Search Results (1,345)

The electrical power system is composed of three essential sectors, generation, transmission, and distribution, with the latter being crucial for the overall efficiency of the system. Enhancing the capabilities of active distribution networks involves integrating various advanced technologies such as distributed generation units, energy storage systems, banks of capacitors, and electric vehicle chargers. This paper provides an in-depth review of the primary strategies for incorporating these technologies into the distribution network to improve its reliability, stability, and efficiency. It also explores the principal metaheuristic techniques employed for the optimal allocation of distributed generation units, banks of capacitors, energy storage systems, electric vehicle chargers, and network reconfiguration. These techniques are essential for effectively integrating these technologies and optimizing the active distribution network by enhancing power quality and voltage level, reducing losses, and ensuring operational indices are maintained at optimal levels. Full article

Background/Objectives: Hemodialysis catheter-related bloodstream infection (HD-CRBSIs) is a main cause of morbidity in hemodialysis. New preventive strategies have emerged, such as using lock solutions with antiseptic or antibiotic capacity. In this study, the antimicrobial effect was analyzed in vitro and with a catheter model of lock solutions of gentamicin (LSG), gentamicin/heparin (LSG/H), and gentamicin/citrate (LSG/C) in clinical and ATCC strains of Pseudomonas aeruginosa and Staphylococcus aureus. Methods: The formation, minimum inhibitory concentration, and minimum inhibitory concentration of the biofilm and minimum biofilm eradication concentration of the lock solutions were determined. Additionally, colony-forming unit assays were performed to evaluate the antimicrobial efficacy of the lock solutions in a hemodialysis catheter inoculation model. Results: The minimum inhibitory concentration (MIC) of planktonic cells of both P. aeruginosa and S. aureus for LSG/H and LSG/C was 4 µg/mL. In the minimum biofilm inhibitory concentration (MBIC) tests, the LSG/H was less effective than LSG/C, requiring higher concentrations for inhibition, contrary to the minimum biofilm eradication concentration (MBEC), where LSG/H was more effective. All lock solutions eradicated P. aeruginosa biofilms in the HD catheter model under standard conditions. Nevertheless, under modified conditions, the lock solutions were not as effective versus ATCC and clinical strains of S. aureus. Conclusions: Our analysis shows that the lock solutions studied managed to eradicate intraluminal mature P. aeruginosa in non-tunneled HD catheters under standard conditions. Biofilm inhibition and eradication were observed at low gentamicin concentrations, which could optimize the gentamicin concentration in lock solutions used in HD catheters. Full article

This paper introduces a robust and imperceptible video watermarking framework designed for blind extraction in dynamic video environments. The proposed method operates in the spatial domain and combines multiscale perceptual analysis, adaptive Just Noticeable Difference (JND)-based quantization, and temporal redundancy via multiframe embedding. Watermark bits are embedded selectively in blocks with high perceptual masking using a QIM strategy, and the corresponding DCT coefficients are estimated directly from the spatial domain to reduce complexity. To enhance resilience, each bit is redundantly inserted across multiple keyframes selected based on scene transitions. Extensive simulations over 21 benchmark videos (CIF, 4CIF, HD) validate that the method achieves superior performance in robustness and perceptual quality, with an average Bit Error Rate (BER) of 1.03%, PSNR of 50.1 dB, SSIM of 0.996, and VMAF of 97.3 under compression, noise, cropping, and temporal desynchronization. The system outperforms several recent state-of-the-art techniques in both quality and speed, requiring no access to the original video during extraction. These results confirm the method’s viability for practical applications such as copyright protection and secure video streaming. Full article

Leptospirosis is a globally significant zoonosis affecting animal health, productivity, and the environment. While typically associated with tropical climates, its persistence in semi-arid regions such as La Laguna, Mexico—characterized by low humidity, high temperatures, and limited water sources—remains poorly understood. Although these adverse environmental conditions theoretically limit the survival of Leptospira, high livestock density and synanthropic reservoirs (e.g., rodents) may compensate, facilitating transmission. In this cross-sectional study, blood sera from 445 dairy cows (28 herds: 12 intensive [MI], 16 semi-intensive [MSI] systems) were analyzed via microscopic agglutination testing (MAT) against 10 pathogenic serovars. Urine samples were cultured for active Leptospira detection. Risk factors were assessed through epidemiological surveys and multivariable analysis. This study revealed an overall apparent seroprevalence of 27.0% (95% CI: 22.8–31.1), with significantly higher rates in MSI (54.1%) versus MI (12.2%) herds (p < 0.001) and an estimated true seroprevalence of 56.3% (95% CI: 50.2–62.1) in MSI and 13.1% (95% CI: 8.5–18.7) in MI herds (p < 0.001). The Sejroe serogroup was isolated from urine in both systems, confirming active circulation. In MI herds, rodent presence (OR: 3.6; 95% CI: 1.6–7.9) was identified as a risk factor for Leptospira seropositivity, while first-trimester abortions (OR:10.1; 95% CI: 4.2–24.2) were significantly associated with infection. In MSI herds, risk factors associated with Leptospira seropositivity included co-occurrence with hens (OR: 2.8; 95% CI: 1.5–5.3) and natural breeding (OR: 2.0; 95% CI: 1.1–3.9), whereas mastitis/agalactiae (OR: 2.8; 95% CI: 1.5–5.2) represented a clinical outcome associated with seropositivity. Despite semi-arid conditions, Leptospira maintains transmission in La Laguna, particularly in semi-intensive systems. The coexistence of adapted (Sejroe) and incidental serogroups underscores the need for targeted interventions, such as rodent control in MI systems and poultry management in MSI systems, to mitigate both zoonotic and economic impacts. Full article

Background/Objectives: In recent years, the discharge of antibiotics into rivers and irrigation canals has increased. However, few studies have addressed the impact of these compounds on agricultural fields that use such water to meet crop demands. Methods: In this study, the transport of two types of gentamicin (pure gentamicin and gentamicin sulfate) was modeled at concentrations of 150 and 300 μL/L, respectively, in a soil with more than 60 years of agricultural use. Infiltration tests under constant head conditions and gentamicin transport experiments were conducted in acrylic columns measuring 14 cm in length and 12.7 cm in diameter. The scaling parameters for the Richards equation were obtained from experimental data, while those for the advection–dispersion equation were estimated using inverse methods through a nonlinear optimization algorithm. In addition, a fractal-based model for saturated hydraulic conductivity was employed. Results: It was found that the dispersivity of gentamicin sulfate is 3.1 times higher than that of pure gentamicin. Based on the estimated parameters, two simulation scenarios were conducted: continuous application of gentamicin and soil flushing after antibiotic discharge. The results show that the transport velocity of gentamicin sulfate in the soil may have short-term consequences for the emergence of resistant microorganisms due to the destination of wastewater containing antibiotic residues. Conclusions: Finally, further research is needed to evaluate the impact of antibiotics on soil physical properties, as well as their effects on irrigated crops, animals that consume such water, and the soil microbiota. Full article

Biofilms, structured communities of microorganisms encased in an extracellular matrix, are a major cause of persistent infections, particularly when formed on medical devices. This study investigated the kinetics of biofilm formation by Escherichia coli and Pseudomonas aeruginosa, two clinically significant pathogens, on two medical-grade polymers: polyether ether ketone (PEEK) and polyamide 12 (PA12). Using a modified crystal violet staining method and spectrophotometric quantification, we evaluated biofilm development over time on polymer granules and catheter segments composed of these materials. Results revealed that PEEK surfaces supported significantly more biofilm formation than PA12, with peak accumulation observed at 24 h for both pathogens. Conversely, PA12 demonstrated reduced bacterial adhesion and lower biofilm biomass, suggesting surface characteristics less conducive to microbial colonization. Additionally, the study validated a reproducible protocol for assessing biofilm formation, providing a foundation for evaluating anti-biofilm strategies. While the assays were performed under static in vitro conditions, the findings highlight the importance of material selection and early prevention strategies in the design of infection-resistant medical devices. This work contributes to the understanding of how surface properties affect microbial adhesion and underscores the critical need for innovative surface modifications or coatings to mitigate biofilm-related healthcare risks. Full article

This study evaluated the effects of liquid oregano oil, chitosan nanoparticles with encapsulated liquid oregano oil, and a negative control of empty chitosan nanoparticles on in vitro ruminal fermentation. Three Boer goats were used as ruminal fluid donors, fed with a formulated ration for 21 d for inoculum adaptation. Treatments tested on in vitro assays were diet without oregano oil or nanoparticles (CON); diet with 100 ppm of oregano oil in nanoparticles (100N); diet with 300 ppm of liquid oregano oil (300L); diet with 300 ppm of oregano oil in nanoparticles (300N); and diet with 300 ppm of empty nanoparticles (300CHN). The variables studied were in vitro dry matter digestibility (ivDMD), in vitro neutral detergent fiber digestibility (ivNDFD_om), total gas production (TGP), ammonia nitrogen concentration (NH₃), and pH. The experimental design was a randomized complete block design. Linear and quadratic regressions were used to identify dependence and inflection points. The ivDMD increased at 12, 36, and 48 h, with 300N and with 300L exhibiting increased ivNDFD_om at 36 h. Ruminal pH was highest (p < 0.05) with 300CHN at 36 h. For first-order regression analysis of TGP, coefficients (β) were highest (p < 0.05) for 300N. In conclusion, 300N increased ruminal fermentation in vitro, as reflected by increases in ivDMD, ivNDFD_om, and TGP. Full article

Precipitation-hardenable stainless steels (PHSS) are widely used in various applications in the aeronautical industry such in as landing gear supports, actuators, and fasteners, among others. This research aims to study the pitting corrosion behavior of passivated martensitic precipitation-hardening stainless steel, which underwent passivation for 120 min at 25 °C and 50 °C in citric and nitric acid baths before being immersed in solutions containing 1 wt.% sulfuric acid (H₂SO₄) and 5 wt.% sodium chloride (NaCl). Electrochemical characterization was realized employing electrochemical noise (EN), while microstructural analysis employed scanning electron microscopy (SEM). The result indicates that EN reflects localized pitting corrosion mechanisms. Samples exposed to H₂SO₄ revealed activation–passivation behavior, whereas those immersed in NaCl exhibited pseudo-passivation, indicative of an unstable oxide film. Current densities in both solutions ranged from 10⁻³ to 10⁻⁵ mA/cm², confirming susceptibility to localized pitting corrosion in all test conditions. The susceptibility to localized attack is associated with the generation of secondary oxides on the surface. Full article

Background: Acute appendicitis is a common emergency requiring abdominal surgery. Despite its prevalence, there are no specific biomarkers for its diagnosis and prognosis. The aim of this study was to assess the basic laboratory tests of patients with acute appendicitis and to evaluate and integrate biochemical variables into the diagnosis of appendicitis. Methods: This was a retrospective, cross-sectional cohort study that included data from patients who underwent an appendectomy. Two groups of patients were considered based on their surgical (non-complicated/complicated appendicitis) or pathological diagnosis (non-perforated/perforated appendicitis). Factor analysis was carried out to identify communalities to put forward classificatory indices. Receiver operating characteristic (ROC) analysis was used to assess the accuracy of the predictions. Results: The cohort included 246 patients (51.6% male, mean age: 24.79 ± 19.32 years). By using their biochemical data, we generated 6 new indices whose areas under the ROC curve (AUC) ranged between 0.632 and 0.762 for complicated appendicitis and from 0.597 to 0.742 for perforated appendicitis. Inflammatory Metabolic Index (IMI) at the fixed cutoffs was a promising biomarker for both histopathological and surgical diagnoses with odds ratios (OR) of 10.45 and 5.21, respectively. The Metabolic-Inflammatory Stress Index (MISI) showed high specificity (over 72%) and significant AUC values for both diagnoses (0.742 and 0.676). These findings were reinforced by significant p-values and Youden indices. Conclusions: IMI and MISI were demonstrated to be effective biomarkers for complicated and perforated appendicitis. IMI provides predictive capability, while MISI offers specificity and significant AUC values for both histopathological and surgical diagnoses. Incorporating these biomarkers could enhance the accuracy of appendicitis diagnosis and potentially guide clinical decision-making. Full article

Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. Full article

Data analysis and machine learning have become essential cross-disciplinary skills for engineering students and professionals. Traditionally, these topics are taught through lectures or online courses using pre-existing datasets, which limits the opportunity to engage with the full cycle of data analysis and machine learning, including data collection, preparation, and contextualization of the application field. To address this, we designed and implemented a learning activity that involves students in every step of the learning process. This activity includes multiple stages where students conduct experiments to record their own electroencephalographic (EEG) signals and use these signals to learn data analysis and machine learning techniques. The purpose is to actively involve students, making them active participants in their learning process. This activity was implemented in six courses across four engineering careers during the 2023 and 2024 academic years. To validate its effectiveness, we measured improvements in grades and self-reported motivation using the MUSIC model inventory. The results indicate a positive development of competencies and high levels of motivation and appreciation among students for the concepts of data analysis and machine learning. Full article

This paper calculates the expected cost of uncertainty in solar and wind energy using the uncertainty cost function (UCF), with a primary focus on computational processing time. The comparison of processing time for the uncertainty cost quantification (UCQ) is conducted through three methods: the Monte Carlo simulation method (MC), numerical integration method, and analytical method. The MC simulation relies on random simulations, while numerical integration employs established numerical formulations. These methods are commonly used for solving cost optimization problems in power systems. However, the analytical method is a less conventional approach. The analytical method for calculating uncertainty costs is closely related to the UCF, as it relies on a mathematical representation of the impact of uncertainty on costs, which is modeled through the UCF. A multi-objective approach was employed for scheduling an energy management system, that is to say, thermal–wind–solar energy systems, proposing a simplified method for modeling controllable renewable generation through UCF with an analytical method, instead of the complex probability distributions typically used in traditional methods. This simplification reduces complexity and computational processing time in optimization problems, offering greater accuracy in approximating real distributions and adaptability to various scenarios. The simulations performed yielded positive results in improving cost estimation and computational efficiency, making it a promising tool for enhancing economic distribution and grid operability. Full article

Traditional taco preparation methods, such as oil immersion and steaming, can significantly affect the nutritional and metabolic characteristics of the final product. This study evaluated tacos made with five commercial nixtamalized maize flours and four common fillings (chicharron, beef skirt, potato, and refried beans), processed using three different methods: Plain, Full-Fat, and Patted-Dry. We assessed their chemical composition, starch digestibility, and thermal properties, and measured satiety-related hormone responses in mice. Fillings had a stronger influence on protein, fat, and moisture content than tortilla type. Full-fat tacos exhibited increased amylose–lipid complex formation and a lower gelatinization enthalpy, whereas plain tacos retained more retrograded starch and a crystalline structure. In vitro digestion revealed that Plain tacos, especially those with plant-based fillings, had the highest resistant starch content and the lowest predicted glycemic index. Hierarchical clustering showed that resistant starch, moisture, and gelatinization onset temperature were closely linked in the Plain samples, whereas lipid-driven variables dominated in the Full-Fat tacos. In mice, tacos with a higher resistant starch content led to greater GLP-1 levels, lower ghrelin levels, and reduced insulin responses, suggesting improved satiety and glycemic control. Patted-Dry tacos showed intermediate hormonal effects, supporting their potential as a balanced, health-conscious alternative. These findings demonstrate how traditional preparation techniques can be leveraged to enhance the nutritional profile of culturally relevant foods, such as tacos. Full article

The 100-Day Mission, coordinated by the Coalition for Epidemic Preparedness Innovations (CEPI) and endorsed by significant international stakeholders, aims to shorten the timeframe for developing and implementing vaccines to 100 days after the report of a new pathogen. This ambitious goal is outlined as an essential first step in improving pandemic preparedness worldwide. This review highlights the mission’s implementation potential and challenges by examining it through the lens of low- and middle-income countries (LMICs), which often face barriers to equitable vaccine access. This article explores the scientific, economic, political, and social aspects that could influence the mission’s success, relying on lessons learned from previous pandemics, such as the Spanish flu, H1N1, and COVID-19. We also examined important cornerstones like prototype vaccine libraries, accelerated clinical trial preparedness, early biomarkers identification, scalable manufacturing capabilities, and rapid pathogen characterization. The review also explores the World Health Organization (WHO) Pandemic Agreement and the significance of Phase 4 surveillance in ensuring vaccine safety. We additionally evaluate societal issues that disproportionately impact LMICs, like vaccine reluctance, health literacy gaps, and digital access limitations. Without intentional attempts to incorporate under-resourced regions into global preparedness frameworks, we argue that the 100-Day Mission carries the risk of exacerbating already-existing disparities. Ultimately, our analysis emphasizes that success will not only rely on a scientific innovation but also on sustained international collaboration, transparent governance, and equitable funding that prioritizes inclusion from the beginning. Full article

Energy production is a global concern, encouraging the search for sustainable alternatives such as charcoal, a promising solid biofuel. This study evaluated the effects of temperature and carbonisation time on charcoal produced from Quercus wood. Carbonisation was carried out at 550 °C for 30 min, 700 °C for 30 min and under two progressive heating profiles: one starting at 550 °C for 30 min and increasing to 700 °C for a further 30 min, and another starting at 300 °C for 2 h and rising to 1000 °C for 10 min. Mass and volumetric yield, bulk density, proximate analysis, calorific value, energy yield and fuel ratio were determined. The results showed that carbonisation temperature affected charcoal properties. Mass and volumetric yields were highest at 550 °C (30.10% and 4.81 m³ t⁻¹) in Q. convallata and Q. urbanii. At higher temperatures, bulk density (0.56 g cm⁻³), fixed carbon (91.51%) and calorific value (32.82 MJ kg⁻¹) increased in Q. urbanii. Lower temperatures led to lower moisture levels (2.46%) and a higher energy yield (48.02%). Overall, temperatures above 700 °C improved energy properties, while those below 550 °C favoured higher yields. Species’ characteristics also influenced charcoal quality. These findings offer valuable insights into optimising the carbonisation of Quercus species and supporting the development of more efficient, sustainable charcoal production methods. Full article