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Recent Submissions
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Exploring the Seismic Performance of Confined Brick Masonry Walls via Diverse Toothing Connections: A Numerical Investigation(American Society of Civil Engineers (ASCE), 2026-02-01)Confined brick masonry (CBM) combines masonry walls with reinforced concrete ties for enhanced structural integrity. The wall-to-tie connection is essential for effective load transfer, preventing out-of-plane failure, and enhancing ductility. Introducing tie-columns into masonry walls through various toothing connections is crucial. However, previous research and guidelines do not provide clear insights into their specific contributions, making it difficult to accurately assess their impact. Addressing this gap, our study employed a robust numerical approach, utilizing an integrated finite element macromodel that treated wall and tie members as a single entity, thereby improving computational efficiency. Additionally, the study applied the concrete damage plasticity model to predict damage progression in CBM walls and performed pushover analysis to evaluate the seismic performance of various toothing schemes in CBM walls. An extensive parametric study was conducted to compare various toothing schemes, evaluate the optimal horizontal and vertical projections of tooth, assess the impact of height-to-thickness ratio on toothing schemes, and investigate the effect of openings on the performance of toothing schemes in CBM walls. This research also assessed the severity of damage encountered by CBM walls, providing insights into crack propagation and distribution and emphasizing the significance of its design. This study highlights the critical role of toothing schemes in the seismic performance of CBM walls, with the machine-made toothing schemes demonstrating superior results. These schemes significantly enhanced ultimate strength, stiffness, and energy absorption compared to handmade, horizontal reinforcement, and no-tooth options. The research also quantified the positive correlation between increased wall thickness and improved structural resilience, particularly when paired with machine-made toothing. Furthermore, the study identified the adverse effects of wall openings on seismic performance, emphasizing the importance of precise tooth size and arrangement. Notably, a 100-mm vertical projection was shown to offer the most effective seismic performance, providing valuable, data-driven guidelines for the design of earthquake-resistant CBM structures.
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Enhancing Minimarket Customer Experience Through YOLOv8-Powered Checkout Systems(Springer Science and Business Media Deutschland GmbH, 2026-01-01)In Lima, Peru, minimarkets are vital, providing essential goods to a growing population. However, slow payment processes lead to long lines and frustrated customers, impacting satisfaction and profitability. The main issue is the slow, error-prone manual item scanning at the checkout. Addressing this inefficiency can enhance economic impact, customer satisfaction, and operational efficiency. Despite the benefits, implementing object detection technology faces challenges such as technological complexity, integration issues, diverse product ranges, and high costs. Previous solutions failed due to inadequate technology, high costs, poor integration, and user resistance. This paper proposes using YOLOv8, a state-of-the-art object detection model, for its precision, real-time processing, cost-effectiveness, and easy integration. This work includes custom hardware, an integration layer, and a user interface, with the aim of reducing checkout times, achieving over 94% product recognition accuracy, and improving customer satisfaction. Initial tests show promising results in speed, accuracy, and customer feedback.
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IoT System Based on Deep Learning for the Identification and Feedback of Work Postures When Using a Computer(Springer Science and Business Media Deutschland GmbH, 2026-01-01)It is common for office workers, mostly dedicated to IT, to present musculoskeletal pain in the back, neck and shoulders due to poor posture practices they adopt while doing their work in front of the computer for long periods, this is known as forced postures. Our main work seeks to implement an IoT system with force sensors, model RP-S40-ST, based on the use of classification algorithms and deep learning techniques for the identification and correction of postures through feedback. Ten classification algorithms were used for training and validation of the model, with the Logistic Regression algorithm achieving the highest accuracy rate being .8794 and .9052 respectively.
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Mitigating Information Leakage in Tech-Sector SMEs: Implementing ISO 27001:2022 for Comprehensive Security(Springer Science and Business Media Deutschland GmbH, 2026-01-01)This paper presents a model for implementing an Information Security Management System (ISMS) based on ISO 27001:2022 tailored to the needs of small and medium-sized enterprises (SMEs) in the technology sector in Lima Metropolitana. The model focuses on mitigating data leakage, a critical issue exacerbated by the increasing digitization of business operations. The proposed framework integrates controls from ISO 27001 aligned with NIST SP 800-53 to enhance information security practices. Results from applying the model to two technology SMEs indicate that one company (Company A) achieved a 94.44% Critical Control Implementation Index (IICC), a 70% Critical Vulnerability Resolution Rate (TRVC), and an 85% Policy Compliance Rate (TCPS), while the second company (Company B) achieved significantly lower rates of 50%, 40%, and 60%, respectively. These findings highlight both strengths in technological controls and weaknesses in organizational security management. This research contributes to the field by providing a practical, scalable approach for SMEs to enhance their information security posture, addressing both human and technological factors.
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Unveiling Injustice: Analyzing Child Mortality Inequality across decades in Peru (1981–2017)(Elsevier Ltd, 2026-01-01)Peru is a developing country that has significantly improved the average of almost all health indicators. Specifically, in the past four decades, child mortality decreased tenfold. However, the same is not necessarily true of equality, which remains a challenge. Using microdata from Peru's population censuses in 1981, 1993, 2007, and 2017, we estimate the inequality in child mortality across different social groups. We estimate differences between ethnic groups, education levels, wealth quintiles, regions, and urban–rural groups and find that although inequality has decreased, it remains significantly high. The data show that inequality in child mortality increased between 1981 and 1993, declined between 1993 and 2007, and then increased between 2007 and 2017. Differences in education are the most crucial factor, associated with 45 % of the inequality in 1981 and 58 % in 2017. Differences between Lima and rural areas account for 27 % to 30 % of the inequality, while ethnicity contributes only 6 % in 1981 and 10 % in 2017.
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A Thorough Evaluation of Demand Prediction Models: Machine Learning, Deep Learning, and Statistical Techniques for Import Businesses(Springer Science and Business Media Deutschland GmbH, 2026-01-01)Nowadays, managing demand in companies is crucial to avoid storage overcosts, stockouts and to improve the service level of companies. To address this scenario, demand predictions through models and algorithms emerge. Therefore, this research aims to evaluate the performance of seven prediction techniques applying machine learning, deep learning, and statistical methods. To validate our experiments, we used Dickey–Fuller, Shapiro–Wilk, Friedman, and Wilcoxon post-hoc statistical tests on the predictions of the models using demand records from a Peruvian import company. The results indicated that deep learning and statistical models have significantly better predictions than machine learning models. In particular, the LSTM, CNN, ARIMA, and Holt-Winters models significantly improve accuracy compared to the Ridge Regression, Random Forest Regressor, and Decision Tree Regressor models. Compared to machine learning models, statistical and deep learning models improve accuracy in a range from 66.01 to 86.10%. These results highlight the statistical advantage of deep learning and statistical models in demand prediction, with the LSTM model showing the lowest error.
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Patients with Chronic Kidney Disease and Advance Health Care Directive Registration(Springer Science and Business Media Deutschland GmbH, 2026-01-01)Advance healthcare directives planning entails the development of an understanding, reflection and communication process among patients, family members and healthcare team to accurately clarify patients’ preferences and identify surrogates, especially in end-of-life scenarios. The purpose of this study was to analyse the perceptions of patients with chronic kidney disease undergoing haemodialysis at Hospital Abraham Godoy Peña de Lautaro regarding the intention to register their advance healthcare directive. A phenomenological approach was applied to understand the meaning of the experiences of the subjects involved. Theoretical sampling was used, and 14 semi-structured interviews were conducted. Data were processed using thematic analysis. Patients with stage 5 chronic kidney disease undergoing haemodialysis were aware of the importance of advance healthcare directive registration; however, the registration process and should be guided by values-based education techniques. Interviewees prefer therapies aligned with their lifestyles, family impact, psychological comfort, and inclusion, rather than focusing solely on clinical results.
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Empirical Experiment to Validate Guidelines for Video Game Users With ADHD(Education Society of IEEE (Spanish Chapter), 2026-01-01)Current video game designs often include features that distract users, a problem exacerbated for individuals with Attention Deficit Hyperactivity Disorder (ADHD). To mitigate distractions for this population, specific design guidelines have been proposed. This study evaluates the benefits of applying such guidelines in the software development process of video games for users with ADHD. A controlled experiment was conducted with 16 subjects interacting with two versions of a video game: one designed according to the guidelines and another without them. Metrics analyzed include perceived effort and user satisfaction, measured through Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Intention to Use (ITU). Results indicate statistically significant differences in effort and satisfaction; users engaging with the guided design reported lower cognitive effort and higher satisfaction. These findings suggest that integrating ADHD-focused design guidelines enhances game accessibility and improves user experience for this demographic.
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Influence of economic globalisation on growth and environmental quality in Pacific Alliance countries(Cogent OA, 2026-01-01)This article examines the dual impact of economic globalisation on economic growth and environmental quality within the Pacific Alliance—Chile, Colombia, Mexico and Peru—between 1990 and 2022. Employing a quantitative longitudinal approach, it applies panel data econometric techniques, specifically Fixed Effects models, complemented by robustness checks using Random Effects and Pooled Ordinary Least Squares estimations. Panel unit root tests (Levin–Lin–Chu; Im–Pesaran–Shin) confirmed data stationarity, and the Hausman test validated the suitability of the Fixed Effects estimator. The results show that trade openness exerts a positive and statistically significant effect on real economic growth, suggesting that deeper integration into global markets enhances productive efficiency, diversifies exports and improves resource allocation. By contrast, foreign direct investment does not exhibit a significant relationship with growth, owing to institutional weakness, limited infrastructure and the shallow financial systems that characterise the region. Regarding environmental outcomes, trade openness increases carbon dioxide emissions, indicating that liberalisation stimulates production while exacerbating environmental degradation in contexts with insufficient regulatory capacity. Foreign direct investment likewise shows no significant effects, possibly due to sectoral heterogeneity. The study concludes that the Pacific Alliance must strengthen environmental governance and guide trade and investment towards sustainable development.
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Model for Monitoring the Pest Carmenta Foraseminis in Cocoa Crops Through Environmental Parameters(Springer Science and Business Media Deutschland GmbH, 2026-01-01)Cocoa consumption has increased in recent years as it has a significant impact on the food and cosmetic sector. It is therefore important to maintain and improve agricultural productivity, which makes it urgent and necessary to improve management techniques, including fertilization and crop and plantation protection practices. However, intensive cocoa production in Peru and South America faces challenges for cocoa production. The most decisive is the attack of the “mazorquera” pest (Carmenta foraseminis), which infests the fruit inside without giving the possibility of early detection and, consequently, the quality and intensity of the harvest is often diminished. The objective of this work is the development and implementation of a method to monitor and detect the “mazorquera” pest in the cocoa crop, by obtaining data in real time and analyzing them. In this sense, the proposal involves the use of sensors to monitor certain environmental variables that correlate with the behavior of the pest “mazorquera” from certain artificial intelligence algorithms. The use of intelligent algorithms decreases the analysis time, the error rate and increases the accuracy in the decision-making process. The proposed method proves to have the relevant correlation between environmental conditions and the behavior with crop pests, allowing its timely detection to intervene quickly and effectively. The proposed procedure is not limited to the improvement in crop management, it also creates the conditions for improved traceability of information, improving cocoa production, making it more competitive and sustainable.
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The KUYUY Accelerograph and SIPA System: Towards Low-Cost, Real-Time Intelligent Seismic Monitoring in Peru(Multidisciplinary Digital Publishing Institute (MDPI), 2026-01-01)Accelerographs are essential instruments for quantifying strong ground motion, serving as the foundation of modern earthquake engineering. In Peru, the first accelerographic station was installed in Lima in 1944; since then, various institutions have promoted the expansion of the national network. However, this network’s spatial coverage and instrumentation remain insufficient to properly characterize strong motion and support seismic risk reduction policies. In this context, the KUYUY accelerograph is presented as a low-cost, low-noise device equipped with real-time telemetry and high-performance MEMS sensors. Its interoperability with the Intelligent Automatic Processing System (SIPA) enables real-time monitoring and automated signal analysis for seismic microzonation studies and rapid damage assessment, contributing to seismic risk reduction in Peru. The validation process included static gravity calibration, field comparison with a reference accelerograph, and an initial deployment in Lima and Yurimaguas. The results demonstrate the proposed accelerograph’s linear response, temporal stability, and amplitude consistency with respect to high-end instruments, with differences below 5–10%.
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IoT-based air pollution monitoring system: A case study in Artisanal Brick Kilns(Taylor and Francis Ltd., 2026-01-01)The rapid growth of industrial activities has led to a significant increase in environmental pollution. Exposure to these air pollutants negatively impacts health, quality of life, and contributes to climate change. It is essential to develop accessible and low-cost mechanisms for controlling polluting emissions. This study presents an IoT-based air pollution monitoring system implemented in an artisanal brick kiln, where manual processes and the use of fossil fuels generate emissions that can severely affect nearby communities, particularly due to the high concentration of fine particulate matter such as PM2.5, known to cause respiratory and cardiovascular problems. The system uses the SPS30 sensor to measure PM2.5 and PM10, connected to an ESP32 microcontroller, which transmits data using the MQTT protocol to the AWS IoT Core service, where they are processed and visualized in real time through a web application. During its validation, the system demonstrated high accuracy, obtaining a correlation of r = 0.95 for PM2.5 and r = 0.99 for PM10 compared to an official monitoring station. The experiment at the artisanal brick kiln confirmed that on 100% of the days monitored, PM2.5 concentrations exceeded the limit of 75 µg/m³, while PM10 exceeded 150 µg/m³ on 30% of the days evaluated. The system generates automatic alerts for excessive pollutants, facilitating a timely response and the implementation of corrective measures when pollution levels reach critical levels. The proposal presents a scalable and low-cost solution to improve environmental management across various industrial sectors.
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When Virality Builds Brands: The Power of eWOM in Digital Campaigns(Springer Science and Business Media Deutschland GmbH, 2026-01-01)This study analyzes the effectiveness of experiential marketing and the strategies of buzz marketing, Word of Mouth (WOM), and electronic Word of Mouth (eWOM) in Burger King’s 2024 “Kiss Day” campaign, aimed at improving brand perception and fostering consumer engagement. Using a qualitative case study approach, the research examined consumer experiences and perceptions through semi-structured interviews and social media content analysis. The findings show that the combination of positive emotions and active participation was associated with greater campaign’s virality and improved brand recall. However, the initiative demonstrated limited effect on long-term loyalty among non-regular customers. The study concludes that integrating experiential marketing with digital strategies can expand the reach and effectiveness of campaigns, offering key insights for future advertising efforts in the fast-food industry.
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Guidelines for Adapting a Corruption Peruvian Survey into the Ecuadorian Context(Springer Science and Business Media Deutschland GmbH, 2026-01-01)This study aimed to adapt the “Corruption Normalization Scale”, created for, and applied among Peruvian university students, to the Ecuadorian university context. To achieve this, cultural, idiomatic, and linguistic particularities of the latter country were considered. This is a cross-cultural adaptation study, through content validation and expert judgment. Ten multidisciplinary experts from different areas of knowledge were chosen. The experts agree on the relevance of the four criteria evaluated: Sufficiency, Clarity, Coherence, and Relevance. The results showed that a group of experts found it difficult to delimit the scope of the three dimensions (Fraud, Criollada, and Transgression). It is recommended to contemplate the idiomatic particularities of each country or area where the Scale is to be applied, to clearly and correctly transmit what is to be evaluated.
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Perceived Taxi App Quality: Understanding Consumer Behavior from Technology Continuance Theory(Springer Science and Business Media Deutschland GmbH, 2026-01-01)The increased use of smartphones has also expanded access to a wide range of applications aimed at facilitating everyday processes, purchases, or transactions. In this context, contemporary lifestyles demand greater use of such applications, particularly in the field of transportation, as they are consistently associated with enhanced safety and availability. This study aims to explain the influence of perceived application quality on perceived usefulness and user satisfaction, in order to foster an attitude that promotes continued use of a digital platform. A quantitative approach is employed, using structural equation modeling and the partial least squares technique (PLS-SEM), based on a sample of 473 valid responses. The results highlight perceived quality as a key predictor of user satisfaction, while attitude emerges as a critical antecedent of continued use of ride-hailing applications. The findings contribute to expanding the understanding of consumer behavior from the perspective of the Technology Continuance Theory, offering deeper insights about the factors that drive continued use intention within the taxi app.
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Service Quality or Interior Shop Environment? Drivers of Repurchase Intention in Healthy Products Stores(Springer Science and Business Media Deutschland GmbH, 2026-01-01)This study examines the effects of service quality and the store atmosphere on consumer satisfaction and experience in fostering repurchase intention within healthy food retail stores. A quantitative research approach was employed, based on a valid sample of 430 individuals who made purchases in health-focused stores over the past 3 months. Data analysis was conducted using structural equation modeling (SEM) and the partial least squares method (PLS-SEM) to test the proposed hypotheses, utilizing SmartPLS 4.0 software. The results highlight the critical role of service quality in shaping consumer experience, which in turn emerges as a key predictor of repurchase intention. These findings contribute to a deeper understanding of the impact of external stimuli on consumer behavior within physical retail environments, particularly in the realm of specialized retailing. Furthermore, the study provides valuable insights for marketing managers regarding the significance of service quality and store atmosphere in influencing customers’ purchase decision-making progress.
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Virtual Reality as a Resource in Educational Methodologies: Experiences and Perceptions of English Students at Keep Going VR(Springer Science and Business Media Deutschland GmbH, 2026-01-01)This study investigated the application of virtual reality as a resource in educational methodologies within the English courses at KeepGoing VR. The objective was to analyze its implementation, perceptions, and effects on English language learning. A qualitative methodology with a phenomenological design was employed, using semi-structured interviews conducted with English students from KeepGoing VR. The results indicate that virtual reality facilitates more effective and meaningful English learning compared to traditional methods. Three key aspects were identified: adaptive learning, which allows for personalized teaching and flexibility; meaningful learning, where new knowledge connects with prior experiences, enabling students to internalize the language in a practical and highly relevant way; and the development of digital competencies, which significantly enhances both technological and communicative skills required today. It is concluded that virtual reality transforms English learning into a more natural and interactive experience, facilitating practice in realistic environments and the development of digital competencies.
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Maximizing Brand Image: the Power of Social Media Marketing for Boutique Gyms(Springer Science and Business Media Deutschland GmbH, 2026-01-01)In a key digital era for the business movement, Social Media Marketing (SMM) is a relevant strategy that influences customer perception of the brand. Therefore, this study examines the effect of SMM on the brand image (IMG) of a boutique gym with five locations in Metropolitan Lima (Peru) during 2025. Using a quantitative, explanatory research design, 150 active clients were surveyed. Tailored instruments were used to measure SMM dimensions (word of mouth, entertainment, interaction, modernity, personalization and perceived risk) and brand image (functional, affective and reputation). The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through the Smart PLS 4 software. The results show that the use of SMM has a positive effect on the perception of the IMG of the boutique gym. Specifically, entertainment and personalization emerged as the most influential dimensions. Conversely, word of mouth, interaction and modernity did not show a significant influence in this context. Finally, although perceived risk presented a significant relationship, its effect was negative. It is concluded that while SMM is a valuable tool for strengthening brand image, strategies should prioritize entertaining and personalized content, and re-evaluate approaches to interaction, word-of-mouth and modernity strategies are being developed, in order to minimize perceived risk and enhance the positive impact of SMM on brand perception.
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Facial Emotion Recognition in a Serious Game for Children with Autism(Springer Science and Business Media Deutschland GmbH, 2026-01-01)Recognizing and expressing emotions are some of the challenges experienced by individuals with Autism Spectrum Disorder (ASD). Therapy is often used to help children with this neurodevelopmental condition, to improve their emotional and social skills. Our focus is to teach emotions to children with ASD, in order to improve these abilities, by developing a mobile Serious Game and a Facial Emotion Recognition (FER) model to integrate in it. This application offers four main activities: a learning activity, two recognition activities and the imitate activity, which integrates the FER model, and were designed following indications from experts in the field.
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Automated Detection of Melanoma Through Dermoscopic Image Analysis(Springer Science and Business Media Deutschland GmbH, 2026-01-01)Currently, skin diseases are a public health challenge due to their prevalence and the difficulty in detecting and differentiating certain conditions. One of them is melanoma, which is a type of skin cancer, on the other hand, there is nevus and seborrheic keratosis, which are generally benign conditions, however, these can be confused with each other. In this context, this study presents an automated system to detect and classify between these three skin conditions using artificial intelligence techniques applied to dermoscopic images. To evaluate the performance of the model, metrics such as precision and accuracy were used, obtaining an accuracy of 79.1%. These results demonstrate that artificial intelligence has great potential as a support tool for dermatology professionals, allowing a rapid and precise detection of these skin conditions.