Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations. The relevant papers were subjected to analysis using the PRISMA method, and 72 articles that met the criteria for this research project were identified. A detailing of the most commonly used visual sensor systems, machine learning algorithms, human gait analysis parameters, optimal camera placement, and gait parameter extraction methods is presented in the analysis. The findings of this research indicate that non-invasive depth cameras are gaining increasing popularity within this field. Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. This review seeks to establish the foundations for future innovations that will facilitate the development of more effective, versatile, and user-friendly gait analysis tools, with the potential to significantly enhance human mobility, health, and overall quality of life. This work was supported by [GOBIERNO DE ESPANA/PID2023-150967OB-I00].

La absorción de nutrientes por el cultivo de pimienta negra (Piper nigrum) es fundamental para estimar su demanda nutricional y diseñar planes de fertilización eficientes. Este estudio evaluó la absorción de nutrientes en diferentes órganos de la planta al inicio de su producción (1,5 años) en un cultivo ubicado en la parroquia Valle Hermoso, Santo Domingo de los Tsáchilas, Ecuador. Se empleó el diseño completamente al azar simple, utilizando la prueba de Di Rienzo, Guzman y Casanoves para comparar medias (a = 0,05). Los tratamientos incluyeron los órganos de la planta, los macronutrientes y los micronutrientes, con tres repeticiones por tratamiento. Los principales resultados revelaron que hojas presentaron los mayores contenidos de nitrógeno, potasio y calcio (2,86 ± 0,18 a 3,14 ± 0,26 dag kg-1), mientras que las raíces tuvieron más de boro, zinc, cobre y hierro. De igual forma, las hojas acumularon la mayor cantidad de nitrógeno, calcio y azufre (1,00 ± 0,32 a 40,5 ± 3,1 kg ha-1), así como de boro, zinc y manganeso (32,6 ± 3,9 a 217,1 ± 21,5 g ha-1). La absorción de los macronutrientes por el cultivo siguió el orden nitrógeno = potasio = calcio > magnesio > fósforo = azufre, mientras que para los micronutrientes fue hierro > manganeso > zinc > boro > cobre. Estos resultados son relevantes para diseñar planes de fertilización que optimicen la disponibilidad y absorción de nutrientes en el cultivo de pimienta negra en su etapa inicial de producción.

The affectations caused by extreme events of natural origin such as droughts and floods in traditional homes in the province of Gran Chaco, in Bolivia, are frequent. These aspects compromise the habitat of the populations that occupy them, as is the case of the original Weenhayek people, as an alternative for the improvement of the human habitat of this town. Through theoretical and empirical methods, five variables used for the development of the adaptation model were determined, from the bases of planned adaptation as a component of urban-territorial resilience, in search of an improvement of socio-environmental systems in the face of the effects of climate change, exemplified in the Weenhayek native people. The model establishes the improvements of traditional dwellings, from a current trend of deterioration to one of preservation, conservation and growth in the Weenhayek culture, through various features, such as: Respects the cultural design of the house that integrates local patterns of the environment, ecosystem and contemporary construction elements without affecting its image, the materials and construction techniques used are of a traditional nature, but with contemporary elements that improve their application, durability, stability, as an articulated construction system, commits governments in all instances to the technical-constructive study of the rural areas of the human settlements of the Weenhayek people, and establishes a starting point towards new studies focused on native peoples.

High-resolution air quality data are critical for exposure assessment, regulatory compliance, and urban planning. In this study, we present modelled annual mean concentrations of NO2, PM2.5, PM10, Black Carbon (BC), and particle number concentration (PNC) for all ~2.5 million Danish addresses in 2019 using the Air Quality at Your Street 2.0 system. The modelling framework combines coupled chemistry–transport models (DEHM/UBM/OSPM) with input from the Green Mobility Model and GPS-based vehicle speed data. Model outputs were evaluated against observations from the Danish Air Quality Monitoring Programme, showing strong agreement for NO2, PM2.5, PM10, and BC, but notable overestimation of PNC background levels and underestimation of street contributions. Indicative exceedances of NO2 EU limit values decreased markedly from 2012 to 2019, while exceedances of updated EU and WHO guidelines persist, especially for particulate matter. This work identifies key sources of model uncertainty and supports high-resolution national-scale assessment and citizen access via an interactive map.

Mobile robotic platform that allows the search of people after an earthquake has occurred along with a low-scale fire mitigation system was presented. The mechanical, electrical-electronic system and a wireless control interface for the operation and transmission of video of the mobile robot have been developed. The design of the mechanical system allowed the implementation of a mobile platform that enters complex terrains and a robotic arm with 4 degrees of freedom whose functions are the delivery of first aid kits and the manipulation of the fire mitigation system. The electrical-electronic system is in charge of controlling the electromechanical actuators of the system. The supervision and monitoring system uses two cameras on the robot that transmit the video to external monitors or mobile devices. This allows safeguard the integrity of rescuers by supporting them in their search and assistance tasks providing a technological alternative to the teams dedicated to solve this problems. Field tests were carried out at the training and training center of the public company EMBA-EP, which allowed validating the operation of the platform.

The study of the behavior of the brake pads of an ABS breaking system with different materials was done through the Finite Element Analysis Method using software tools. The necessary steps to carry out the study were: determine the specifications, requirements and restrictions, type of analysis and physical variables to be studied as well as the mathematical model. From this information the modeling of the system, discretization (mesh), establishment of the boundary conditions and finally resolution of the problem, generation of results and Post processing were made. It is fundamental to realize a quality mesh so that this allows to improve the convergence of results by optimizing the computational load so that the problem can be solved. One of the most useful questions of this method is that in the post-processing stage, figures, curves and tables can be generated providing a wide range of results, the person doing the study determining which is the most relevant information for each case. In this work, temperature, heat flow, stress, strain and energy dissipated by the braking process were obtained.

This study presents the complete genome sequence of a novel nege-like virus identified in whiteflies (Bemisia tabaci MEAM1), provisionally designated as whitefly negevirus 1 (WfNgV1). The virus possesses a single-stranded RNA genome comprising 11,848 nucleotides, organized into four open reading frames (ORFs). These ORFs encode the putative RNA-dependent-RNA-polymerase (RdRp, ORF 1), a glycoprotein (ORF 2), a structural protein with homology to those in the SP24 family, (ORF 3), and a protein of unknown function (ORF 4). Phylogenetic analysis focusing on RdRp and SP24 amino acid sequences revealed a close relationship between WfNgV1 and Bemisia tabaci negevirus 1, a negevirus sequence recently discovered in whiteflies from Israel. Both viruses form a clade sharing a most recent common ancestor with the proposed nelorpivirus and centivirus taxa. The putative glycoprotein from ORF 2 and SP24 (ORF 3) of WfNgV1 exhibit the characteristic topologies previously reported for negevirus counterparts. This marks the first reported negevirus-like sequence from whiteflies in the Americas.

Ecosystems are currently at risk, and scientific methods have been developed to assess these impacts. In this scenario, the circular economy makes it possible to reuse raw materials and reduce waste. The objective of the research is to analyze the scientific production related to the evaluation of environmental risks in ecosystems with a circular economy approach. Quantitative research was carried out, with a retrospective and descriptive approach, from a bibliometric study in the SCOPUS database in the period 2014-2024. The peak of research was 4, where research articles predominated with 7 in 12 areas of knowledge. The most producing country was the United States. The most producing journal was Thunderbird International Business Review with 159. four research lines and their gaps were identified. Environmental risk assessment is much more than a legal requirement; it is an opportunity to demonstrate an organization's commitment to sustainability.

Purpose: The objective of the research was aimed at achieving the implementation of the theory of constraints on the operating conditions of a hotel, which differs by its characteristics of traditional processes that have applied this method, from the great heterogeneity of resources needed to meet the demand of customers.Design/methodology/approach: To achieve this purpose, a method of generating conversion equations that allowed to express all the resources of the organization under study depending on the number of customers to serve facilitating comparison between different resources and estimated demand through techniques developed traditional forecasting, these features were integrated into the classical methodology of theory of constraints.Findings: The application of tools designed for hospitality organizations allowed to demonstrate the applicability of the theory of constraints on entities under conditions different from the usual, develop a set of conversion equations of different resources facilitating comparison with demand and consequently achieve improve levels of efficiency and effectiveness of the organization.Originality/value: The originality of the research is summarized in the application of the theory of constraints in a very different from the usual conditions, covering 100% of the processes and resources in hospitality organizations.

The aim of this study was the integration of model-based design and Wavelet transform techniques for filtering surface electromyography (sEMG) signals. In the first stage the noises and interferences that disturb sEMG signals were analyzed to implement a digital filter in a low-cost embedded system that filters these signals. It was shown that the noises and interferences are caused by various sources. Sources of interference and noise can be divided into internal and external. Internal noise is caused by the electrodes, EMG signals of other muscles, and noise associated with the functioning of other organs such as the heart or stomach. The external noises are due to the electrical environment, the most prominent of which is the direct interference of the power hum, produced by the incorrect grounding of other devices and electromotors. For the analysis of the digital filter, sEMG signals from the biceps muscle were used when the elbow joint was at rest and during flexion and extension movements. Signals from 10 participants who did not have any atrophies or pathologies in the muscle were considered for this stage. Denoising of sEMG signals was performed using different wavelets; the smallest error was observed when using the biorthogonal wavelet 3/5 of level 6 with the soft thresholding method. The wavelet filter was implemented using the V-model, and the Processor in The Loop (PIL) tests helped to determine the characteristics of the embedded system where the digital filter was implemented. The digital filter code was implemented on an ESP32 board due to its processing speed of 328 ms.

Purpose: This investigation is based on the theoretical analysis of the application of neural networks to the design and manage supply chains, along with an empirical approach, this investigation its developed with the prediction of the level of integration in the supply chain through neural networks.Design/methodology/approach: The methodology designed and used for the processing of data was the instruction of a neural network wich is used to predict the level of integration in a supply chain. This type of predictive application appears in the literature reviewed on supply chains. This analysis was carried out in a comparative way with the heterogeneous and homogeneous weights of the neuron training.Findings: The main results of this research focus on predicting the level of integration in the supply chain from the neoronal network. This provides a coached neuron that can be applied in other studies and, therefore, predict the outcome. On the other hand, it is shown that if the weights of the integration level variables are not homogeneous, the procedure presents different results depending on the context in which it is developed.Research limitations/implications: Among the limitations of the implementation of neural networks it should be noted, the necessary adaptation to the characteristics of the supply chains and the areas of performance of the business organizations under study, in the framework of activities productive or service itself, in addition to analyzing its corporate purpose in relation to the satisfaction of certain needs of the target markets.Originality/value: The literature shows multiple theoretical sources that refer to studies of neural networks in supply chains, observing the opportunity to apply this technique to predict the level of integration due to its benefits for decision making. The originality of this scientific work lies in the possibility of comparing the historical data of the level of integration and those predicted as a result of the coaching of the neuron with the weights of the heterogeneous and homogeneous variables.

This study analyzes the current state of artificial intelligence (AI) adoption among micro-, small-, and medium-sized enterprises (MSMEs) in Ecuador, with a focus on its application across core business functions. Using a stratified random sample of 385 firms from the most representative economic sectors, a survey instrument was designed to assess three dimensions: access to AI-enabling conditions, degree of AI utilization, and organizational characteristics. The results reveal that AI adoption remains limited and highly concentrated in marketing-related functions, particularly in content generation and social media automation, with minimal implementation in finance, logistics, and human resource management. The study also identifies the main barriers hindering AI adoption. The lack of qualified professionals and the unavailability of structured databases emerged as the most critical obstacles, followed by limited financial capacity. One-way ANOVA and Kruskal–Wallis tests confirmed significant differences in AI adoption levels based on company size and sector, especially in areas such as inventory optimization and design prototyping. These findings highlight a gap between the potential of AI technologies and their real-world implementation in Ecuadorian MSMEs. They underscore the need for targeted strategies focused on workforce training, digital infrastructure development, and institutional support to promote broader and more effective AI integration.

This study presents an automated classification system to prioritize electrical service complaints at CNEL EP. A total of 143,113 real records were processed through data cleaning, missing-value imputation, and the engineering of predictive variables reflecting complaint urgency and recurrence. Based on these criteria, the target variable “Priority” was defined to distinguish high-priority from normal complaints. Supervised learning models, specifically Decision Tree and Random Forest, were then trained using one-hot encoding and cross-validation. Random Forest delivered the best performance, achieving 91% accuracy and an AUC-ROC of 0.89. These results indicate that the proposed system can significantly improve technical resource allocation and reduce response times for electrical complaints. Moreover, the study demonstrates the feasibility of integrating machine-learning techniques into the operational management of electric distribution companies, enabling future enhancements and real-time deployment.