Search Results (6,588)

This study describes university students’ acceptance of artificial intelligence (AI) in higher education across three institutional contexts in Mexico, Spain, and Finland. A quantitative descriptive-correlational design was used with a non-probabilistic convenience sample of 416 undergraduate students who participated in a structured virtual workshop on the academic use of AI tools. The study did not include random assignment, a control group, or a pretest–posttest comparison; therefore, the results are interpreted in descriptive and associative terms. Data were collected using the AIComplex instrument, which assesses perceived risk, performance expectancy, effort expectancy, facilitating conditions, perceived value, habit, perceived complexity, and AI acceptance in higher education. The instrument showed adequate overall internal consistency across the three contexts. Additional psychometric evidence was obtained through measurement invariance analysis by dimension and exploratory factor analysis. Descriptive statistics, independent samples t-tests with effect sizes, Pearson correlations, scatterplot matrices, and a correlation heat map were used to examine students’ perceptions and associations among the dimensions. The results showed generally favorable perceptions of AI, particularly in performance expectancy and perceived value. No statistically significant gender differences were found, and the effect sizes were trivial. The strongest observed associations with AI acceptance were found for perceived value, habit, performance expectancy, and perceived complexity. The exploratory factor analysis suggested partial empirical overlap among some dimensions, while the invariance analysis indicated that cross-context comparisons should be interpreted cautiously. The findings provide contextual evidence on AI acceptance in higher education, highlighting associations among cognitive, institutional, and experiential dimensions without implying causal or nationally representative conclusions. Full article

Background/Objectives: Wound infections represent a major clinical challenge due to their polymicrobial nature, biofilm formation, and increasing antimicrobial resistance, which compromise conventional treatments. This study aimed to develop and evaluate ligand-stabilized silver nanoparticles (AgNPs) with improved antimicrobial activity and cytocompatibility, and to investigate their incorporation into electrospun nanofibers for wound management. Methods: Four AgNP formulations stabilized with citrate, cysteine, ketorolac, and diclofenac were synthesized via chemical reduction. Physicochemical characterization included surface plasmon resonance and zeta potential measurements. Antimicrobial activity was assessed through minimum inhibitory concentration (MIC) and bactericidal assays against Gram-positive, Gram-negative, and fungal strains. Toxicity was evaluated using the HET-CAM assay, while cytocompatibility was determined in fibroblasts, MG-63 cells, and mesenchymal stem cells. Diclofenac-stabilized AgNPs were incorporated into electrospun PCL/PEO nanofibers to generate a functional nanocomposite system. Results: All AgNPs exhibited a characteristic SPR at ~400 nm and high colloidal stability. Diclofenac-stabilized AgNPs (dc-AgNPs) showed the highest antimicrobial activity, with MIC values of 18.8 mg/L against Staphylococcus aureus and Pseudomonas aeruginosa, and 4.7 mg/L against Candida albicans, along with strong bactericidal effects. HET-CAM assays indicated negligible irritation at concentrations up to 75 mg/L. Cytocompatibility results revealed a dose-dependent response, with fibroblasts being more sensitive. Electrospun nanofibers loaded with dc-AgNPs achieved a 2.6 log reduction against Streptococcus mutans and moderate reductions (0.4–0.7 log) against other pathogens. Conclusions: Ligand engineering critically influences the antimicrobial efficacy and biocompatibility of AgNPs. The incorporation of dc-AgNPs into electrospun nanofibers represents a promising approach for treating biofilm-associated wound infections. Full article

Microplastics constitute an emerging contaminant of major concern in Latin America, where human exposure predominantly occurs through ingestion of drinking water and marine/estuarine food chains. This review synthesises available evidence on occurrence, exposure pathways, toxicological mechanisms, and regional public health risks, while examining regulatory and monitoring limitations that constrain effective risk management. Reported concentrations in drinking water show a wide range (1–1194 particles/L), dominated by PET, PP, and PS, with fibres and fragments as the main morphotypes. In commercial marine species, prevalence reaches 70–100%, with burdens up to 44 particles/g in oysters and ~90 particles/250 g in mussels. Estimated Daily Intake is 2–5 times higher in children (e.g., Chile: 13.03 vs. 5.59 particles/day in adults). Toxicological mechanisms include oxidative stress, chronic inflammation (NF-κB pathway), endocrine disruption, intestinal dysbiosis, systemic translocation, and placental transfer, exacerbated by vectorization of local co-contaminants (Hg from mining, Cd/Pb from agriculture). Risk indices indicate extreme danger in Brazil, Chile, and Ecuador, where data are available. Significant geographic and methodological gaps persist, with Brazil dominating research (~50–60%). Multicenter biomonitoring, harmonised surveillance networks, and SDG-aligned policies are urgently needed to reduce exposure burdens, protect vulnerable populations, and advance toward comprehensive regional risk assessment. Full article

High renewable penetration introduces stochastic variability in distribution-network operation, requiring probabilistic AC power-flow tools that remain accurate in the tails while avoiding the computational burden of large Monte Carlo simulation. This paper presents a fully reproducible non-intrusive polynomial chaos expansion (PCE) framework for uncertainty propagation through nonlinear Newton–Raphson AC power flow. The method uses sparse-grid quadrature to train PCE surrogates from deterministic power-flow evaluations and is benchmarked against high-fidelity Monte Carlo simulations. In the validation, the IEEE 33-bus feeder is evaluated using up to 50,000 Monte Carlo samples, 95% bootstrap confidence intervals, PCE orders 2–5, correlated uncertainty scenarios, realistic thermal-loading recalibration, reactive-power sensitivity of renewable injections, multi-feeder testing on IEEE 33-bus, CIGRE MV, CIGRE LV, and IEEE 118-bus networks, and a 365-snapshot full-year daily screening. For the base IEEE 33-bus case, third-order PCE required only 494 deterministic power-flow evaluations and reproduced the 50,000-sample Monte Carlo benchmark with relative mean errors of 0.014% for minimum voltage, 0.119% for active losses, and 0.113% for substation import. The corresponding wall-clock speed-up was 13.29×, while reducing deterministic evaluations by approximately 101×. Correlated load–PV uncertainty increased the upper tail of substation import from 6.06 MW to 6.30 MW, and realistic thermal recalibration revealed line-loading p99 values above 100% for the 60% target case, demonstrating the operational value of physically meaningful ampacity settings. The proposed workflow provides an open, scalable, and tail-aware basis for uncertainty-informed distribution-network planning under renewable variability. Full article

The systematics of white-tailed deer (Odocoileus virginianus) has been controversial. Some mammalogists consider the white-tailed deer to be a single species, whereas others consider it to consist of multiple species. To help resolve the controversy, we sequenced mitochondrial cytochrome B (mtCyt-b) in samples collected from 83 Neotropical white-tailed deer. Furthermore, we analyzed mitogenomes of samples collected from 19 white-tailed deer. There were five main results, as follows: (1) Phylogenetic analyses with the mtCyt-b dataset showed the existence of eight groups of O. virginianus, three in North and Central America and five in South America. It was hypothesized from different analyses that a Central American O. virginianus population generated the white-tailed deer populations in South America. (2) The haplotype temporal diversification within O. virginianus occurred during the Pleistocene. With the mitogenome dataset, it was dated to have occurred approximately 2.2 mya, using both Bayesian inference and haplotype networks. (3) All of these O. virginianus groups showed elevated levels of mitochondrial genetic diversity for the mtCyt-b dataset, with the exception of the Ecuadorian population (4) Some groups of O. virginianus yielded significant evidence of female population expansions with the mtCyt-b dataset. (5) Although the genetic heterogeneity among these O. virginianus groups was significant, the genetic distances were relatively small. Provisionally, the karyotypic differences between North American and Colombian specimens were very small; therefore, until further karyotypic studies demonstrate otherwise, we consider the existence of a single species of O. virginianus. Because mtDNA genomes have only one quarter of the effective number of autosomal nuclear genes, this generates relatively rapid coalescence times, which can inflate estimates of divergence among populations. Therefore, it is very important to soon sequence the nuclear genes for the different geographic assemblages of O. virginianus found. Full article

An important issue to consider is the training time, as it can have a considerable influence on the set of stories generated, due to factors such as uncertainty, diversity, and narrative coherence. This paper presents a systematic analysis of the dynamics of predictive entropy at different times and random seeds, studying the interaction of entropy with lexical diversity, repetition, semantic consistency, and entity continuity in probabilistic language generation models. A comparative evaluation of recurrent and attention-based architectures is performed using linguistic metrics. Predictive entropy was reduced by 32.4% (LSTM) and 28.7% (Transformer). LexDiv obtained 0.71 ± 0.03 and Self-BLEU obtained 0.42 ± 0.02, suggesting greater confidence in the model. However, it should be noted that a greater reduction in entropy may be associated with lower lexical diversity and higher Self-BLEU scores. This indicates a trade-off between confidence and expressiveness in probabilistic language models. The entropy term encourages smoother probability distributions and reduces premature mode collapse during Adam optimization. $L_{total}=L_{CE}-\lambda H\left(p\right(y\left|x\right)$ aims to improve stability, reduce random initialization, and enable the generation of adaptable narratives, which may be relevant for neurodiversity-oriented narratives. Full article

Vehicle ad hoc networks (VANETs) can help prevent traffic accidents through wireless communication; however, most studies are based on simulations or static evaluations. This research paper presents the design, implementation, and experimental evaluation of a prototype early-warning system for vehicle proximity based on VANETs using ESP-NOW. The prototype utilizes five ESP32-CAM nodes equipped with MaxSonar sensors installed in vehicles and an RSU unit with a Raspberry Pi for vehicle-to-infrastructure (V2I) communication. Field tests were conducted in Quito, Ecuador, at speeds ranging from 10 to 70 km/h, measuring latency, packet loss, and received signal strength (RSSI). The results show average latencies of 9.9 ms at 10 km/h and 114.5 ms at 70 km/h, with packet loss rates of 2% and 60%, respectively. Statistical analysis reveals 95% confidence intervals for latency ranging from ±0.98 ms to ±6.90 ms, while obstacles introduce marginal attenuation (p = 0.051) with significant dispersion (σ = 5.85 dB). The Doppler shift is negligible (155.6 Hz), but the channel coherence time (2.7 ms) explains the observed degradation. Models were obtained that relate speed to latency (R² = 0.994) and packet loss (R² = 0.991). The prototype is viable for early collision warning at urban speeds (up to 60 km/h), outperforming human reaction time (1.5 s). Full article

This paper presents a hybrid model that combines linguistic data summarization techniques, algorithms for constructing probabilistic trees, and various generative artificial intelligence models for learning and generating linguistic summaries to aid decision-making. The proposal is validated using methodological triangulation techniques that demonstrate high consistency in the knowledge discovered. The proposal also compares different generative artificial intelligence models; among the evaluated models, Gemini achieved the best performance. However, it is evident that, in certain contexts and tasks, small language models can be effective, yielding results comparable to large language models (LLMs) at a lower computational cost. This study applies the algorithms in a case study analyzing oceanographic data from Northern Chile. In the validation scenario, the combination of linguistic data summarization methods with unsupervised learning techniques effectively models human tolerance for imprecision when processing complex data and generated linguistic summaries easily interpretable by human decision-makers with high levels of confidence. Studies of energy capacities in the studied region and their behavior in both winter and summer are presented. Full article

Mine tailings are highly disturbed technogenic materials whose low mechanical stability may limit mine closure and long-term land rehabilitation. This study evaluates the geomechanical potential of Chrysopogon zizanioides (L.) Roberty, commonly known as vetiver grass, to improve the shear-strength response of filtered mine tailings under controlled laboratory and numerical modelling conditions. The study does not constitute field-scale validation of phytostabilization; rather, it examines the contribution of vetiver roots to apparent cohesion and shallow slope stability. A combined experimental–numerical framework was implemented, including laboratory characterization of unreinforced and root-reinforced tailings, derivation of Mohr–Coulomb shear-strength parameters, and limit-equilibrium slope-stability analysis under predefined root-growth and root-orientation scenarios. The results indicate that vetiver roots increased apparent cohesion by up to 34.6%, whereas changes in friction angle remained below 10%, suggesting that the dominant reinforcement mechanism is pseudo-cohesive rather than frictional. The calculated factors of safety varied according to slope geometry, assumed root length, root orientation, and simplified water-condition scenarios. However, the findings remain limited to controlled experimental and numerical conditions. Field-scale validation, long-term root monitoring, moisture variability, nutrient availability, phytotoxicity, contaminant immobilization, and life-cycle performance should be assessed before practical implementation. This study provides preliminary geomechanical evidence of vetiver-induced root reinforcement in filtered mine tailings. Full article

In the context of economic disparities, housing as a fundamental right highlights processes of social differentiation and stratification. From a complexity perspective, factors such as location, distance from development hubs, and designs that standardize needs exacerbate weaknesses in its conception. The new realities of living in housing prompt us to rethink design approaches that integrate housing and work. This research analyzes the Ciudad Alegría Social Housing Program, located in the city of Loja, Ecuador. The diagnostic method indicated that 24% of homes have commercial projections as a survival strategy. While these spatial patterns diminish the levels of habitability in the homes, they also provide benefits such as proximity between home and work, savings in transportation costs, interaction with neighbors, and mixed uses. These observations reveal gaps in the architectural design process, which fails to consider both service providers and users in decision-making related to the design of VIS programs, highlighting the need for this phenomenon to be elevated to public policy. Full article

There is a lack of knowledge concerning the interlaminar fracture toughness of 3D-printed composite materials using both commercial filament composites and fused deposition modeling (FDM) technology from Markforged^®. In this investigation, additive manufacturing (AM) continuous fiber-reinforced thermoplastic (cFRT) specimens have been tested to characterize the initiation and propagation of interlaminar fracture toughness in mode I (G_I). Unidirectional glass fiber (GF)-reinforced polyamide 6 (PA) laminates were characterized by means of the double cantilever beam (DCB) test. These specimens were manufactured using a MarkTwo^® printer and tested without doublers, following a laminate configuration selected according to appropriate experimental findings reported in the state of the art, ensuring reliable fracture characterization. The experimental results exhibited repeatability and strong agreement between the modified compliance calibration (MCC) and modified beam theory (MBT) reduction methods. The resistance curve (R-curve) indicated a progressive increase in fracture resistance during crack propagation. To analyze the experienced failure mechanism during testing, the fracture surfaces of representative post-mortem DCB specimens were observed using a scanning electron microscope (SEM), revealing characteristic morphological features at two magnification levels. Moreover, representative cross-sections of the tested DCB specimens were electronically observed to analyze the interlaminar morphologies, showing an irregular and random distribution of the matrix, fiber, and voids between consecutive plies and adjacent deposited rasters. Compared with previously reported Markforged^® continuous fiber-reinforced systems, the GF/PA composite material exhibited intermediate initiation fracture toughness but lower propagation toughness. This study contributes to filling the existing gap in fracture toughness data for glass fiber-reinforced additively manufactured composites. Full article

Population aging poses significant challenges to oral communication due to age-related changes in articulation, verbal fluency, and speech pacing, even among older adults without neurodegenerative conditions. Despite advances in voice-based assistive technologies, there remains a lack of integrated engineering solutions that support structured, autonomous speech practice in non-clinical environments. This study proposes a deterministic, rule-based speech evaluation workflow implemented within a hybrid web–mobile assistive system. The workflow integrates audio capture, cloud-based automatic speech recognition (ASR), rule-based pronunciation evaluation, immediate multimodal feedback, and progress monitoring within a unified system architecture. The proposed architecture includes a mobile application for older adults and a web platform for configuration and monitoring by caregivers. A prototyping-oriented methodology was applied, including requirements elicitation, system design, implementation, and usability evaluation using the Thinking Aloud method and the System Usability Scale (SUS). Results showed stable system behavior under controlled evaluation conditions, an average recognition accuracy of 90% during preliminary evaluation sessions, and a response latency of 1.82 s, supporting stable real-time interaction during guided speech exercises. These findings demonstrate the feasibility of the proposed assistive architecture as an accessible and reproducible solution for guided speech support in older adults. Full article

Cutaneous Larva Migrans (CLM) is a neglected tropical disease (NTD) caused by zoonotic Ancylostomatidae larvae, mainly Ancylostoma braziliense and Ancylostoma caninum, which infect dogs and cats. Humans are accidental hosts, acquiring infection when L3 larvae in contaminated soil penetrate the skin, producing serpiginous, pruritic lesions. We report a 24-year-old female from Quito, Ecuador, who developed a pruritic lesion on her right foot nine days after walking barefoot on wet, potentially fecally contaminated sand at Atacames Beach. Initial self-treatment with benzyl benzoate and herbal washes, followed by misdiagnoses as scabies and plantar warts, delayed proper care. Lesions progressed over three weeks with intense pruritus and functional impairment. CLM was correctly diagnosed by a podiatric technician 26 days post-exposure. Oral albendazole (400 mg/day for 4 days) led to rapid symptomatic relief within three days, with complete resolution by day 50. A survey analyzed by the McNemar Test revealed difficulties in recognizing early-stage CLM, regardless of experience or region among participants. Prevention requires personal protection, environmental sanitation, and regular anthelmintic treatment of dogs and cats. This case underscores the clinical consequences of delayed or incorrect diagnosis and highlights the need for enhanced healthcare training and One Health measures to reduce zoonotic diseases in Ecuador. Full article

Biostimulants from Ascophyllum nodosum (L.) are effective as regulators of molecular, physiological and biochemical processes in plants. Two independent experiments were conducted using foliar application in Rosa × hybrida variety White O’Hara of two A. nodosum-based biostimulant formulations (B1: A. nodosum (10% w/v), N, P₂O₅, K, Ca, Mg, oxidizable total organic carbon (3% w/v), minor elements, and free amino acids (3.9% w/v); B2: A. nodosum (11% w/v), oxidizable total organic carbon (6.8% w/v) N (37.2% w/v), and P₂O₅ (50% w/v)). Each experiment was conducted in a Randomized Complete Block Design (RCBD) with a factorial arrangement including four treatments (0; 0.5; 1.0; and 1.5 mL L⁻¹), which were evaluated over two production cycles. Foliar chlorophyll (μmol m⁻²), stomatal conductance (mmol m⁻² s⁻¹), and leaf vapor pressure deficit were measured every two weeks, and productivity was evaluated at the end of the cycle. Statistical differences were detected in chlorophyll content for the application of B1 and B2 over two production cycles with increases of around 16–17% in chlorophyll compared to the control. Significant differences in stomatal conductance were detected during weeks 20 and 22 for all doses. The control treatment consistently exhibited lower means for the leaf vapor pressure deficit compared to B1 and B2. Biostimulants improved photosynthetic activity and carbon assimilation and also delayed leaf senescence. B1 at 1 mL L⁻¹ reduced unproductive stems from 54% to 38% compared to the control. Biostimulant treatments enhanced physiological tolerance to temperature extremes (2.2–32.6 °C). Based on the results, 1.5 mL L⁻¹ of the B1 biostimulant and 1 mL L⁻¹ of the B2 are recommended; these findings offer key insights for optimizing rose cultivation and prove that intensive floriculture can be both productive and sustainable. Full article

AI, specifically Bayesian networks, was applied in this study to diagnose and predict interruptions (whether due to faults or maintenance) that most significantly impact the time of interruption per kilowatt and frequency of maintenance intervention per kilowatt indicators in electrical distribution systems. Bayesian networks were employed to identify which functional stages, such as sub-transmission lines, distribution substations, and medium-voltage networks, exert the greatest influence on these performance metrics. Additionally, the analysis was conducted to categorize the interruption catalog and contribute to these impacts. By disaggregating the service quality and maintenance indicators reported monthly by the Guayas Los Ríos Business Unit of the National Electricity Corporation to Ecuador’s electricity sector regulatory bodies, the developed framework in this study enhances service reliability, optimizes maintenance planning, and reduces interruption times. Bayesian network models generated using R illustrate relationships between interruption causes and their impact on service quality indicators. Furthermore, a comparison of several models, including Naive Bayes, Tree Augmented Naive Bayes (TAN), and Backward Sequential Elimination and Joining (BSEJ), demonstrated that TAN and BSEJ achieved the highest accuracy in predicting interruption outcomes. These insights allow for more efficient targeting of maintenance resources, ultimately reducing the most impactful categories of interruptions and improving overall technical service quality. Full article