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This study investigates the flow and heat transfer in a magnetohydrodynamic (MHD) ternary hybrid nanofluid (HNF), considering the effects of viscous dissipation and radiation.

The transport equations are transformed into nondimensional partial differential equations. The local nonsimilarity (LNS) technique is implemented to truncate nonsimilar dimensionless system. The LNS truncated equation can be treated as ordinary differential equations. The numerical results of the equation are accomplished through the implementation of the bvp4c solver, which leverages the fourth-order three-stage Lobatto IIIa formula as a finite difference scheme.

The findings of a comparative investigation carried out under diverse physical limitations demonstrate that ternary HNFs exhibit remarkably elevated thermal efficiency in contrast to conventional nanofluids.

The LNS approach (Mahesh et al., 2023; Khan et al., 20223; Farooq et al., 2023) that we have proposed is not currently being used to clarify the dynamical issue of HNF via porous media. The LNS method, in conjunction with the bvp4c up to its second truncation level, yields numerical solutions to nonlinear-coupled PDEs. Relevant results of the topic at hand, obtained by adjusting the appropriate parameters, are explained and shown visually via tables and diagrams.

Fixed mode noise and random mode noise always exist in the image sensor, which affects the imaging quality of the image sensor. The charge diffusion and color mixing between pixels in the photoelectric conversion process belong to fixed mode noise. This study aims to improve the image sensor imaging quality by processing the fixed mode noise.

Through an iterative training of an ergoable long- and short-term memory recurrent neural network model, the authors obtain a neural network model able to compensate for image noise crosstalk. To overcome the lack of differences in the same color pixels on each template of the image sensor under flat-field light, the data before and after compensation were used as a new data set to further train the neural network iteratively.

The comparison of the images compensated by the two sets of neural network models shows that the gray value distribution is more concentrated and uniform. The middle and high frequency components in the spatial spectrum are all increased, indicating that the compensated image edges change faster and are more detailed (Hinton and Salakhutdinov, 2006; LeCun et al., 1998; Mohanty et al., 2016; Zang et al., 2023).

In this paper, the authors use the iterative learning color image pixel crosstalk compensation method to effectively alleviate the incomplete color mixing problem caused by the insufficient filter rate and the electric crosstalk problem caused by the lateral diffusion of the optical charge caused by the adjacent pixel potential trap.

The purpose of this paper is to evaluate the relationship between bankers’ perspectives and their pro-green banking behaviors (i.e. intentions). Specifically, how do bankers’ perspectives on environmental concerns, environmental normative structure and green technology affect their intentions toward G-banking activities?

A theoretical framework of the theory of bounded rational planned behavior (TBRPB) as its foundation was established. Using measurement scales to measure different aspects of environmental concern, environmental normative structure, green technology, attitudes, perceived behavioral control and subjective norms, a survey instrument was developed to examine the various associations implied by the model of TBRPB. Data were collected from the bankers of selected commercial banks in Bangladesh following the random sampling procedure. The data were analyzed using the partial least square structural equation modeling technique.

Findings indicate that all of the predictors appear to be robust in predicting the G-banking intention of the sampled bankers in Bangladesh. The results also show that attitudes, subjective norms and perceived behavioral control have significant mediating effects toward bankers’ bounded rational G-banking intention.

There are a few limitations in the study. First, the study considers environmental concerns as an antecedent of the attitude of bankers toward G-banking activities. Future studies can explore other variables related to environmental problems to study G-banking adoption and practices. Second, this study only considers the private conventional bankers as respondents to the survey to assess G-baking intention. In the future, other types of bankers, such as Islamic bankers and public banks’ bankers could be included in the survey to explore G-banking practices. Finally, this research has been done in a developing country-context.

In this study, environmental concerns of bankers appeared to be highly significant predictors to influence their attitudes toward bounded rational G-banking intention. Similarly, the social normative structure also appears to be a robust antecedent of subjective norms to influence bounded rational G-banking intention of respondent bankers. Finally, green technology or bakers’ personal and skill-related ability to control bounded rational G-banking intention also appeared to be a strongly significant predictor of green banking activities. All this evidence implies that respondent bankers in the sample responded positively to provide their positive intention toward G-banking activities based on their environmental concern.

Important social implication of the current study is G-banking practices can help reduce carbon emissions and other pollutants which would enrich overall environmental sustainability and ecological conditions.

Few studies are directed on G-banking perspective in Bangladesh. This research is one of the empirical studies which will certainly add values for the clients, institutions and policymakers in banking paradigm.

Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.

Aim to the limitations of grey relational analysis of interval grey number, based on the generalized greyness of interval grey number, this paper tries to construct a grey angle cosine relational degree model from the perspective of proximity and similarity.

Firstly, the algorithms of the generalized greyness of interval grey number and interval grey number vector are given, and its properties are analyzed. Then, based on the grey relational theory, the grey angle cosine relational model is proposed based on the generalized greyness of interval grey number, and the relationship between the classical cosine similarity model and the grey angle cosine relational model is analyzed. Finally, the validity of the model in this paper is illustrated by the calculation examples and an application example of related factor analysis of maize yield.

The results show that the grey angle cosine relational degree model has strict theoretical basis, convenient calculation and is easy to program, which can not only fully utilize the information of interval grey numbers but also overcome the shortcomings of greyness relational degree model. The grey angle cosine relational degree is an extended form of cosine similarity degree of real numbers. The calculation examples and the related factor analysis of maize yield show that the model proposed in this paper is feasible and valid.

The research results not only further enrich the grey system theory and method but also provide a basis for the grey relational analysis of the sequences in which the interval grey numbers coexist with the real numbers.

The paper succeeds in realizing the algorithms of the generalized greyness of interval grey number and interval grey number vector, and the grey angle cosine relational degree, which provide a new method for grey relational analysis.

This paper aims mainly at introducing applied statisticians and econometricians to the current research methodology with non-Euclidean data sets. Specifically, it provides the basis and rationale for statistics in Wasserstein space, where the metric on probability measures is taken as a Wasserstein metric arising from optimal transport theory.

The authors spell out the basis and rationale for using Wasserstein metrics on the data space of (random) probability measures.

In elaborating the new statistical analysis of non-Euclidean data sets, the paper illustrates the generalization of traditional aspects of statistical inference following Frechet's program.

Besides the elaboration of research methodology for a new data analysis, the paper discusses the applications of Wasserstein metrics to the robustness of financial risk measures.

This study aims to analyze iodine content in various foods and water.

Food and water samples were collected from rural and urban areas of different agroclimatic zones of Haryana State, India, and analyzed for iodine content by a standard method.

A wide variation was observed in the iodine content of cereals, pulses, vegetables, fruits and milk. The iodine content in water samples varied from source to source and from zone to zone.

The investigators could not obtain sufficient samples of pulses and fruits for analysis from rural areas as they are not grown by farmers in their fields.

The study is original and innovative. The values of iodine for various foods are not available in the literature, and thus data of the present study will be useful to researchers, nutritionists, food scientists and dieticians.

Considers that as a general rule metrics serve to compare elements in the same set, such as the Euclidean metric by means of which we can calculate the distance between points ℜ ², ℜ³ etc. In practical applications such as in the biological sciences, it is necessary to compare cells which may have identical or different numbers of genes. Suggests that we may wish to compare different genes formed of finite sequences of four nucleotides. These are genes which may have the same or different numbers of bases. These and other questions are analysed from the viewpoint of general systems theory.

THE use of liquid oxygen as an oxidizer for various fuels in liquid rocket propellent systems is not new. Professor Goddard used liquid oxygen in his rocket experiments and the well known German V‐2 rockets used this material as an oxidizer. However, its effect on non‐metallic materials ordinarily used in rocket systems was not investigated until recent years. This investigation was prompted by phenomena which had been experienced by rocket engine and rocket aircraft manufacturers and by suppliers of the material. It was observed that when some organic materials came in intimate contact with liquid oxygen they became prone to detonation when subjected to certain impact energies. This was undoubtedly due to the formation of unstable organo‐peroxide compounds which when impacted released high levels of energy resulting in an explosion. Specifically, when liquid oxygen was accidently spilled on asphalt and inadvertently stepped on, the asphalt would often explode. Also, leather gaskets immersed in liquid oxygen and subjected to surge impact detonated with disastrous effects.

In the paper the optimization procedure of a direct current motor with a hybrid excitation has been presented. The excitation system consists of a shunt field winding disposed around field pole and permanent magnet, both acting in parallel. The field distribution in motor for various magnet and field pole dimensions has been determined using finite element method. The flux linkage with the armature winding has been computed. Demagnetizing effect of armature current was taken into account. The optimal permanent magnet and the field pole dimensions, ensuring minimum total cost of purchase and work during assumed period of time were calculated. Motor characteristics e.g. rated torque, rated current, speed regulation limit were used as constraints. In worked up algorithm and computer program Carroll internal penalty function method was used. Some basic suggestions tor design of optimal hybrid excited dc motors with parallel permanent magnet and electromagnetic excitation are given.