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In this study, we present a novel parametric iterative method for computing the polar decomposition and determining the matrix sign function.

This method demonstrates exceptional efficiency, requiring only two matrix-by-matrix multiplications and one matrix inversion per iteration. Additionally, we establish that the convergence order of the proposed method is three and four, and confirm that it is asymptotically stable.

In conclusion, we extend the iterative method to solve the Yang-Baxter-like matrix equation. The efficiency indices of the proposed methods are shown to be superior compared to previous approaches.

The efficiency and accuracy of our proposed methods are demonstrated through various high-dimensional numerical examples, highlighting their superiority over established methods.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

The purpose of this paper is to present a weighted algorithm based on the homotopy perturbation method for solving the heat transfer equation in the cast‐mould heterogeneous domain.

A weighted algorithm based on the homotopy perturbation method is used to minimize the volume of computations. The authors show that this technique yields the analytical solution of the desired problem in the form of a rapidly convergent series with easily computable components.

The authors illustrate that the proposed method produces satisfactory results with respect to Adomian decomposition method and standard homotopy perturbation method. The reliability of the method and the reduction in the size of computational domain give this method a wider applicability.

This research presents, for the first time, a new modification of the proposed technique, for aforementioned problems and some interesting results are obtained.

In today's advanced economy, there is a broader presence of information revolution, such as artificial intelligence (AI). AI primarily drives modern banking, leading to innovative banking channels, services and solutions disruptions. Thus, this chapter intends to determine AI's place in contemporary banking and stock market trading.

Stock market forecasting is hampered by the inherently noisy environments and significant volatility surrounding market trends. There needs to be more research on the mantle of AI in revolutionising banking and stock market trading. Attempting to bridge this gap, the present research study looks at the function of AI in banking and stock market trading.

The researchers have synthesised the literature pool. They undertook a systematic review and meta-synthesis method by identifying the major themes and a systematic literature review aided in the critical analysis, synthesis and mapping of the body of existing material.

The study's conclusions demonstrated the efficacy of AI, which has played a robust role in banking and finance by reducing risk and operational costs, enabling better customer experience, improving regulatory complaints and fraud detection and improving credit and loan decisions. AI has revolutionised stock market trading by forecasting future prices or trends in financial assets, optimising financial portfolios and analysing news or social media comments on the assets or firms.

AI's debut in banking and finance has brought sea changes in banking and stock market trading. AI in the banking industry and capital market can provide timely and apt information to its customers and customise the products as per their requirements.

The demand for the cold chain logistics of agricultural products was investigated through demand forecasting; targeted suggestions and countermeasures are provided. This paper aims to discuss the aforementioned statement.

A Markov-optimised mean GM (1, 1) model is proposed to forecast the demand for the cold chain logistics of agricultural products. The mean GM (1, 1) model was used to forecast the demand trend, and the Markov chain model was used for optimisation. Considering Guangxi province as an example, the feasibility and effectiveness of the proposed method were verified, and relevant suggestions are made.

Compared with other models, the Markov-optimised mean GM (1, 1) model can more effectively forecast the demand for the cold chain logistics of agricultural products, is closer to the actual value and has better accuracy and minor error. It shows that the demand forecast can provide specific suggestions and theoretical support for the development of cold chain logistics.

This study evaluated the development trend of the cold chain logistics of agricultural products based on the research horizon of demand forecasting for cold chain logistics. A Markov-optimised mean GM (1, 1) model is proposed to overcome the problem of poor prediction for series with considerable fluctuation in the modelling process, and improve the prediction accuracy. It finds a breakthrough to promote the development of cold chain logistics through empirical analysis, and give relevant suggestions based on the obtained results.

The purpose of this article is to develop and analyze a new derivative-free class of higher-order iterative methods for locating multiple roots numerically.

The scheme is generated by using King-type iterative methods. By employing the Traub-Steffensen technique, the proposed class is designed into the derivative-free family.

The proposed class requires three functional evaluations at each stage of computation to attain fourth-order convergency. Moreover, it can be observed that the theoretical convergency results of family are symmetrical for particular cases of multiplicity of zeros. This further motivates the authors to present the result in general, which confirms the convergency order of the methods. It is also worth mentioning that the authors can obtain already existing methods as particular cases of the family for some suitable choice of free disposable parameters. Finally, the authors include a wide variety of benchmark problems like van der Waals's equation, Planck's radiation law and clustered root problem. The numerical comparisons are included with several existing algorithms to confirm the applicability and effectiveness of the proposed methods.

The numerical results demonstrate that the proposed scheme performs better than the existing methods in terms of CPU timing and absolute residual errors.

The purpose of this paper is to examine perfume packaging in Spain and its effects on Basque female consumers’ purchase decision. The study population was made up of females, as they represent the highest consumer in the perfume market, accounting for 67 percent of the total perfume sales (Trufragance.com). Furthermore, in the past few years the perfume industry has basically targeted females (McIntyre, 2013).

An empirical study was conducted using a questionnaire to collect primary data in order to test the hypotheses. The questionnaire was distributed to 400 randomly selected respondents, from the general female population.

The findings show a relationship between the independent variables (i.e. visual packaging design, verbal packaging design, and packaging benefits) and the dependent variable (i.e. consumer purchase decision) based on several reasons discussed thoroughly in this paper. Additionally, age, education level, marital status, monthly income, and employment category of sample subjects influence the effect of perfume packaging on purchase decisions.

The main limitation of this study is the use of simple random sampling. The research findings bear important implications for more functional, emotional, environmental, and socially responsible marketing practice where packaging is concerned.

The findings of this study contribute to the understanding of packaging as a strategic marketing tool and how it can significantly influence the female’s purchase decision. Thus, giving managers and marketers a competitive advantage in this increasingly growing market. A new concept and measurement scale is presented that can be used for identifying creative packaging design and its benefits.

This study remains one of few research works focusing on the four dimensions of packaging benefits: functional, social, emotional, and environmental. Furthermore, it attempts to fulfill the identified need for encompassing potential and generally accepted packaging elements, including both the visual and verbal elements. Therefore, the uniqueness of this study arises from its examination of both aspects simultaneously, which has been ignored in previous research.

The purpose of the article is to construct a new class of higher-order iterative techniques for solving scalar nonlinear problems.

The scheme is generalized by using the power-mean notion. By applying Neville's interpolating technique, the methods are formulated into the derivative-free approaches. Further, to enhance the computational efficiency, the developed iterative methods have been extended to the methods with memory, with the aid of the self-accelerating parameter.

It is found that the presented family is optimal in terms of Kung and Traub conjecture as it evaluates only five functions in each iteration and attains convergence order sixteen. The proposed family is examined on some practical problems by modeling into nonlinear equations, such as chemical equilibrium problems, beam positioning problems, eigenvalue problems and fractional conversion in a chemical reactor. The obtained results confirm that the developed scheme works more adequately as compared to the existing methods from the literature. Furthermore, the basins of attraction of the different methods have been included to check the convergence in the complex plane.

The presented experiments show that the developed schemes are of great benefit to implement on real-life problems.

In order to be able to use the aroma hand massage as a skill that can be done by a nurse who does not have a special aromatherapy technique, we examine anti-stress effects of simplified aroma hand massage for healthy subjects. We evaluated the anti-stress action of aroma hand massage and the different components of the procedure in 20 healthy women in their twenties. We used autonomic nervous function measured via electrocardiogram as an index of stress. After conducting a baseline electrocardiogram, we induced stress in the participants by asking them to spend 30 minutes completing Kraepelin's arithmetic test. We then administered various treatments and examined the anti-stress effects. Kraepelin's test significantly increased sympathetic nervous function and significantly reduced parasympathetic nervous function. Compared with massage without essential oil or aroma inhalation, aroma hand massage significantly increased parasympathetic nervous function and significantly decreased sympathetic nervous function. The effect of the aroma hand massage persisted when the procedure was simplified. The anti-stress action of the aroma hand massage indicates that it might have beneficial application as a nursing technique. There are several limitations in this study; ambiguities of low component/high component ratio of heart rate variability and bias by small subjects groups of the same women.

In recent years, there is a gradual shift from sequential computing to parallel computing. Nowadays, nearly all computers are of multicore processors. To exploit the available cores, parallel computing becomes necessary. It increases speed by processing huge amount of data in real time. The purpose of this paper is to parallelize a set of well-known programs using different techniques to determine best way to parallelize a program experimented.

A set of numeric algorithms are parallelized using hand parallelization using OpenMP and auto parallelization using Pluto tool.

The work discovers that few of the algorithms are well suited in auto parallelization using Pluto tool but many of the algorithms execute more efficiently using OpenMP hand parallelization.

The work provides an original work on parallelization using OpenMP programming paradigm and Pluto tool.