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This study aims to conduct a bibliometric analysis on “intertemporal choice” behavior of individuals from journals in the Scopus database between 1957 and 2023. The research covered the data on the said topic since it first originated in the Scopus database and carried out performance analysis and content analysis of papers in the business management and finance disciplines.

Bibliometric analysis, including science mapping and performance analysis, followed by content analysis of the papers of identified clusters, was conducted. Three clusters based on cocitation analysis and six themes (three major and three minor) were identified using the bibliometrix package in R studio. The content analysis of the papers in these clusters and themes have been discussed in this study, along with the thematic evolution of intertemporal choice research over the period of time, paving a way for future research studies.

The review unpacks publication and citation trends of intertemporal choice behavior, the most significant authors, journals and papers along with the major clusters and themes of research based on cocitation and degree of centrality and relevance, respectively, i.e. discounting experiments and intertemporal choice, impulsivity, risk preference, time-inconsistent preference, etc.

Over the past years, the research on “intertemporal choice” has flourished because of the increasing interest of researchers and scholars from different fields and the dynamic and pervasive nature of this topic. The well-developed and scattered body of knowledge on intertemporal choice has led to the need of applying a bibliometric analysis in the intertemporal choice literature.

Real‐time multiplication of two analog signals is one of the most important operations in analogue signal processing. Driven by low‐power and low‐voltage requirements for integrated mixedsignal portable applications, the paper's aim is to propose a novel four‐quadrant low‐voltage analog multiplier using dynamic threshold MOS transistors (DTMOS).

The SPICE simulations were performed with 0.25 μm technology parameters and results verify the performance of the circuit. The multiplier is simulated at low‐supply voltage of ±0.5 V.

The proposed multiplier has high linearity and simple structure hence it is suitable for high‐performance and low‐power analog VLSI applications.

A new low‐voltage four quadrant analog multiplier using DTMOS circuit topology is presented in the paper.

The purpose of this study was to explore patient’s perception of their disorder.

This study used a phenomenographical approach within a qualitative research paradigm. A total of 21 patients with knee osteoarthritis (OA) were recruited for the study, and data were collected through open-ended face-to-face interviews. The interviews were transcribed and thematically analyzed. The transcribed verbatim was analyzed for themes.

The themes developed reflected the patients’ perceptions about the disease process. Thematic analysis revealed three themes: Knee OA is a degenerative disease, Knee OA is an age-related disease and Knee OA is caused by certain activities of daily living. The patient’s information varied and was limited to what had been provided by the health-care practitioner. The knowledge was more biomedical in orientation and was limited and not supported by the evidence.

There is a need to provide evidence-based information that the patient must understand. Health-care providers must use a biopsychosocial framework to discuss the disease knowledge with patients.

This study helps us in identifying disease perceptions that can be used to design education programs for knee OA patients. It also highlights the need for delivering educational programs to knee OA patients.

This study lays a foundation for further research. To the author’s best knowledge, this is the first study to explore disease perceptions using a qualitative approach conducted among patients from a lower middle-income country.

This study aims to improve the detection and quantification of cardiac issues, which are a leading cause of mortality globally. By leveraging past data and using knowledge mining strategies, this study seeks to develop a technique that could assess and predict the onset of cardiac sickness in real time. The use of a triple algorithm, combining particle swarm optimization (PSO), artificial bee colony (ABC) and support vector machine (SVM), is proposed to enhance the accuracy of predictions. The purpose is to contribute to the existing body of knowledge on cardiac disease prognosis and improve overall performance in health care.

This research uses a knowledge-mining strategy to enhance the detection and quantification of cardiac issues. Decision trees are used to form predictions of cardiovascular disorders, and these predictions are evaluated using training data and test results. The study has also introduced a novel triple algorithm that combines three different combination processes: PSO, ABC and SVM to process and merge the data. A neural network is then used to classify the data based on these three approaches. Real data on various aspects of cardiac disease are incorporated into the simulation.

The results of this study suggest that the proposed triple algorithm, using the combination of PSO, ABC and SVM, significantly improves the accuracy of predictions for cardiac disease. By processing and merging data using the triple algorithm, the neural network was able to effectively classify the data. The incorporation of real data on various aspects of cardiac disease in the simulation further enhanced the findings. This research contributes to the existing knowledge on cardiac disease prognosis and highlights the potential of leveraging past data for strategic forecasting in the health-care sector.

The originality of this research lies in the development of the triple algorithm, which combines multiple data mining strategies to improve prognosis accuracy for cardiac diseases. This approach differs from existing methods by using a combination of PSO, ABC, SVM, information gain, genetic algorithms and bacterial foraging optimization with the Gray Wolf Optimizer. The proposed technique offers a novel and valuable contribution to the field, enhancing the competitive position and overall performance of businesses in the health-care sector.