Search Results (32,004)

A substantial number of students with hearing impairments are enrolled in higher education, motivating the development of inclusive assistive technologies that reduce communication barriers. This study developed and evaluated a prototype electronic glove that translates Mexican Sign Language (LSM) signs into Spanish text using machine learning. Eight participants (four deaf and four hearing with LSM proficiency) completed four sessions involving 12 signs; three sessions (S1–S3) were used for model development and one session (T) was held out for evaluation. Models were trained on S1–S3 and tested on T using a session-level split without window mixing across sessions; therefore, results represent a speaker-dependent, inter-session pilot assessment rather than a speaker-independent generalization test. The glove integrates flex sensors and an inertial measurement unit IMU MPU6050 connected to an ESP32-C3 SuperMini microcontroller. These components were selected due to their low cost, availability, and ease of integration, making them suitable for the development of accessible wearable assistive technologies. Under this protocol, the system achieved a window-level overall test accuracy of 97.0% (95% CI computed at the window level: 96.00–97.00), with higher performance for the dynamic subset (98.0%) than for the static subset (95.0%), and an algorithmic decision delay of 1.2 s. Usability and acceptance were evaluated using the System Usability Scale (SUS) and a Technology Acceptance Model (TAM)-based questionnaire. The mean SUS score was 50.6 ± 1.8 (marginal usability), while participants reported positive perceptions across TAM constructs. Overall, findings demonstrate technical feasibility under controlled inter-session conditions and provide a foundation for iterative user-centered refinement, followed by strict speaker-independent validation and classroom deployment studies in future work. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) is becoming increasingly prevalent worldwide, particularly among individuals with obesity and type 2 diabetes (T2D). MASLD remains potentially reversible in the early phases but, without timely intervention, it can progress to metabolic dysfunction-associated steatohepatitis (MASH) and hepatic fibrosis, which in turn may advance to cirrhosis and hepatocellular carcinoma over time. With no pharmacological treatments specifically indicated for MASLD, current therapeutic strategies include lifestyle modifications, including dietary modifications. Niacin and its molecular derivatives (collectively belonging to the vitamin B3 group) play a central role in metabolic processes, especially through their involvement in the biosynthesis of the oxidized form of nicotinamide adenine dinucleotide (NAD⁺). A growing body of preclinical evidence suggests that reduced NAD⁺ levels are a hallmark of MASLD, and that NAD⁺ precursors may help attenuate disease progression through multiple mechanisms, including sirtuin 1 (SIRT1)-mediated inhibition of hepatic lipogenesis. Although these findings from experimental models suggest a potential role for niacin and related molecular derivatives as a modulators of MASLD-related pathways, evidence from human studies remains limited and inconsistent. For instance, interventional studies evaluating niacin or molecular derivatives supplementation have reported variable findings, with several trials showing limited meaningful benefits on MASLD-related outcomes. Consequently, further well-designed, controlled trials are needed to clarify therapeutic efficacy, dose–response relationship, and the feasibility of integrating niacin derivatives into dietary or therapeutic strategies aimed at reducing liver fat and improving adverse metabolic outcomes. This review aims to (i) summarize mechanistic insights on the role of niacin as a source of NAD⁺ on experimental MASLD and (ii) critically evaluate the available human evidence on the effect of supplemental niacin and derivatives in the prevention of MASLD development and its progression to MASH and fibrosis. Full article

Emotion identification via computer vision has made continuous progress over the last few years. Although images have been the gold standard for the past two decades, video is increasingly common. Video is particularly suitable for the study of emotions, as it allows them to be considered as spatiotemporal phenomena. In particular, the discovery of anxiety among Mexican students is a key element for improving their learning in the classroom. In pursuit of this goal, we focused on the following challenges. First, the scarcity of specialized datasets for this task prompted us to develop an experimental protocol to generate a specific dataset; second, to conduct a thorough study of the appropriate number of emotional intensity levels; and third, to develop a suitable design for a deep learning architecture. Our pivotal results include the development of a new dataset labeled with three different emotion levels and appropriate ConvNet architectures, complemented by a study of various intensity levels. The optimal architecture achieved an F1-score of 0.7620 across five intensity levels and provides an adequate baseline for multiclass classification. Full article

According to the World Health Organization, antibiotic research remains insufficient, emphasizing the urgent need for new active molecules, particularly against resistant bacteria. Based on known antibacterial scaffolds, new fluoroquinolone derivatives have been synthesized by our research group, including compound 7a, a difluoroboranyl-fluoroquinolone that previously demonstrated activity against sensitive strains. Methods: The minimum inhibitory (MIC) and bactericidal (MBC) concentrations of compound 7a were determined against Staphylococcus aureus, Klebsiella pneumoniae, and Escherichia coli. The selective development of ciprofloxacin-resistant S. aureus was induced by reseeding the isolate on seven consecutive days with an antibiotic concentration that was not capable of inhibiting its development. Pharmacokinetic and toxicological properties were predicted using SwissADME, Way2Drug, and molecular docking (AutoDock Vina). In vivo toxicity was evaluated in BALB/c mice through histopathological liver and kidney analysis and serum biochemical markers. The antibacterial efficacy of 7a (80 mg/kg/day) was assessed in a murine pneumonia model induced by ciprofloxacin-resistant S. aureus. DNA gyrase inhibition was confirmed through plasmid electrophoresis assays in E. coli DH5-α cells. Results: Compound 7a exhibited both MIC and MBC values of 0.25 μg/mL, while ciprofloxacin-resistant S. aureus strains did not exhibit a detectable MIC within the concentration range tested (up to 1024 μg/mL). In silico predictions revealed favorable ADME profiles, low toxicity, and strong interaction with DNA gyrase. In vivo, 7a showed no signs of hepatotoxicity or nephrotoxicity and effectively reduced pneumonic tissue to 1.99% in infected mice. Electrophoretic assays confirmed DNA gyrase inhibition consistent with the mechanism of fluoroquinolones. Conclusions: Compound 7a evidenced activity against ciprofloxacin-resistant S. aureus in vitro and reduced infection progression in vivo. It also displays favorable drug-like properties, low predicted toxicity, and DNA gyrase inhibition. Full article

This study identifies motivational profiles among high school students regarding access to the University of Guanajuato, Yuriria Campus, within the framework of Sustainable Development Goal 4 (SDG 4). Using a survey of 306 students from diverse public and private institutions in southern Guanajuato, we applied K-means clustering analysis with validation techniques (elbow method, silhouette, bootstrap) to examine five key dimensions: family support, university interest, academic perception, transport accessibility, and self-efficacy. The analysis revealed three distinct profiles: (1) “Privileged and committed” (21%), with high scores in all variables and predominantly from private schools; (2) “Supported but not captivated” (65%), with moderate resources but low specific interest in the institution; and (3) “Vulnerable and disconnected” (14%), facing multiple barriers including low family support, economic constraints, and rural origin. ANOVA confirmed significant differences between clusters (p < 0.001). The inclusion of socioeconomic variables allowed for a deeper characterization of equity gaps. These findings provide evidence-based insights for designing targeted recruitment and retention strategies aligned with SDG 4, demonstrating how educational data analytics can inform sustainable higher education policies in regional contexts. Full article

Secondary succession in tropical forests is traditionally described as a linear process driven by time since disturbance. However, growing evidence suggests that recovery pathways depend strongly on historical and environmental contexts. We evaluated how disturbance legacies and edaphic constraints interact to shape successional trajectories in a tropical karst landscape of the Maya Forest, Mexico. We sampled 100 plots along a chronosequence, quantifying vegetation structure, floristic diversity, biomass (NDVI), disturbance legacies, and soil properties. Using unsupervised clustering (K-means) and multivariate ordination, we identified four contrasting ecological typologies that represent distinct successional states rather than transient stages. Our results show a pronounced dichotomy in vegetation dynamics following the abandonment of land-use practices: while some sites are experiencing diverse development due to positive forest legacies (Typology B), others remain stalled (Typology C), dominated by lianas, where biotic barriers inhibit tree regeneration despite decades of abandonment. Additionally, we documented an asynchronous recovery between floristic recovery and vertical development; in sites with edaphic constraints, forests reach high diversity and biomass but exhibit stunted growth (Typology D). This suggests that severe abiotic constraints—specifically high rockiness and shallow soils—limit the dominance of highly competitive species, thereby acting as a filter that maintains high levels of diversity despite structural limitations. Edaphic analysis confirmed that chemical fertility and physical constraints (rockiness and shallow depth) act as orthogonal filters. This explains the persistence of structurally constrained yet functionally mature forests as stable, edaphically determined outcomes. Overall, secondary succession in tropical karst is nonlinear and path-dependent, governed by a hierarchical filtering model where historical land use dictates community identity and physical substrate limits structural architecture. These findings highlight the need for trajectory-specific management and the abandonment of uniform expectations of forest recovery in karst landscapes. Full article

The development of AI-driven computational methods has enabled rapid and non-invasive analysis of respiratory sounds using acoustic data, particularly cough recordings. Although the COVID-19 pandemic accelerated research on cough-based acoustic analysis, many early studies were limited by insufficient data quality, lack of standardized protocols, and limited reproducibility due to data scarcity. In this study, we propose an audio analysis framework for cough-based respiratory disease screening research using COVID-19 as a clinically validated case dataset. All analyses were conducted on a single clinically acquired multicentric dataset collected under standardized conditions in certified laboratories in Mexico and Spain, comprising cough recordings from 1105 individuals. Model training and testing were performed exclusively within this dataset. The framework incorporates signal preprocessing and a comparative evaluation of segmentation strategies, showing that segmented cough analysis significantly outperforms full-signal analysis. Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) for CNN2D models and the supervised Resample filter implemented in WEKA for classical machine learning models, both applied exclusively to the training subset to generate balanced training sets and prevent data leakage. Feature extraction and classification were carried out using Random Forest, Support Vector Machine (SVM), XGBoost, and a 2D Convolutional Neural Network (CNN2D), with hyperparameter optimization via AutoML. The proposed framework achieved a best balanced screening performance of 85.58% sensitivity and 86.65% specificity (Random Forest with GeMAPSvB01), while the highest-specificity configuration reached 93.90% specificity with 18.14% sensitivity (CNN2D with SMOTE and AutoML). These results demonstrate the methodological feasibility of the proposed framework under the evaluated conditions. Full article

A correct description of quarkonia production and kinematics is still one of the most challenging assignments for Quantum Chromodynamics. This document presents a study of the ${\rm Y}$(1S), (2S) and (3S) mean transverse momentum ($⟨p_{\mathrm{T}}^{{\rm Y}}⟩$) as a function of the charged particle multiplicity ($N_{\mathrm{Track}}$) in proton–proton collisions at $\sqrt{s}$ = 7 TeV generated with Pythia 8.312 CUETP8M1 tune. The comparison to real data collected by the CMS experiment indicates that the agreement is much better for the excited states than for the ground state. The observed fast increase in the $⟨p_{\mathrm{T}}^{{\rm Y}}⟩$ at small values of $N_{\mathrm{Track}}$ is mainly due to the contribution from the away region. Furthermore, when computing the $⟨p_{\mathrm{T}}^{{\rm Y}}⟩$ from jetty and isotropic events, a clear $p_{\mathrm{T}}$ hardening is observed in jetty events. Finally, analyzing the fragmentation of jets containing an ${\rm Y}$(nS), a new method is proposed to test the new quarkonia shower present in the Monte Carlo event generator. Full article

This study aimed to evaluate the efficacy of various control alternatives against Varroa destructor in Apis mellifera colonies in a semi-arid region of Mexico. One hundred and ten homogeneous colonies, with a uniform population and infestation level of V. destructor, were randomly distributed into the following 11 experimental groups (10 colonies/group): amitraz, oxalic acid in glycerin (OA-G), oxalic acid in sugar syrup (OA-SS), ethanolic extracts of Bursera penicillata, Larrea tridentata, and Lippia graveolens, powdered sugar dusting, three vehicle controls (vegetable oil, ethanol, glycerin), and one untreated control. Efficacy was determined by recording mite fall during the treatment period relative to a subsequent reference treatment. Significant differences were observed among treatments (p < 0.0001). Amitraz was the most effective (94.4%), followed by OA-G (85.1%). The OA-SS and plant extracts showed intermediate efficacy (62.1% to 73.7%), while sugar dusting showed lower values (55.8%) but still higher than the control (31.2%). These findings support the restricting of amitraz use to minimize resistance risk and suggest implementing OA-G as a high-efficacy alternative. Furthermore, ethanolic plant extracts and powdered sugar dusting combined with sticky bottom boards may serve as accessible, complementary tools within integrated pest management programs to reduce reliance on synthetic acaricides and mitigate the development of resistance. Full article

In this study, rice-husk fiber (RHF) extracted via alkali hydrolysis was used as a reinforcing material (0–10 wt%) in a pectin-sodium alginate (PE/SA) matrix to develop biofilms by the casting method. These biofilms were characterized by using FTIR, XRD, TGA, and DSC. The thickness, moisture content, water solubility, swelling behavior, water-contact angle, water-vapor permeability, optical transparency, and mechanical properties of biofilms were investigated. It was observed that the PE/SA/RHF film loaded with 5% RHF had better visual attributes, and a further increase in reinforcement was not found to be as favorable. The addition of 10 wt% RHF significantly enhanced the thickness from 0.094 to 0.127 mm, water solubility from 49.25 to 56.13%, water-contact angle from 48.4 to 62.6°, and tensile strength from 4.17 to 10.23 MPa. However, decreases in water-vapor permeability from 1.94 × 10⁻⁹ to 1.32 × 10⁻⁹ g·m⁻¹·Pa⁻¹·s⁻¹ and in elongation at break from 19.24 to 2.87% were observed in the biofilms. Structurally, FTIR confirmed intermolecular hydrogen bonding between components. XRD revealed that the films remained predominantly amorphous, without significant crystalline alterations. Furthermore, thermal stability improved with the addition of RHF. Finally, these PE/SA/RHF composite films may be potential eco-friendly biodegradable packaging candidates for food industry applications. Full article

During the COVID-19 pandemic, women have had to face different psychosocial problems. For this reason, psychoeducational interventions based on web-based resources have been developed to address their mental health. This study aimed to evaluate the pilot of a psycho-emotional support web platform based on elements of cognitive-behavioural therapy in Mexican women during the COVID-19 pandemic. Through a pre-experimental design with pre-test and post-test evaluations, 73 women between 18 and 68 years old (M = 43.42 years, SD = 12.40) had access to this platform for one month, which contained four thematic modules (stress, anxiety, depression and violence). They also received two complementary three-hour synchronous sessions. All participants reported similar levels of emotional symptoms (p > 0.05), as well as perceiving violence exerted by their partners (p > 0.05). The web platform and its psychoeducational content turned out to be quality informative resources; however, no statistically significant changes were observed in the psychological variables in question. Web platforms and emotional support applications should be developed according to the needs and characteristics of the population for which they are designed; this will promote greater satisfaction and reduce therapeutic abandonment. Full article

Higher Education is facing a transformation of the teaching profession due to the confluence of active methodologies, learning management systems, and artificial intelligence. However, existing research tends to address these elements in isolation, lacking integrative analyses that examine their combined impact on the teaching role in higher education. Through a narrative review and thematic analysis of 49 articles, opportunities and challenges in this intersection are identified. The results indicate that the teacher is not merely a content transmitter but a designer of formative experiences, a critical guide, and an ethical reference in the use of AI. More than a replacement, technology proposes a teacher profile as an architect of critical and adaptive learning that combines pedagogy, technology, and ethics. Full article

Background/Objectives: Sciatic nerve injuries are among the most common classes of peripheral nerve harm and have a strong impact on quality of life, as well as a significant negative economic impact for patients, society, and governments, since they represent a frequent cause of work-related disabilities and sick leave applications. Following nerve injury, neurons, Schwann, and satellite cells undergo marked changes in phenotype, metabolic activity, neuronal survival, nervous transmission, and an exacerbated activation of the inflammatory response. Leuprolide acetate (LA), a clinically available agonist of gonadotropin-releasing hormone (GnRH), has shown clear neurotrophic properties and is considered a novel potential candidate for treating neural injuries, including sciatic nerve pathologies. This study aimed to analyze the effect of LA treatment on sensory function and dorsal root ganglia (DRG) changes in a rat sciatic nerve full-transection (SNT) model. Methods: Variations in cold and heat sensitivity were assessed using the thermal plate test, while DRG tissue sections were examined for modifications in reactive gliosis by immunofluorescence analysis, and axonal transport using a retrograde tracer. Also, changes in the expression of pro-regenerative genes Stat3, Socs3, Fos, Jun, Atf4, and Limk1 were quantified by qPCR. Results: Our results showed that LA treatment exerted a distinct neurotrophic effect, since it promoted the specific recovery of cold sensitivity, improved axonal transport, regulated the inflammatory response, and modulated the exacerbated expression of pro-regenerative genes in the SNT model. Conclusions: These findings indicate that LA therapy may have the potential to improve sensory recovery in patients with sciatic nerve injuries. Full article

During the early years of interaction between humans and computer systems, user authentication and identification was carried out with the support of knowledge-based factors (something the user knows: passwords, PINs, etc.) and tokens (something the user possesses: credentials, RFID cards, etc.) or a combination of both. In other words, the user presents a token and a password to the system in order to gain access. These solutions pose major challenges: Knowledge-based systems, which rely on secrets like passwords, are vulnerable to those secrets being guessed, shared, or forgotten. On the other hand, tokens are also vulnerable; some, despite implementing encryption, attract cyber attackers who can forge them, and users can share or lose them. In the search for more robust methods, the use of biometrics has been considered. Full article

Global energy demand relies heavily on fossil fuels, which produce greenhouse gas emissions. Additionally, municipal solid waste, driven by population growth, represents another source of emissions. In Mexico, organic waste contributes 61 million tons of CO₂eq annually due to inadequate disposal. In Mérida, Yucatan, over 231,000 tons of organic waste are generated yearly, including Urban Tree Pruning (UTP) from 760 public spaces—a significant, undervalued lignocellulosic resource. This study presents a comprehensive, year-round compositional characterization of Mérida’s UTP to establish its chemical profile and assess its seasonal stability as a precursor for bio-based products (i.e., bioethanol). Characterizing local and stable feedstocks, such as UTP, is a fundamental step to enabling Mexico’s compliance with biofuel policies like the 5.8% gasoline blend mandate (NOM-016-CRE) and the Alcohol-to-Jet strategy, supporting progress toward SDGs 7, 11, and 13. Based on a stratified random sampling, monthly analysis (May 2024–April 2025) revealed a consistent biochemical profile with mean annual contents of 23.32% lignin and 62.46% holocellulose. Statistical analysis (Tukey’s test) confirmed its structural homogeneity throughout the year. This uniformity is a key operational attribute, as it allows for the use of standardized industrial pretreatment parameters. Furthermore, the characterized composition supports a theoretical ethanol yield of 170 g/kg of dry biomass, a value competitive with traditional feedstocks like sugarcane bagasse. Consequently, Mérida’s UTP is characterized as a reliable and consistent biomass resource, supporting a transition from linear waste disposal to a circular bioeconomy model. Full article