In today’s rapidly accelerating digital transformation landscape, shopping malls face significant challenges stemming from changes in consumer behavior, the rise of e-commerce, and the impact of the COVID-19 pandemic on in-person visits. This situation has led to a decline in foot traffic, shorter dwell times, and increased difficulty in making timely, data-driven decisions.To address this problem, we propose a Big Data platform focused on dynamic customer segmentation, aimed at enhancing the visitor experience and optimizing commercial management within physical retail spaces. The solution leverages Amazon Web Services (AWS) cloud technologies and integrates multiple data sources, including Wi-Fi networks, captive portals, and sensors, to identify real-time behavioral patterns.The platform was developed using the agile Scrum methodology across twelve sprints, enabling an iterative implementation validated in development, testing, and production environments. Deployment was carried out in two shopping malls from Chile and one from Colombia, utilizing real customer data captured through the mall’s technological infrastructure. However, the specific names of the malls cannot be disclosed due to confidentiality agreements and company policies. To enable the public release of the models and datasets, all identifying information has been anonymized.Validation was conducted using key performance indicators such as average monthly visits, average dwell time, and Net Promoter Score (NPS). Short-term post-deployment measurements showed increases of 5%, 30%, and 1 point, respectively, compared to the immediate pre-deployment baseline. However, when comparing long-term trends between 2019 and 2021, average dwell time showed an 8.98% decrease, likely reflecting post-pandemic shifts in consumer behavior rather than a limitation of the implemented system. We conclude that scalable platforms and data-driven segmentation can effectively address the challenges of the commercial real estate sector, paving the way for predictive models and artificial intelligence for advanced personalization.

Contrary to popular belief, rare genetic diseases affect a significant portion of the global population, with a prevalence ranging from 3.5% to 8 %. These conditions are particularly prevalent among children. In Chile, the interval between the onset of symptoms and the diagnosis of these diseases can extend between six and eight years, resulting in significant emotional and economic costs for affected families. The primary reason for this delay is the dearth of knowledge about these diseases among neuropediatricians. To address this issue, we propose the implementation of a clinical decision support system (CDSS) called Diagen-AI which infers the condition or disease based on information from the child's phenotype (symptoms and signs). In addition to aiding in the diagnosis, the system offers suggestions regarding potential tests and facilitates the integration of Chilean physicians' expertise with statistical data on clinical conditions documented in Orphanet and insights from scientific literature, a novel approach for this type of solution. Diagen-AI operates by employing a Bayesian network to estimate the posteriori probability associated with the likelihood of a given condition based on observed symptoms. Testing recommendations are derived from the estimation of the impact of incorporating a new test as supplementary evidence in the prediction of conditions. The validation of Diagen-AI was conducted through the generation of synthetic data. Preliminary results were successful, for example, if the algorithm is informed with 50% of the symptoms, a correct diagnosis is achieved in 80% of the cases. With respect to the recommendation of tests, it is verified that on average 3 visits to the doctor (with 3 tests per visit) are required to achieve a correct diagnosis in 80% of the cases. We believe that Diagen-AI will be a valuable tool to shorten diagnostic periods, reducing the suffering and uncertainty of affected families by generating synthetic data.

Falls in the elderly population have become a public health problem worldwide, since they represent one of the main causes of disability. Automatic identification of the possible danger of falls would help prevent them before they happen. In this research, an alternative approach is proposed as an automated solution, based on the continuous monitoring of the person for a day, as a “Holter” type recording of movement patterns along with the correlation of the intrinsic and extrinsic factors that predispose to a greater risk of falling. The crossing of all the variables recorded and associated with the risk of falls will allow better preventive decisions to be made against them, reducing their morbidity and, at the same time, the costs and burden of the associated health services. It is proposed to design and implement a smartphone application as a scientific and technological solution that allows solving the problem of estimating the risk of falls in older adults.

Various factors, such as hydration levels, urinary tract diseases, prostatic hyperplasia, neurological disorders, medications, diabetes, and renal failure, can affect urination. This article explores the possibility of continuously evaluating urinary health using IoT technology by employing a contact microphone attached to the outside of the toilet bowl to record the acoustic patterns of urination for subsequent analysis. The performance of several algorithms for detecting urination patterns was investigated. Acoustic recordings were divided into segments of different sizes, from which 11 features were extracted. Support Vector Machines (SVM) were then used to assess the algorithm's effectiveness with various combinations of features and segment sizes. The aim of this study is to investigate the effectiveness of different methods for detecting acoustic patterns of urination, providing a range of algorithmic alternatives adaptable to the available processing capacity for detection.

Securing microservice applications is crucial. Security smells denote symptoms of bad –often unintentional– design decisions, which may result in violating security properties, and that can be resolved via refactoring. Stakeholders take into account the services’ business value, problem criticality, and available resources to decide which smells to resolve or leave alone, but making such decisions is inherently complex for microservice applications with many services, possibly affected by multiple security smell instances. Borrowing from hospital emergency room triage practices, which assign an urgency code to incoming patients, this paper introduces the notion of urgency for microservice security smell instances, and proposes the TriSS method to triage them. TriSS enables assigning to each security smell instance with an urgency code based on combining the services’ business relevance and the smells’ impacts on security and other quality attributes, e.g., performance and maintainability. The practical applicability of TriSS is illustrated with a use case based on a third-party microservice application, and its usefulness is evaluated with a controlled experiment involving 26 practitioners. The experiment’s results suggest that TriSS eases the triage process and yields urgency codes on which practitioners are more confident.

Microservices gained momentum in enterprise IT, as they enable building cloud-native applications. At the same time, they come with new security challenges, including security smells, viz., symptoms of bad (though often unintentional) design decisions that might affect application security. This study aims to explore the impacts of microservice security smells- and of the refactorings known to mitigate their effects-beyond security. In particular, we systematically elicit possible impacts of smells and refactorings on applications' maintainability, performance efficiency, and adherence to microservices' key design principles. We then validate the elicited impacts by means of an online survey targeting experienced practitioners and researchers. Our main contributions include 35 validated impacts, and a discussion of the survey results geared towards analyzing the (mis)alignment between practitioners and researchers.

Panoramic radiography is a routine technology used to diagnose oral and maxillofacial pathology; it has the advantages of low cost, high speed, and safety because of the reduced dose and exposure time to radiation. Third molars are responsible for benign or malignant tumors that can result in neurological problems, diminish the resistance to fractures owing to their position in the bone and frequently generate infectious problems. The appropriate planning and classification allow patients to be treated safely and help reduce waiting lists. We used panoramic radiography and machine learning to identify the inclination, depth, and available space of third molars to support diagnosis and surgical treatment. In experiments, our approach identified third molars with 100% accuracy The molars were classified as per the Winter and Pell–Gregory classification schemes having high accuracies of >80%. The contribution of this work adds value to the imaging diagnosis, allowing an estimation of the disinclusion time of third molars to support the surgical management of the operating room.

The paper deals with medical temporal scales based on both the chronologic time of the physician who observes the patient, the proper inner time of the patient (related to the circadian rhythms of his major physiologic variables) and the disease duration, estimated here by using the hospital stay (for this pathology) duration. We show that patients inside homogeneous Diagnosis Related Groups can be treated through a network of co-expertise identified by using Internet facilities and based on medical co-publication networks.