This work reports a multilead QT interval measurement algorithm for a high-resolution digital electrocardiograph. The software enables off-line ECG processing including QRS detection as well as an accurate multilead QT interval detection algorithm using support vector machines (SVMs). Two fiducial points (Qini and Tend) are estimated using the SVM algorithm on each incoming beat. This enables segmentation of the current beat for obtaining the P, QRS, and T waves. The QT interval is estimated by updating the QT interval on each lead, considering shifting techniques with respect to a valid beat template. The validation of the QT interval measurement algorithm is attained using the Physionet PTB diagnostic ECG database showing a percent error of 2.60±2.25 msec with respect to the database annotations. The usefulness of this software tool is also tested by considering the analysis of the ECG signals for a group of 60 patients acquired using our digital electrocardiograph. In this case, the validation is performed by comparing the estimated QT interval with respect to the estimation obtained using the Cardiosoft software providing a percent error of 2.49±1.99 msec.

Chatbots have emerged as valuable tools in public service domains such as healthcare and education, supporting information delivery, training, and user engagement. Despite growing adoption, development practices remain fragmented. This paper presents a Systematic Literature Review (SLR) of 27 studies to identify trends, challenges, and research gaps in chatbot development, focusing on technological approaches, frameworks, evaluation methodologies, and language distribution. Findings reveal a strong preference for open-source frameworks, particularly Rasa, valued for flexibility and scalability, while TensorFlow is often used for custom ML-based implementations. English dominates as the primary development language, with limited representation of Spanish, despite its global importance. Most studies apply quantitative (performance-based) metrics such as F1-score and accuracy; only one combines quantitative and qualitative evaluations, reflecting a lack of standardized assessment approaches. Additional gaps include the absence of hybrid evaluation frameworks, limited attention to Responsible AI principles, and scarce discussion on deployment requirements. Future research should prioritize multilingual chatbot models, integrate hybrid evaluation frameworks, and promote transparency to ensure scalability, ethical AI practices, and user-centered design.

Purpose: This paper proposes model-based standard times estimates, using multiple linear regression, nonlinear optimization, and fuzzy systems in four real cases assembly lines. The work includes a description of the models and a comparison of their performance with values obtained using the conventional chronometer method. These models allow estimating standard times without reconducting field studies. Design/methodology/approach: For the development of the time study, the methodology applied by the International Labour Organization (ILO) was used as a baseline. This methodology is structured in three phases: selection of the case study, registration of the process by direct observation, and calculation/estimation of the standard time. The selected case studies belong to real assembly lines of motorcycles, television sets, printed circuit boards, and bicycles. Findings: In the motorcycle’s assembly case, the study allowed constructing seven linear regression models to estimate standard times for assembling the front parts, and seven linear regression models to predict standard times for the rear parts of the different motorcycle types. Compared to the classical chronometer method, the results obtained never exceeded 10%. Regarding the case studies of assembling TV sets and PCBs, the study considered the construction of nonlinear optimization models that allow making appropriate predictions of the standard times in their assembly lines. Finally, for the bicycle assembly line, a fuzzy logic model to represent the standard time was constructed and validated. Research limitations/implications: For reasons of confidentiality of information, this work omitted the names of companies, services, and models of manufactured products. Originality/value: The literature consulted does not refer to the representation of standard time on assembly lines using mathematical models. The construction of these models with empirical data from actual assembly lines was a valuable aid to the companies involved in supporting activity planning.