organizations and emerging Ukrainian movements. The findings highlight civil society’s pivotal role in promoting resilience and addressing adversity to enhance social justice and unity in Ukraine. Specific contributions include countering disinformation, providing psychosocial support and mobilizing community efforts. Furthermore, the paper identified six key steps that epitomize societal resilience and formulated a conceptual model based on these steps.

This research offers a comprehensive and practical analysis of grassroots organizations’ efforts to sustain social resilience and unity amid war. It builds upon previous studies, extending their scope by providing empirical validation and offering new insights into civil society’s contributions to Ukraine’s defense and social cohesion. The study also explores the obstacles faced by civil society and the implications for Ukraine’s future sustainability and social cohesion.

The purpose of this paper is to illustrate the growing role of robots in environmental monitoring.

Following an introduction, this first considers aerial robots for monitoring atmospheric pollution. It then discusses the role of aerial, surface and underwater robots to monitor aquatic environments. Some examples are then provided of the robotic monitoring of the terrestrial environment, and finally, brief conclusions are drawn.

Robots are playing an important role in numerous environmental monitoring applications and have overcome many of the limitations of traditional methodologies. They operate in all media and frequently provide data with enhanced spatial and temporal coverage. In addition to detecting pollution and characterising environmental conditions, they can assist in locating illicit activities. Drones have benefited from the availability of small and lightweight imaging devices and sensors that can detect airborne pollutants and also characterise certain features of aquatic and terrestrial environments. As with other robotic applications, environmental drone imagery is benefiting from the use of AI techniques. Ranging from short-term local deployments to extended-duration oceanic missions, aquatic robots are increasingly being used to monitor and characterise freshwater and marine environments.

This provides a detailed insight into the growing number of ways that robots are being used to monitor the environment.