Optimization is an entire field that aims to improve efficiency and effectiveness across various domains. Its primary objective is to minimize costs, time, and risks while maximizing gains, quality, and efficiency. In this context, the 0/1 1-D bin packing problem is one of combinatorial optimization’s most challenging and extensively studied problems. This problem holds significant practical applications in supply chain management, packaging design, and resource optimization. This work solves the 0/1 1-D bin packing problem using a nature-inspired golden eagle optimizer. The hunting behavior of golden eagles inspires this bio-solver, and it employs swarm intelligence-based strategies to approximate solutions. We perform a comparative analysis of the bio-inspired algorithm to evidence its yield. We use twenty instances of the 1-D bin packing problem. Computational results show that the golden eagle optimizer exhibits better results in convergence time than well-known bio-inspired algorithms.

This paper focuses on modeling and solving the Manufacturing Cell Design Problem (MCDP) by using the Harmony Search (HS) metaheuristic. The MDCP consists on grouping machines and parts that they process, into groups called cells. So, the idea is to identify an organization of cells such that the number of times that a piece is transported between these cells is minimized. To this end, we use the HS optimization algorithm, which is based on the process of improvisation performed by musicians to find a perfect musical harmony. The experimental results demonstrate the efficiency of the proposed approach which is able to reach all global optimums for a set of 90 well-known MDCP instances.

The COVID-19 outbreak has shown that Modeling and Simulation (M&S) methodologies are an important aspect to study the spread of the disease and assess the effect of different measures to diminish its negative effect. Although traditional models have been widely used, there is a need to build new, highly configurable disease models to explore multiple scenarios quickly. We present an M&S framework to perform rapid prototyping of pandemic spread using the Cell-DEVS space-based discrete-event modeling approach. This method supports age segmentation of the population, hospital-capacity-dependent deaths, and enforcing mobility restriction policies. This method is useful for studying the spread of the disease, as well as combining the simulation results with different visualization tools.

The Model-Driven Development paradigm aims to represent all the information system features through models. Conceptual-Model Programming offers a similar approach, but with a focus on automatic code generation. Both approaches consider modeling and traceability of different abstraction levels, where each level can be tackled with different modeling methods. This heterogeneity introduces a challenge for the quality of the traceability and transformations among models, especially when aiming for automatic code generation. In this paper, we introduce a holistic conceptual-model programming method to generate code from different abstraction levels (from the problem space to the solution space), through three modeling languages whose consistency has been ontologically ensured by two transformation techniques. Particularly, we focus on transformations from the strategic layer using i*, to business process layer using Communication Analysis (CA), and to the system conceptual model layer with OO-Method, which can automatically generate fully functional systems. Even though there are previous works that have proposed partial transformations among these modeling methods, this paper is the first one that deals with the perspective of putting together all the models in a single development method. For each transformation, we discuss what parts can be automatically performed and what parts need human intervention.

Detecting and extracting findings in a radiological report is crucial for text mining tasks in several applications. In this case, a labeled process for the image associated with the radiological report in mammography and Spanish context for a computer vision model is required. This paper shows the methodology and process generated for this goal. This paper presents a Named Entity Recognition (NER) approach based on a transformer deep learning model, using a labeled corpus and fine-tuning process to find three concepts that compose a typical finding in a mammographic radiological report: laterality, location, and the finding. We add another concept in the labeled process, the negation, necessary to identify falses positive inside the text that writes the radiologist. Our model achieves an F1 score of 88.24% classifying the three principal concepts for a finding, product of the labeled and fine-tuning process. The results presented here will be used as input for future training work on a computer vision model.

In datasets, the preponderance of imbalanced classes impedes accurate cyberattack categorization. While high aggregate accuracy is sought, it's paramount to adeptly classify all attack types, especially the under-represented ones. Existing methodologies, such as Ensemble techniques and the Synthetic Minority Oversampling Technique (SMOTE), address these disparities, yet the dynamic nature of underrepresented cyberattacks in cybersecurity remains a concern. To address this, we introduce a nested cascade model tailored for diverse cyberattacks within imbalanced datasets. This model leverages binary classifiers across tiers, each targeting a specific attack type. Before initializing the cascade, SMOTE is applied to counterbalance class disparities. The cascade's classification sequence employs a dual strategy: an initial one-vs-all binary classifier approach for pending classes, followed by prioritization based on model performance. We assessed our approach using the UNSW-NB15 dataset. Preliminary results indicate approximately 80% efficiency across metrics like accuracy, recall, and Fl-score. Notably, SMOTE's in- tegration yielded significant improvements for underrepresented classes.

Osteoporosis is a skeletal disorder characterized by low bone mass, which compromises its resistance and increases the risk of fractures, and is a widespread problem worldwide. Currently, the gold standard for assessing fracture risk is the measurement of the areal bone mineral density with Dual-Energy X-ray Absorptiometry. Several ultrasound techniques have been presented as alternatives. It has been shown that the estimation of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission, are associated with non-traumatic fractures in postmenopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. However, this model-based inverse approach tends to fail for the patients associated with poor guided mode information. A recent study has shown the potential of an automatic classification tool, Support Vector Machine, to analyze guided wave spectrum images independently of any waveguide model. The aim of this study is to explore how the classification accuracy varies with the number of features. Optimization was done using the Particle Swarm Optimization algorithm, while adjustment was made considering age, body mass index, and cortisone intake. The results show that adjusting the data and optimizing the parameters improved classification. Moreover, the number of features was reduced from 32 to 15, with 73.5% accuracy comparable to the gold standard.

Advances in health technologies have been proposed in studies aimed at improving the quality of life of older people. Ambient assisted living supports elderly adults and people with special needs during their daily activities. This concept aims to maintain the independence of users, giving them autonomy in daily life activities in addition to increasing their quality of life, well-being, and safety. In this work, two tools that are part of a ADL SoS-based Platform are presented: one low-cost sensor to detect nocturia and a complete module for identifying fall potential. Experimental results show that our work can be implemented in ambient assisted living as smart-home devices, in laboratories, or any other place, and that it is competitive compared to other developed tools.

Data protection regulations impose requirements on organizations that require interdisciplinary. Conceptual modeling of information systems, particularly goal modeling, has served to communicate with stakeholders of different backgrounds for software requirements analysis. An extension for a Socio-Technical Security (STS) modeling language was proposed to include data protection modeling concepts to help represent relevant issues of the European Union’s General Data Protection Regulation. This article examines whether models designed with this extension serve as communication facilitators for privacy compliance and common ground across stakeholders.

Query optimization strategies is an important aspect in database systems that have been mainly studied only from the perspective of reducing the execution time, neglecting the analysis of their impact on energy consumption. We perform an empirical evaluation for understanding the impact of end-to-end query optimization strategies on the power consumption of database systems, from both client and server perspectives. We perform tests over a PostgreSQL database for two optimization strategies (i.e., indexation and data compression) using the TPC-H benchmark, configured with 22 queries on a 1GB dataset. To measure the energy consumption of both client and server, we propose Juliet, a C++ agent for monitoring and estimating Linux processes energy consumption in Joules (J). Experimental results show that indexation is more effective than data compression to reduce the energy consumed by the execution of the majority of the 22 queries tested.

Nowadays, the vast majority of university careers declare a competency-based curriculum. However, having concrete evidence to measure learning outcomes can be a complex problem in engineering. Academics make great efforts to design active learning experiences to stimulate skills development. However, more evidence is required to describe methodologies integrated with technological tools to evaluate learning outcomes. In this paper, we propose using the Lego Serious Play (LSP) methodology and Multimodal Learning Analytics (MMLA) techniques to assess learning outcomes in the Software Engineering I course. The results obtained are quite promising since it was possible to identify difficulties in acquiring and applying knowledge.

Craft fairs are spaces where artisans can exhibit their artistic pieces and interact with the public, however this activity is always affected by external factors, such as weather, stoppages, health situation (due to covid-19), among others. This proposal is an innovative contribution to this problem that makes technology available to support this sector. Through a Web platform that incorporates a 3D interface, the craftsman will reach his audience without having to leave his home, since it simulates the environment of a craft fair where you can browse and see the crafts in its 360°, admiring each detail as if the user were in person. The technological infrastructure includes a catalog of all the pieces offered by the artisans and contact details. A filter facilitates the search for parts according to established criteria. The proposal has validations and various functional tests that show the technical feasibility and usability tests that demonstrate its usefulness and ease of use.

Finding real-world applications whose records contain missing values is not uncommon. As many data analysis algorithms are not designed to work with missing data, a frequent approach is to remove all variables associated with such records from the analysis. A much better alternative is to employ data imputation techniques to estimate the missing values using statistical relationships among the variables.

Stroke (CVA) is the second leading cause of death and the third leading cause of disability in the world. A large proportion of adults who survive such an accident can recover; however, this is achieved after going through a long process of kinesiology rehabilitation. In recent times, there are numerous advances in medical equipment, systems and applications that allow a progressive recovery from the consequences caused by this type of accident. The purpose of this work is to analyze the state of the art in which kinesiological rehabilitation technologies are found, through a systematic review of the literature, which also allows to determine the effectiveness of the application of these technologies in the recovery of patients. Motor rehabilitation technologies, based on virtual reality environments and robotic equipment, were identified for adult patients who present hemiparesis or hemiplegia. A great variation was found in the treatments and scales used for its evaluation, in the motor functionality of the upper and lower limb. In the case of robotic technologies, improvements in the range of motion of the limbs, gait and functionality of the upper limbs and muscular endurance stood out. Also, it highlights the adherence of patients to therapies with virtual reality technologies, since these were partly stimulating and enhanced positive aspects such as self-esteem. The work represents a contribution to engineers, who are interested in specializing in patient rehabilitation technologies.

The aim of this study was to update the Chilean male workers’ anthropometric database to be used for design purposes and to examine the secular changes observed in a group of anthropometric variables for Chilean male workers. Data collection involved a sample of 2,346 male workers with ages ranging from 18 to 65+, assigned to the Mutual de Seguridad C.Ch.C in the two most populated regions of Chile (Valparaíso and Metropolitana) distributed in nine economic activities branches. Data collection was performed by two teams of 3 physiotherapies each. Before starting the collection process, the measurement teams underwent a training session of one week that included a theoretical session on the basic concepts of anthropometrics, as well as some practical instructions. A total of 32 anthropometric measures were gathered following the ISO standard 7250-1. Before applying the data, a checking process was carried out, trying to identify errors related to the data collection process. The magnitude of the positive secular trend for the stature was 1.0 cm per decade and the highest value was found for shoulder breadth, with a positive increase of 2.7 cm per decade. The results obtained allowed to observe the existence of a positive secular trend for most of the selected body measurements. Finally, it was also possible to verify that the current research may be used for design purposes and to establish the baseline data for long-term observation regarding anthropometric changes among Chilean male workers.

In Chile, a high number of hospital establishments are in the project, design and construction stages, and due to its complexity, the use of the BIM methodology constitutes an opportunity to take advantage of all its potential for the benefit of the timely and good results of these works, especially considering its social impact. This paper examines the definitions and functionalities of BIM within hospital projects and presents some cases of hospital projects that have used the methodology to obtain the benefits it offers. And the contribution that biomedical engineering can make to the comprehensive development of projects. It concludes with different aspects to consider for the successful and multidisciplinary implementation of the BIM methodology in hospital projects.

This article presents the result of applying the Cremme filter to the radiographic images of the knee. In addition, texture indices and the invariant moment of each image are calculated, observing that an index n of the analytical form of the filter has an effect on the resulting images. In particular, the contrast and the ISM, as the univariate moment 4 at n =4 presents a minimum. This can improve the diagnosis for the detection of osteoarthritis of the knee, giving an index that can be studied to optimize the application of the filter.

Collaboration is a skill recognized as essential for professional development for educational institutions and employers. However, development and assessment remain complex, because the conditions must be in place to foster collaboration and individualize aspects of the behavior of each member of the collaborative activity. This article presents a framework for real-time evaluation of collaboration in face-to-face environments based on nonverbal communication. The framework considers collecting voices and videos from members and delivering visualizations based on six collaboration constructs. In addition, an implementation of the framework was designed and developed for validation with education professionals. The results suggest that the proposed visualizations might be helpful for educators to get insights into the cognitive contributions and team integration in collaboration.

Users with Autism Spectrum Disorders (ASD) show great interest in, and operate with facility, technological devices like smartphones and tablets. As a result, the number of applications specially developed for these kinds of users keeps growing. Nevertheless, the creation of an application that adapts to user abilities is not a straightforward process. This article focuses on identifying the optimal target size and drag distance that developers and designers can use when creating applications for users with ASD to allow for easier interaction of users with screen elements. In the experiment performed, different target sizes and drag distances were compared. Based on the results, we suggest that 57 pixels is the minimum target size to support the interaction of level 1 and 2 for users diagnosed with Autism Spectrum Disorders. These results can be used as guidelines for interaction designers of mobile applications for autism. Nevertheless, the creation of an application that adapts to user abilities is not a straightforward process, because users with these conditions have significant sensory-motor problems.

The well known asymmetry of type Ia supernova data is used to infer general features of an alternative model for dark energy. Using a model that allows DE to vary with redshift as a probe, the results indicate the low redshift transition of the deceleration parameter q(z) is evident only taken data points from one hemisphere of the sky. A related effect is a reconstructed DE density X(z) that shows evolution (it decreases as redshift increases) only towards the hemisphere where q(z) manifest the transition. We discuss on possible theoretical frameworks where these effects can be explained.