Everardo Bárcenas

This study investigates the feasibility of acoustic signal analysis for the assessment of weld bead quality in the shielded metal arc welding (SMAW) process. The work focuses on comparing time-domain acoustic signals and time–frequency spectrogram representations for the classification of welds as accepted or rejected according to standard welding inspection criteria. Two key acoustic descriptors, the fundamental frequency (F₀) and the harmonics-to-noise ratio (HNR), were extracted and analyzed to evaluate statistical differences between the two weld quality classes. Statistical tests, including Anderson–Darling, Levene, ANOVA, and Kruskal–Wallis (α = 0.05), revealed significant differences between accepted and rejected welds. Accepted welds exhibited a bimodal HNR distribution associated with transient arc instability at the beginning and end of the bead, whereas rejected welds showed more uniform acoustic behavior throughout the process. Subsequently, the acoustic data were represented using both audio signals and spectrograms and used as inputs for ten supervised machine learning models, including Support Vector Classifier (SVC), Logistic Regression (LR), k-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), and Naïve Bayes (NB). The results demonstrate that spectrogram-based representations significantly outperform time-domain signals, achieving accuracies of 0.95–0.96, ROC-AUC values above 0.95, and false positive and false negative rates below 6%. These findings indicate that, while scalar acoustic descriptors provide statistically significant insight into weld quality, time–frequency representations combined with machine learning enable a more robust and reliable framework for automated non-destructive evaluation, particularly in manual SMAW processes under realistic operating conditions.

Fused Deposition Modeling (FDM) components require accurate identification of printing parameters to ensure reliable quality assessment and support scalable reverse-engineering workflows. The objective of this study is to evaluate whether mechanical response curves obtained from tensile tests can be used to infer key manufacturing parameters, specifically part orientation, layer thickness, and infill density. Force–displacement and stress–strain data were transformed into image-based representations and classified using several individual and ensemble machine learning models. In addition, the influence of applying a moving-average filter to smooth the curve-derived images was analyzed. Ensemble methods, particularly the AdaBoost classifier, achieved the best performance across the evaluated variables, with the highest accuracy obtained from unfiltered stress–strain images. Under limited-data conditions, ensemble models consistently outperformed individual classifiers, whereas Multilayer Perceptron and Support Vector Machine models exhibited more stable but lower predictive accuracy. These results demonstrate that mechanical response curves contain relevant information about manufacturing conditions and can be used to infer FDM printing parameters. The proposed approach offers a potential non-destructive framework for parameter identification in additively manufactured components, thereby improving traceability and quality control in additive manufacturing processes.

Smart Contracts are stored and executed on a Blockchain network, thereby automatically enforcing the predefined rules once the execution conditions are satisfied. Hence, if the contract incorporates contradictory design rules, it may result in unforeseen outcomes within the blockchain environment. Accordingly, this proposal models the rules embedded in a Smart Contract through the Web Ontology Language (OWL), by applying the formal definition of consistency within a verification framework grounded in Description Logics. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1258Dimensions.Open Alex.

Fused Deposition Modeling (FDM) components require accurate identification of printing parameters to support reliable quality assessment and scalable reverse‑engineering workflows. This study evaluates whether mechanical response curves can be used to infer critical manufacturing parameters—specifically build direction, layer thickness, and infill density. Force–displacement and stress–strain data obtained from tensile tests were converted into image‑based representations and classified using individual and ensemble machine learning models. The influence of applying a moving‑average filter to smooth the curve‑derived images was also examined. Ensemble approaches, particularly AdaBoost, achieved higher accuracy and robustness across the evaluated variables, with the best results obtained from unfiltered stress–strain images. Under limited‑data conditions, ensemble models generally outperformed individual classifiers, while Multilayer Perceptron and Support Vector Machine models showed more stable but less accurate behavior. Overall, the findings demonstrate the feasibility of predicting FDM printing parameters directly from mechanical‑curve‑derived images, enabling a non‑destructive approach suitable for scalable reverse‑engineering and improved traceability within additive manufacturing processes.

Machine learning techniques are increasingly employed to predict material properties and reduce mechanical testing costs. This study uses tensile load‒displacement data to predict the breaking force, maximum force, ultimate tensile strength (UTS), and breaking strength of nylon PA 12 specimens manufactured via selective laser sintering (SLS). To address variations in specimen ductility, linear and fourth-degree polynomial equalisations were applied to the data. The results indicate that polynomial equalisation generally achieves superior predictive performance across various machine learning models. While all the models exhibit a decline in performance metrics with a reduction in the observation matrix data, several demonstrate strong predictive capabilities even with reduced data sets. Specifically, when using 20% of the observation matrix data (corresponding to the elastic zone) are used, the kernel ridge, elastic net, and Lasso regression models achieve R ² values of 0.987, 0.931, and 0.987, respectively, for predicting breaking strength. For maximum force prediction with 20% of the data, the kernel ridge, support vector machine, and Lasso regression models resulted in R ² values of 0.975, 0.960, and 0.930, respectively. However, the prediction accuracy for UTS and ultimate strength at break is lower, with the Elastic Net model achieving R ² values of 0.703 and 0.682, respectively, with 20% of the data. These findings suggest that while certain mechanical properties can be accurately predicted using data solely from the elastic zone, minimising specimen waste and testing time, the prediction of other properties requires data beyond the elastic limit. This study highlights the potential of machine learning to optimise and streamline the quality control process in additive manufacturing.

Program synthesis is the process of automatically generating software from a requirement specification. This paper presents a systematic literature review focused on program synthesis from specifications expressed in natural language. The research problem centers on the complexity of automatically generating accurate and robust code from high-level, ambiguous natural language descriptions – a barrier that limits the broader adoption of automatic code generation in software development. To address this issue, the study systematically examines research published between 2014 and 2024, focusing on works that explore various approaches to program synthesis from natural language inputs. The review follows a rigorous methodology, incorporating search strings tailored to capture relevant studies from five major data sources: IEEE, ACM, Springer, Elsevier, and MDPI. The selection process applied strict inclusion and exclusion criteria, resulting in a final set of 20 high-quality studies. The findings reveal significant advancements in the field, particularly in the integration of large language models (LLMs) with program synthesis techniques. The review also highlights the challenges and concludes by outlining key trends and proposing future research directions aimed at overcoming these challenges and expanding the applicability of program synthesis across various domains.

The Internet of Things (IoT) has become one of the most popular technologies in recent years. Advances in computing capabilities, hardware accessibility, and wireless connectivity make possible communication between people, processes, and devices for all kinds of applications and industries. However, the deployment of this technology is confined almost entirely to tech companies, leaving end users with only access to specific functionalities. This paper presents a framework that allows users with no technical knowledge to build their own IoT applications according to their needs. To this end, a framework consisting of two building blocks is presented. A friendly interface block lets users tell the system what to do using simple operating rules such as “if the temperature is cold, turn on the heater.” On the other hand, a fuzzy logic reasoner block built by experts translates the ambiguity of human language to specific actions to the actuators, such as “call the police.” The proposed system can also detect and inform the user if the inserted rules have inconsistencies in real time. Moreover, a formal model is introduced, based on fuzzy description logic, for the consistency of IoT systems. Finally, this paper presents various experiments using a fuzzy logic reasoner to show the viability of the proposed framework using a smart-home IoT security system as an example.

In recent years, Blockchain-based systems have experienced rapid growth. Although these systems are in production, they are not exempt from presenting defects in the design of their elements such as Smart Contracts. Design defects in Smart Contracts lead to inconsistencies and conse-quently to incorrect operation, which generates problematic situations during and after the execution of the system. In this paper, we describe an overview of main current approaches to formally verify Smart Contracts. Moreover, it is proposed to use Descriptive Logics to verify the consistency of functionality in the designs of Smart Contracts. The balance between expressiveness and computational complexity of Descriptive Logics, allow to model in an unified framework elusive Smart Contract properties, such as temporal and spatial ones. Furthermore, it will allow reliable and efficient verification of these properties.

En este artículo se describe el estado del arte sobre el análisis de redes sociales. Así mismo, se propone un estudio para la identificación de patrones de ubicación de publicaciones en redes sociales con respecto al nivel de contagios COVID-19 en determinadas zonas de la Ciudad de México.