Search Results (784)

Accurate estimation of peak water demand remains a challenge in institutional settings with floating populations, particularly in regions where design standards may require revision and validation to accommodate evolving consumption patterns. This pilot study assesses the potential of a probabilistic adaptation of Hunter’s method, calibrated through high-resolution flow and pressure monitoring, for peak flow estimation in five academic buildings in Loja, Ecuador. Over 62 days, usage parameters, duration (t), frequency (i), and peak period (h), were disaggregated from 1 min interval data to derive building-specific binomial probability distributions. The adapted model was compared against three benchmarks: the Neyman–Scott Rectangular Pulse Model (NSRPM), the Water Demand Calculator (WDC), and Ecuador’s Hydro-Sanitary Standard (NHE 2011). Results indicate the proposed approach estimates peak flows within −11.6% to +20.0% of observed values, outperforming WDC (systematic underestimation up to −81.5%) and NHE 2011 (average underestimation of −31.3%), though NSRPM achieved high accuracy for one site (−1.1%) with high inter-building variability (average −38.4%). While limited to a small sample in a single climatic context, this pilot demonstrates that temporal disaggregation of stochastic demand enables a context-sensitive recalibration of Hunter’s method, offering a methodologically sound basis for future validation across diverse institutional settings in the Global South. Full article

Hydropower is the primary source of electricity in several countries in Latin America. Hydropower provides approximately 80% of Ecuador’s electricity; however, it remains highly vulnerable to climate change, resulting in uncertainties in power generation due to altered precipitation patterns, runoff, and systematic failures. Consequently, Ecuadorians are becoming increasingly reliant on diesel generators during crises, resulting in public health, safety, and economic impacts, as well as social and political disruptions. This study evaluated noise pollution in the central urban area of the city of Loja for the first time during the 2024–2025 electricity crisis in Ecuador. A Type 1 integrating sound-level meter was used to monitor noise pollution (LAeq, 10min) at 20 locations during periods of generator operation and non-operation. At each location, the number of generators, the density of commercial activities along the streets, as well as traffic and other urban characteristics, were recorded. Results revealed that the presence of generators, street width, and the number of generators significantly increased the LAeq, 10min, often exceeding the limits set by the World Health Organization and Ecuador’s environmental regulations. Frequency spectrum analysis revealed that medium frequencies increased with A-weighting, while low frequencies rose with C-weighting, suggesting potential health risks to the local population. The thematic noise map during generator inactivity showed lower noise levels, averaging around 71.5 dBA. Conversely, when the generators were operational, noise levels exceeded 79.6 dBA, indicating a significant increase in environmental noise exposure associated with their use. This highlights an urgent need to implement and expand renewable energy sources, as existing options like wind power, photovoltaic energy, and biomass are insufficient to meet community demands. Full article

This study presents a multi-resolution and multi-temporal remote sensing approach to assess human-induced changes in cultural landscapes, with a focus on the archaeological site of Amrit (Syria) within the MapDam project. By integrating satellite archives (KH, Landsat series, NASADEM) with ancillary geospatial data (OpenStreetMap) and advanced analytical methods, four decades (1984–2024) of land-use/land-cover (LULC) change and shoreline dynamics were reconstructed. Machine learning classification (Random Forest) achieved high accuracy (Test Accuracy = 0.94; Kappa = 0.89), enabling robust LULC mapping, while predictive modelling of urban expansion, calibrated through a Gradient Boosting Machine, attained a Figure of Merit of 0.157, confirming strong predictive reliability. The results reveal path-dependent urban growth concentrated on low-slope terrains (≤5°) and consistent with proximity to infrastructure, alongside significant shoreline regression after 1974. A Business-as-Usual projection for 2024–2034 estimates 8.676 ha of new anthropisation, predominantly along accessible plains and peri-urban fringes. Beyond quantitative outcomes, this study demonstrates the replicability and scalability of open-source, data-driven workflows using Google Earth Engine and Python 3.14, making them applicable to other high-risk heritage contexts. This transparent methodology is particularly critical in conflict zones or in regions where cultural assets are neglected due to economic constraints, political agendas, or governance limitations, offering a powerful tool to document and safeguard endangered archaeological landscapes. Full article

Background: Minimally invasive hyperthermia and regenerative therapies require materials that deliver precise, localized heat without compromising biocompatibility. Most conventional polymers are thermally insulating and challenging to control in vivo, motivating this review. Objectives: We aimed to (i) examine the use of thermally enhanced biopolymers in hyperthermia-based therapies, (ii) appraise evidence from clinical and preclinical studies, (iii) identify and classify principal applications in regenerative medicine. Methods: A PRISMA-guided systematic review (2020–2025) with predefined inclusion/exclusion criteria was conducted and complemented by a bibliometric analysis using VOSviewer for mapping and visualization. Results: Modifying biopolymers—via functionalization with photothermal or magnetic nanoagents (Au; Fe₂O₃/Fe₃O₄/CoFe₂O₄; CuS; Ag; MXenes, e.g., Nb₂C), crosslinking strategies, and hybrid formulations—significantly increased thermal conductivity, enabling localized hyperthermia and controlled drug release. In vitro and in vivo studies showed that europium-doped iron oxide nanoparticles embedded in chitosan generated heat efficiently while sparing healthy tissues, underscoring the need to balance biocompatibility and thermal performance. Hydrogel systems enriched with carbon nanomaterials (graphene, carbon nanotubes) and matrices such as GelMA, PNIPAM, hyaluronic acid, and PLA/PLGA demonstrated tissue compatibility and effective thermal behavior; graphene was compatible with neural tissue without inducing inflammation. Conclusions: Thermally conductive biopolymers show growing potential for oncology and regenerative medicine. The evidence supports further academic and interdisciplinary research to optimize safety, performance, and translational pathways. Full article

Slope instability in mountainous regions poses serious challenges to infrastructure resilience, particularly under anthropogenic pressures and climatic variability. This study evaluates the mechanical reinforcement provided by Vetiver (Chrysopogon zizanioides) to stabilize slopes in seven representative soils of the Quillollaco Formation, southern Ecuador. Direct shear tests on soil–root composites were used to quantify root-induced improvements in cohesion (c′) and internal friction angle (ϕ′), and slope stability was analyzed with the Spencer limit equilibrium method for heights of 5, 10, and 15 m under dry conditions. Results demonstrated significant reinforcement effects, with Vetiver increasing shear strength parameters at all depths and maintaining safety factors above 1.10 in every scenario. The highest gains, up to 16%, were observed in high-plasticity soils (CH and MH). Sensitivity and inferential statistical analyses confirmed the robustness of these improvements, with over 95% of paired t-tests yielding p < 0.05. These findings highlight Vetiver as a reliable nature-based solution for slope stabilization in tropical mountain environments, offering a sustainable and transferable alternative to conventional geotechnical methods. Full article

Plant pathogenic fungi pose a persistent global threat to food security, causing severe yield losses in staple crops and increasing dependence on chemical fungicides. However, the ecological and toxicological drawbacks of synthetic fungicides have intensified the search for safer, plant-derived alternatives. This review synthesizes current advances on the antifungal mechanisms of plant essential oils (EOs) and their prospects for biofungicide development. The literature reveals that the antifungal activity of EOs arises from their diverse phytochemical composition, principally terpenes, phenolics, and aldehydes that target multiple fungal cellular sites. These compounds disrupt membrane integrity through ergosterol depletion, inhibit chitin and β-glucan synthesis, interfere with mitochondrial energy metabolism, and induce oxidative stress, leading to lipid peroxidation and cell death. Morphological and transcriptomic evidence confirms that EOs alter hyphal growth, spore germination, and key gene expression pathways associated with fungal virulence. Furthermore, emerging nanotechnological and encapsulation strategies enhance EO stability, bioavailability, and field persistence, addressing major barriers to their large-scale agricultural application. The integration of EO-based biofungicides within sustainable and precision agriculture frameworks offers a promising route to reduce chemical inputs, mitigate resistance development, and promote ecological balance. This review underscores the need for interdisciplinary research linking phytochemistry, nanotechnology, and agronomy to translate EO-based antifungal mechanisms into next-generation, environmentally compatible crop protection systems. Full article

This study develops an optical surface plasmon resonance (SPR) biosensing platform for non-invasive glucose detection directly in urine and examines how two-dimensional (2D) nanomaterials modulate sensing performance. Angular interrogation at 633 nm is modeled using a transfer-matrix framework for Au/Si₃N₄ stacks capped with graphene, semiconducting single-walled carbon nanotubes (s-SWCNTs), graphene oxide (GO), or reduced graphene oxide (rGO). Urine–glucose (UGLU) refractive indices spanning clinically relevant concentrations are used to evaluate resonance angle shifts and line-shape evolution. Sensor metrics—sensitivity, detection accuracy, figure of merit, quality factor, and limit of detection—are computed to compare architectures and identify thickness windows. Across all designs, increasing glucose concentration produces monotonic angle shifts, while the 2D overlayer governs dip depth and full width at half maximum. Graphene- and s-SWCNT-capped stacks yield the lowest limits of detection and the most favorable figures of merit, particularly at higher concentrations where narrowing improves the quality factor. rGO exhibits a thin, low-loss regime that provides large shifts with acceptable broadening, whereas thicker films degrade detectability; GO offers stable line shapes suited to metrological robustness. These results indicate that nanoscale optical engineering of 2D overlayers can meet practical detectability targets in urine without biochemical amplification, supporting compact, label-free platforms for routine glucose monitoring. Full article

Waterlogging is an increasing constraint to crop productivity under climate change, yet information on quinoa’s sensitivity to excess water remains limited. This study aimed to identify the most vulnerable phenological stage of quinoa to waterlogging stress. A greenhouse experiment was conducted using the variety Tunkahuan, applying waterlogging (12 days) at six growth stages according to the BBCH scale, plus a well-drained control. Growth, chlorophyll content, biomass, yield components, and harvest index were assessed. Waterlogging significantly reduced growth and yield at all stages (p ≤ 0.05). Early vegetative stress reduced canopy cover and chlorophyll, with partial recovery after stress removal, but caused 73% plant mortality at BBCH 25. Reproductive stress, especially at anthesis (BBCH 60–67), was most critical, reducing yield by 71–77% compared to the control due to severe declines in grain number and harvest index. Stress during grain filling caused moderate yield reductions (22–28 g plant⁻¹). We conclude that quinoa is sensitive to waterlogging throughout its development, but anthesis is the most vulnerable stage for irreversible yield loss. These findings highlight the need for breeding programs targeting waterlogging tolerance and improved management practices to minimize flooding risk during flowering. Full article

Background: Molecular mimicry contributes to the development of unwanted responses to self-antigens. Autoimmune phenomena have been observed in diseases caused by Aedes aegypti-transmitted arboviruses, but the occurrence of mimicry between salivary and human proteins has been unexplored. Methods: We used bioinformatic tools to determine if peptides from Aedes aegypti salivary proteins were present in the human proteome. We further characterized the potential of shared sequences to induce immunity by analyzing their predicted binding to MHC molecules and their occurrence in peptides from the Immune Epitope Database (IEDB). Results: We analyzed 9513 octapeptides from 29 Aedes aegypti salivary proteins against the human proteome and found 47 peptides identical to sequences from 52 human proteins, ranging in length from 8 to 18 amino acids. We found 302 matches of peptides predicted to bind with high affinity to MHC-I and MHC-II alleles associated with autoimmune diseases, and 14 human peptides containing shared sequences with Aedes aegypti salivary proteins validated as immunogenic in the IEDB. Conclusions: These results support the existence of molecular mimicry between Aedes aegypti salivary proteins and human antigens and provide a framework for studies to determine its contribution to responses directed to self-antigens in the context of arboviral infections. Full article

The identification of producer typologies is a crucial tool for understanding the heterogeneity of agroecosystems and designing targeted policies. Andean agroecosystems, particularly those in rapidly suburbanizing areas, have been understudied in this regard, creating a critical knowledge gap. This study addressed this void by determining the typologies of smallholder agroecosystems in the suburban periphery of Cuenca, Ecuador, by applying an unsupervised machine learning technique, Partitioning Around Medoids (PAM) Clustering, to survey data from 293 farmers. Our analysis revealed three distinct typologies, highlighting a socio-economic and productive gradient defined by income sources, market access, and agrochemical use. The typologies range from economically vulnerable households to more commercially oriented and environmentally sustainable ones, underscoring the complex interplay between livelihoods strategies and environmental management. This research provides one of the first empirical typologies of suburban Andean agroecosystems, demonstrating the value of unsupervised learning for capturing farm heterogeneity in data-scarce contexts. The findings offer a robust evidence base for moving beyond one-size-fits-all approaches, enabling the design of differentiated agricultural and territorial policies that enhance sustainability, equity, and resilience at the rural–urban interface. Full article

Electroencephalography (EEG) provides insights into the neural mechanisms underlying attention, response inhibition, and distraction in cognitive tasks. This dataset was collected to examine neural activity in young drivers and non-drivers performing Go/No-Go tasks under visual and auditory distraction conditions. A total of 40 university students (20 drivers, 20 non-drivers; balanced by sex) completed eight experimental blocks combining visual or auditory stimuli with realistic distractions, such as text message notifications and phone call simulations. EEG was recorded using a 16-channel BrainAccess MIDI system at 250 Hz. Experiments 1, 3, 5, and 7 served as transitional blocks without participant responses and were excluded from behavioral and event-related potential analyses; however, their EEG recordings and event markers are included for baseline or exploratory analyses. The dataset comprises raw EEG files, event markers for Go/No-Go stimuli and distractions, and metadata on participant demographics and mobile phone usage. This resource enables studies of attentional control, inhibitory processes, and distraction-related neural dynamics, supporting research in cognitive neuroscience, brain–computer interfaces, and transportation safety. Full article

Urban mobility plays a critical role in ensuring resilience during natural disasters such as floods, yet developing reliable traffic models remains challenging for medium-sized cities with limited monitoring infrastructure. This study developed a hybrid traffic modeling approach that integrates Google Traffic data with field measurements to address incomplete digital coverage in flood-prone areas of Loja, Ecuador. The methodology involved collecting 1501 field speed measurements and 235,690 Google Typical Traffic observations using exclusively open-source tools and freely available data sources. Adjustment factors ranging from 0.25 to 0.97 revealed systematic discrepancies between Google Traffic estimates and field observations, highlighting the need for local calibration. The resulting traffic network model encompassing 4966 nodes and 5425 edges accurately simulated flood impacts, with the most critical scenario (Thursday 17–19, 100% road impact) showing travel time increases of 1123% and congestion index deterioration from 1.79 to 21.69. Statistical validation confirmed significant increases in both travel times (p = 0.0231) and distances (p = 0.0207) under flood conditions across five representative routes. This research demonstrates that accurate traffic models can be developed through intelligent integration of heterogeneous data sources, providing a scalable solution for enhancing urban mobility analysis and emergency preparedness in resource-constrained cities facing climate-related transportation challenges. Full article

Essential oils from the genus Piper are recognized for their chemical diversity and biological potential, yet Piper platylobum has been scarcely investigated. This study aimed to characterize the chemical composition of the leaf essential oil of P. platylobum and evaluate its antimicrobial, antioxidant, and anticholinesterase activities. The oil was obtained by steam distillation and analyzed through gas chromatography–mass spectrometry (GC-MS) and gas chromatography equipped with a flame ionization detector (GC-FID), leading to the identification of 35 compounds that accounted for 91.11% of the volatile fraction. Dillapiole (42.0%) was the principal constituent, followed by α-(E)-bergamotene (5.69%), (E)-caryophyllene (5.01%), and (E)-isocroweacin (3.75%). Biological assays revealed selective antimicrobial activity, with inhibition observed only against Enterococcus faecium (MIC = 1000 µg/mL), while no effect was detected against other bacterial or fungal strains tested. Antioxidant evaluation showed moderate activity in the ABTS assay (SC₅₀ = 335.71 ± 1.43 µg/mL; TEAC = 45.85 ± 1.68 µM Trolox/g EO), but no activity in the DPPH assay. The essential oil also displayed moderate inhibition of acetylcholinesterase (IC₅₀ = 76.86 ± 1.00 µg/mL), suggesting a potential role in neuroprotective applications. This study constitutes the first report on the chemical composition and biological activities of P. platylabum essential oil, highlighting its potential as a novel source of bioactive compounds. Full article

The development of advanced drug delivery systems is essential for improving therapeutic efficacy, particularly in the treatment of neurodegenerative disorders such as Alzheimer’s disease. This study investigates zinc-modified mordenite zeolite (MR-ZN) as a novel platform for the controlled delivery of donepezil (DPZ), a cholinesterase inhibitor. Natural mordenite was modified with zinc, enhancing its surface area from 62.1 to 85.4 m²/g and improving its adsorption properties. Donepezil was successfully loaded at two doses (10 mg and 23 mg), achieving high loading efficiencies of 95% and 94%, respectively. Adsorption kinetics followed a pseudo-second-order model (R² > 0.99), indicating that chemisorption predominates through coordination between DPZ functional groups and Zn²⁺ sites, while complementary physisorption via hydrogen bonding and van der Waals interactions also contributes to molecular stabilization within the zeolite framework. In vitro release studies under simulated gastrointestinal conditions demonstrated sustained and pH-responsive release profile with 80% and 82% of donepezil released after 24 h for 10 mg and 23 mg formulations, respectively. Density Functional Theory (DFT) calculations revealed favorable adsorption energy (−26.4 kJ/mol), while Bader and Electron Localization Function (ELF) analyses confirmed hydrogen bonding and electrostatic interactions without compromising the zeolite framework. These findings validate MR-ZN as structurally stable, efficient, cost-effective and biocompatible matrix for oral drug delivery. The combination of experimental data and theoretical modeling supports its potential to improve bioavailability and therapeutic performance in neurodegenerative treatment. Full article