Universidad de Xalapa

Introduction: Organizational climate is a determining factor in employee performance and well-being, while job satisfaction acts as an indicator of staff commitment, motivation, and retention, underscoring the relevance of its study in the field of government organizations. Objective: To analyze the relationship between organizational climate and job satisfaction among employees of a municipal public administration in the state of Veracruz, active in the first half of 2024. Methodology: Quantitative, descriptive-comparative-correlational approach, with a non-experimental design, using a survey applied to 388 employees. Results: Spearman’s Rho coefficient indicated a positive and moderately high correlation between organizational climate and job satisfaction. Discussion: The Student’s t-test showed that there is no statistically significant evidence to affirm that gender and/or seniority affect the perception of both the organizational climate and job satisfaction, which could reflect a mature institution or an organization that demands structural changes to prevent the risk of complacency from becoming a reality. Conclusion: The development of strategies that strengthen the well-being of employees in public administrations is necessary to improve the quality of service to citizens.

Liquid State Machines (LSMs), a form of Reservoir Computing (RC) based on spiking neurons, have shown promising results for tasks involving complex, temporally structured data. However, research on the impact of coding schemes, classifier choice, and separability metrics applied to LSM remains insufficient. In this work, six coding schemes, six classifiers, and three separability metrics across two synthetic benchmark tasks (Frequency and Pattern Recognition) are systematically evaluated. This allows us to contrast readout strategies and assess how well separability metrics align with classification accuracy. Our results showed that rate-based coders combined with linear classifiers yield the most accurate performance. Among separability metrics, K-Nearest Neighbor Overlap Score (KNN-OS) provides the most consistent correlation with accuracy, while Silhouette Score (SS) and Davies-Bouldin Index (DBI) display important limitations. These findings contribute to a clearer understanding of readout design in LSMs and provide practical guidance for selecting coding strategies, classifiers, and evaluating metrics in future applications.

This study investigates the ability of unsupervised clustering algorithms to identify latent structures in the FairFace image dataset. To do this, high-dimensional feature vectors are extracted from each image using the AlexNet convolutional neural network. The K-means and Fuzzy C-means algorithms are applied, and the optimal number of clusters is determined to be <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{k}=2$</tex>. However, external validation, by comparing the groups formed with the seven racial labels, reveals a low correspondence, showing an almost equal distribution of classes in both clusters. It is concluded that, although the algorithms identify a consistent bipartite structure in the data, this does not correspond to the predefined racial categories. This suggests that the features extracted by AlexNet are dominated by other visual attributes, such as gender, lighting, or pose, and are not sufficient on their own to separate the data according to the underlying racial classification.

Lymphedema is a frequent complication associated with breast cancer treatment. It is estimated that up to 30% of patients undergoing mastectomy develop this condition within 12 to 24 months post-surgery. In Mexico, the limited emphasis placed on lymphedema prevention in breast cancer patients is reflected in the insufficient coverage of this issue within official medical guidelines. In this review, research articles, systematic reviews, and official treatment guidelines were retrieved from PubMed, Google Scholar, Elsevier, SciELO, and Redalyc databases, to examine studies about the application and effectiveness of physiotherapy techniques for the prevention, diagnosis, and treatment of postmastectomy lymphedema. Our findings indicate that complex decongestive therapy (CDT) is considered the first-line treatment for lymphedema. Among its components, compression therapy shows the strongest individual evidence base. Nevertheless, studies consistently demonstrate that the combined use of all four components of CDT (manual lymphatic drainage, compression, skin care, and exercise) results in superior patient outcomes. Despite this, CDT is not routinely implemented as a standard of care for patients following mastectomy and/or lymphadenectomy in Mexico. Therefore, there is a pressing need to promote the inclusion of physiotherapy strategies, particularly CDT, in the prevention and management of postmastectomy lymphedema within national healthcare protocols.

Within the medical field, computer vision has an important role in different tasks, such as health anomaly detection, diagnosis, treatment, and monitoring medical conditions. Image segmentation is one of the most used techniques for medical support to identify regions of interest in different organs. However, performing accurate segmentation is difficult due to image variations. In this way, this work proposes an automated multiple-feature construction approach for image segmentation, working with magnetic resonance images, computed tomography, and RGB digital images. Genetic programming is used to automatically create and construct pipelines to extract meaningful features for segmentation tasks. Additionally, a co-evolution strategy is proposed within the evolution process to increase diversity without affecting segmentation performance. The segmentation is addressed as a pixel classification task; in this way, a wrapper approach is used, and the classification model’s segmentation performance determines the fitness. To validate the effectiveness of the proposed method, four datasets were used to measure the capability of the proposal to deal with different types of medical images. The results demonstrate that the proposal achieves values of the DICE similarity coefficient of more than 0.6 in MRI and C.T. images. Additionally, the proposal is compared with SOTA GP-based methods and the convolutional neural networks used within the medical field. The method proposed outperforms these methods, achieving improvements greater than 20% in DICE, specificity, and sensitivity. Additionally, the qualitative results demonstrate that the proposal accurately identifies the region of interest.

In Mexico, school violence affects students and hinders the achievement of educational goals such as civic education. The main objective of this qualitative study was to understand the experiences, senses, meanings and opinions about school violence in the intersection or confluence with civic education. Semi-structured interviews and focus groups were developed to recover the narratives of secondary school teachers and students in Xalapa, Veracruz. Grammars were identified that inscribe consensus and dissent about the concept, manifestations and “causes” of school violence. We discuss the possibility that learning the fundamentals of citizenship can lead to better relationships between people, constituting an alternative for the education and prevention of violence in schools.

The analysis of the electroencephalogram (EEG) signals within the Human-Computer Interaction (HCI) field is a helpful method when it comes to reducing the bias that usually arises while obtaining data for evaluating information interactive systems. The needs of a group of people with specific characteristics can be unveiled by the interpretation and comparison of their interaction responses, as well as in a more objective way, throughout their EEG signals. Particularly, this strategy is not a common practice during the execution of research for the development of new technologies directed at Attention-Deficit/Hyperactivity Disorder (ADHD) people. The present work is a systematic literature review (SLR) about how EEG signals have been implemented to identify or differentiate people with ADHD within the fields of computer science and engineering. Hence it aims to provide a wide scope related to the advancements in the area of HCI intended for the above-mentioned population. Together with classifying the associated recent contributions to identify different opportunity areas that remain unexplored. This review was executed by following the methodology proposed by Kitchenham, through which 20 articles were selected as final primary studies. The performed analysis allowed the identification of relevant applications, along with potential research areas.

In May and June 2024, a die-off of Mexican mantled howler monkeys (Alouatta palliata mexicana) occurred in southern Mexico. This commentary documents the event, attributing it to extreme heatwaves, drought, wildfires, and habitat impoverishment. Despite their reported resilience to habitat disturbances, mantled howler monkey mortality rate in some areas reached 31%. Key evidence points to heatstroke as the primary cause of death, exacerbated by limited hydration and reduced dietary diversity in disturbed habitats. Immediate responses included community-led rescues (e.g., hydrating the monkeys), coordination of rescue activities by nongovernmental organizations (NGOs) (e.g., managing donations), involvement of scientists (e.g., monitoring of primate populations), and assistance from government officials (e.g., providing legal support for animal management). This event underscores the urgency of developing action plans to prevent and attend future crises. Among other actions, we highlight (i) establishing primate care infrastructure with medical and rehabilitation centers; (ii) developing protocols and training programs to ensure rapid crisis response; (iii) fostering collaboration among government, NGOs, and academic institutions for effective crisis management; and (iv) developing targeted research on climate change impacts, predictive models, and long-term health monitoring. We emphasize the critical need for coordinated conservation efforts to protect wild primates and maintain natural ecosystem resilience in the face of escalating climate challenges.