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Although residues and resource shortages are major problems of modern living, these topics are not usually properly covered in university curricula. To tackle this situation, we have guided undergraduate students through the design of a group of active learning activities, to demonstrate the science behind separation and recycling: trash separation and recycling of paper, plastic, oil, and metals (lithium and cobalt available in mobile phone batteries). With a better understanding of the underlying scientific processes and fundamentals, undergraduate students were engaged to undertake and develop adequate physicochemical procedures for waste separation and recycling. A selection of the activities was presented at a science and technology fair at the University of the Balearic Islands as outreach activities that enrolled undergraduate students acting as teachers for fair visitors. The results showed a good engagement rate of the involved undergraduates as well as a stimulated visitors' interest.

The Dicentric Chromosome Assay (DCA) is widely used in biological dosimetry, where the number of dicentric chromosomes induced by ionizing radiation (IR) exposure is quantified to estimate the absorbed radiation dose an individual has received. Dicentric chromosome scoring is a laborious and time-consuming process which is performed manually in most cytogenetic biodosimetry laboratories. Further, dicentric chromosome scoring constitutes a bottleneck when several hundreds of samples need to be analyzed for dose estimation in the aftermath of large-scale radiological/nuclear incident(s). Recently, much interest has focused on automating dicentric chromosome scoring using Artificial Intelligence (AI) tools to reduce analysis time and improve the accuracy of dicentric chromosome detection. Our study aims to detect dicentric chromosomes in metaphase plate images using an ensemble of artificial neural network detectors suitable for datasets that present a low number of samples (in this work, only 50 images). In our approach, the input image is first processed by several operators, each producing a transformed image. Then, each transformed image is transferred to a specific detector trained with a training set processed by the same operator that transformed the image. Following this, the detectors provide their predictions about the detected chromosomes. Finally, all predictions are combined using a consensus function. Regarding the operators used, images were binarized separately applying Otsu and Spline techniques, while morphological opening and closing filters with different sizes were used to eliminate noise, isolate specific components, and enhance the structures of interest (chromosomes) within the image. Consensus-based decisions are typically more precise than those made by individual networks, as the consensus method can rectify certain misclassifications, assuming that individual network results are correct. The results indicate that our methodology worked satisfactorily in detecting a majority of chromosomes, with remarkable classification performance even with the low number of training samples utilized. AI-based dicentric chromosome detection will be beneficial for a rapid triage by improving the detection of dicentric chromosomes and thereby the dose prediction accuracy.

Cancer and infectious diseases are two of the world’s major public health problems. Gemcitabine (GEM) is an effective chemotherapeutic agent against several types of cancer. In this study, we developed macrocapsules incorporating GEM into a chitosan matrix blended with magnetite and zeolite by ionic gelation. Physicochemical characterization was performed using HRTEM-ED, XRD, FESEM–EDS, FT-IR, TGA, encapsulation efficiency (%E.E.), and release profiles at pHs 7.4 and 5.0. Cell viability tests against A549 and H1299 cell lines, and microbiological properties against staphylococcal strains were performed. Our results revealed the successful production of hemispherical capsules with an average diameter of 1.22 mm, a rough surface, and characteristic FT-IR material interaction bands. The macrocapsules showed a high GEM encapsulation efficiency of over 86% and controlled release over 24 h. Cell viability assays revealed that similar cytotoxic effects to free GEM were achieved with a 45-fold lower GEM concentration, suggesting reduced dosing requirements and potentially fewer side effects. Additionally, the macrocapsules demonstrated potent antimicrobial activity, reducing Staphylococcus epidermidis growth by over 90%. These results highlight the macrocapsules dual role as a chemotherapeutic and antimicrobial agent, offering a promising strategy for treating lung cancer in patients at risk of infectious diseases or who are immunosuppressed.

The prognostic impact of non-obstructive coronary artery disease (CAD) remains controversial. Therefore, the objective of this study is to assess the long-term prognostic significance of non-obstructive CAD using machine learning models. We designed a multicenter retrospective, longitudinal, and observational study that included 3265 patients classified into three groups: 1426 patients with lesions < 20%, 643 patients with non-obstructive CAD (lesions 20–50%), and 1196 patients with obstructive CAD (lesions > 70%). A composite cardiovascular event (acute myocardial infarction, stroke, hospitalization due to heart failure, or cardiovascular-related death) was assessed after a mean follow-up of 43 months. To achieve this, various machine learning models were constructed. The model with the highest accuracy was selected to perform a Shapley Additive Explanations (SHAP) analysis, revealing the contribution of different variables in predicting an event. The SHAP analysis suggested that the percentage of coronary lesion was the most significant predictor of cardiovascular events. None of the models demonstrated adequate capability in predicting the event, showing only a good predictive ability for the absence of an endpoint. In conclusions, this study demonstrates how machine learning techniques may facilitate the development of high-performing models for predicting long-term cardiovascular events in patients undergoing coronary angiography.

Copper selenide nanoparticles (Cu2-x Se NPs) have received a lot of attention in recent decades due to their interesting properties and potential applications in various areas such as electronics, health, solar cells, etc. In this study, details of the synthesis and characterization of copper selenide nanoparticles modified with gum arabic (GA) are reported. Also, through transmission electronic microscopy (TEM) analysis, the transformation of the morphology and particle size of copper selenide nanoparticles in aqueous solution was studied. In addition, we present an antimicrobial study with different microorganisms such as Staphylococcus aureus (S. aureus), Escherichia coli (E. coli) and Candida albiacans (C. albicans). Copper selenide nanoparticles were characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry analysis (DSC) and TEM. XRD confirmed the crystal-line structure of the nanoparticles such as cubic berzelanite with a particle size of 6 nm ± 0.5. FTIR and TGA corroborated the surface modification of copper selenide nanoparticles with gum arabic, and DSC suggested a change in the structural phase from cubic to hexagonal. TEM analysis demonstrated that the surface modification of the Cu2-x Se NPs stabilized the nanostructure of the particles, preventing changes in the morphology and particle size. The antimicrobial susceptibility analysis of copper selenide nanoparticles indicated that they have the ability to inhibit the microbial growth of Staphylococcus aureus, Escherichia coli and Candida albicans.

The addition of ground olive stones in the manufacture of mortar bricks reduces their thermal conductivity coefficient and increases heat exchange insulation. So, it can improve energy efficiency and reduce the energy demand of buildings. This research analyzes, from other points of view, the economic, social and environmental impacts of the doping of GERO mortar bricks with ground olive stone in a percentage between 5% and 15% by volume of fine aggregate for use in building construction. The study focuses mainly on the region of Andalusia, Spain, where large quantities of olive stones are collected as waste from olive oil production. The economic analysis shows how the use of ground olive stones as a partial replacement of fine aggregate in mortar can generate significant savings and economic returns in the medium term (10–20 years) by reducing conventional energy consumption in heating and air conditioning installations. Finally, the Environmental Life Cycle Analysis shows that the use of ground olive stones in GERO perforated bricks reduces environmental impacts in the medium term. In summary, the research concludes that the doping of mortar bricks with ground olive stones can have a positive impact on the economy, society and the environment, and represents a sustainable and cost-effective alternative for the construction industry.

Abstract Lithium manganese‐rich layered oxides are promising cathode materials for lithium‐ion batteries due to their high discharge capacity, but they suffer from capacity fading and poor rate performance. Herein we report on the physicochemical and electrochemical properties of Li1.15Mn0.7Ni0.2Co0.1O2 synthesized via a sol gel method, and study the effect of copper doping on the resulting materials. Electrochemical testing at 1 C revealed 1 % Cu doping enhanced capacity retention (from 58 % to 78 %) but also decreased capacity, while in contrast 10 % Cu doping increased capacity by 21 %. Thorough characterization (including x‐ray diffraction, scanning electron microscopy, inductively coupled plasma‐optical emission spectroscopy, and electrochemical analyses such as voltage vs. capacity and dQ/dV plots) was employed to understand these results, which may be attributed to the competition of two different phenomena depending on the copper content. Consequently, this work highlights the importance of targeting bespoke stoichiometries depending on the desired electrochemical characteristics – an important point for the future design of materials for fast charging and high power applications.

We aimed to investigate the acute effects of blood flow restriction (BFR) combined with neuromuscular electrical stimulation (NMES) on muscle strength, thigh circumference, and knee joint reposition sense in female amateur football players, as well as to determine whether this procedure is safe. Methods: This was a pilot trial. Twenty female amateur football players were randomized into two groups: group 1 (n = 10) received a single NMES session; group 2 (n = 10) received NMES + BFR. The measured variables included maximal voluntary eccentric contraction (MVEC) and maximal voluntary concentric contraction (MVCC), thigh circumference, and knee joint reposition sense test. The type of electrical current used was TENS (symmetrical biphasic rectangular pulse, 350 μs, and 50 Hz), combined simultaneously with active knee extension (75 repetitions in 4 sets, 20% MVCC, 30 s rest between sets), for both groups. Group 2 had BFR added (80% of arterial occlusion pressure). Results: Statistically significant differences (p ≤ 0.05) were obtained for thigh circumference in both groups. The comparison between groups did not show statistically significant differences (p ≤ 0.05) in MVEC, MVCC, thigh circumference, or the knee joint reposition sense test. Conclusions: Both the isolated NMES intervention and its combination with BFR induced immediate changes in thigh circumference without impairing the muscle strength or proprioceptive ability of the football players. However, these results should be interpreted with caution, and future studies including a control group and isolated BFR application are needed.

In the modern era, air pollution is one of the most harmful environmental issues on the local, regional, and global stages. Its negative impacts go far beyond ecosystems and the economy, harming human health and environmental sustainability. Given these facts, efficient and accurate modeling and forecasting for the concentration of ozone are vital. Thus, this study explores an in-depth analysis of forecasting the concentration of ozone by comparing many hybrid combinations of time series models. To this end, in the first phase, the hourly ozone time series is decomposed into three new sub-series, including the long-term trend, the seasonal trend, and the stochastic series, by applying the seasonal trend decomposition method. In the second phase, we forecast every sub-series with three popular time series models and all their combinations In the final phase, the results of each sub-series forecast are combined to achieve the results of the final forecast. The proposed hybrid time series forecasting models were applied to four Metropolitan Lima monitoring stations—ATE, Campo de Marte, San Borja, and Santa Anita—for the years 2017, 2018, and 2019 in the winter season. Thus, the combinations of the considered time series models generated 27 combinations for each sampling station. They demonstrated significant forecasts of the sample based on highly accurate and efficient descriptive, statistical, and graphic analysis tests, as a lower mean error occurred in the optimized forecast models compared to baseline models. The most effective hybrid models for the ATE, Campo de Marte, San Borja, and Santa Anita stations were identified based on their superior out-of-sample forecast results, as measured by RMSE (4.611, 3.637, 1.495, and 1.969), RMSPE (4.464, 11.846, 1.864, and 15.924), MAE (1.711, 2.356, 1.078, and 1.462), and MAPE (14.862, 20.441, 7.668, and 76.261) errors. These models significantly outperformed other models due to their lower error values. In addition, the best models are statistically significant (p < 0.05) and superior to the rest of the combination models. Furthermore, the final proposed models show significant performance with the least mean error, which is comparatively better than the considered baseline models. Finally, the authors also recommend using the proposed hybrid time series combination forecasting models to predict ozone concentrations in other districts of Lima and other parts of Peru.

Efficiently modeling thin features using the finite-difference time-domain (FDTD) method involves a considerable reduction in the spatial mesh size. However, in real-world scenarios, such reductions can lead to unaffordable memory and CPU requirements. In this manuscript, we present two stable and efficient techniques in FDTD to handle narrow apertures on conductive thin panels. One technique employs conformal methods, while the other utilizes subgridding methods. We validate their performance compared to the classical Gilbert-Holland model and present experimental results in reverberation environments to shed light on these models’ actual confidence margins in real electromagnetic compatibility (EMC) scenarios.

In this paper, a mask region-based convolutional neural network (Mask R-CNN) is used to improve the performance of machine vision in the challenging task of detecting peduncles and fruits of green sweet peppers (Capsicum annuum L.) in greenhouses. One of the most complicated stages of the sweet pepper harvesting process is to achieve a precise cut of the peduncle or stem because this type of specialty crop cannot be grabbed and pulled by the fruit since the integrity and value of the product are compromised. Therefore, accurate peduncle detection becomes vital for the autonomous harvesting of sweet peppers. ResNet-101 combined with the feature pyramid network (FPN) architecture (ResNet-101 + FPN) is adopted as the backbone network for feature extraction and object representation enhancement at multiple scales. Mask images of fruits and peduncles are generated, focused on green sweet pepper, which is the most complex color variety due to its resemblance to the background. In addition to bounding boxes, Mask R-CNN provides binary masks as a result of instance segmentation, which would help improve the localization process in 3D space, the next phase of the autonomous harvesting process of sweet peppers, since it isolates the pixels belonging to the object and demarcates its boundaries. The prediction results of 1148 fruits on 100 test images showed a precision rate of 84.53%. The prediction results of 265 peduncles showed a precision rate of 71.78%. The mean average precision rate with an intersection over union at 50 percent (mAP@IoU=50) for model-wide instance segmentation was 72.64%. The average detection time for sweet pepper fruit and peduncle using high-resolution images was 1.18 s. The experimental results show that the proposed implementation manages to segment the peduncle and fruit of the green sweet pepper in real-time in an unmodified production environment under occlusion, overlap, and light variation conditions with effectiveness not previously reported for simultaneous 2D detection models of peduncles and fruits of green sweet pepper.

The aim of this research was to evaluate the redox system of Nocardia corallina B-276 in the biotransformation of 1-phenyl-1-propanone (1a), 2-hydroxy-1-phenylethanone (2a) and methyl (2-chlorophenyl)(oxo)acetate (3a) into 1-phenylpropan-1-ol (1b), 1-phenyl-1,2-ethanediol (2b) and methyl (2-chlorophenyl)(hydroxy)acetate (3b). The biomass of N. corallina was obtained in a liquid medium with an initial pH of 8.50, but the pH changed during the 96 h of the culture media, the final pH was between 4.74 and 7.62. The N. corallina biomass biocatalyzed the enantioselective reduction of 1a–3a to the corresponding alcohols. Whereas, during the process of oxidation of the rac-alcohols 1b–3b, 1b was oxidized in enantioselective way, the oxidation of 2b was not selective, but 3b was biotransformed mainly to (R)-3b. These results are indicative that N. corallina produced reductases and oxidases, whereby the biocatalytic activity was influenced by the final pH of the culture media, the reaction time and structure of the substrate.

Magnetic resonance imaging (MRI) is a valuable diagnostic tool that provides detailed information about the structure and function of tissues in the human body. In particular, measuring relaxation times, such as T1 and T2, can provide important insights into the composition and properties of different tissues. Accurate relaxation time mapping is therefore critical for clinical diagnosis and treatment planning, as it can help to identify and characterize pathological conditions, monitor disease progression, and guide interventions. However, the computation of relaxation time maps in MRI is a complex and challenging task that requires sophisticated mathematical algorithms. Thus, there is a need for robust and accurate algorithms that can reliably extract the desired information from MRI data. This article compares the performance of the Reduced Dimension Nonlinear Least Squares (RD-NLS) algorithm versus several widely used algorithms to compute relaxation times in MRI, such as Levenberg-Marquardt and Nelder-Mead. RD-NLS simplifies the search space for the optimum fit by leveraging the partial linear relationship between signal intensity and model parameters. The comparison was performed on several datasets and signal models, resulting in T1 and T2 maps. The algorithms were evaluated based on their fit error, with the RD-NLS algorithm showing a lower error than other fit-ting algorithms. The improvement was particularly notable in T1 maps, with less of a difference in T2 maps. Additionally, the average T1 values computed with different algorithms differed by up to 14 ms, indicating the importance of algorithm selection. These results suggest that the RD-NLS algorithm outperforms other commonly used algorithms for computing relaxation times in MRI.

Auto-labeling is one of the main challenges in 3D vehicle detection. Auto-labeled datasets can be used to identify objects in LiDAR data, which is a challenging task due to the large size of the dataset. In this work, we propose a novel methodology to generate new 3D based auto-labeling datasets with a different point of view setup than the one used in most recognized datasets (KITTI, WAYMO, etc.). The performance of the methodology has been further demonstrated with the development of our own dataset with the auto-generated labels and tested under boundary conditions on a bridge in a fixed position. The proposed methodology is based on the YOLO model trained with the KITTI dataset. From a camera-LiDAR sensor fusion, it is intended to auto-label new datasets while maintaining the consistency of the ground truth. The performance of the model, with respect to the manually labeled KITTI images, achieves an F-Score of 0.957, 0.927 and 0.740 in the easy, moderate and hard images of the dataset. The main contribution of this work is a novel methodology to auto-label autonomous driving datasets using YOLO as the main labeling system. The proposed methodology is tested under boundary conditions and the results show that this approximation can be easily adapted to a wide variety of problems when labeled datasets are not available.

The number of chronic kidney disease (CKD) persons continues to rise in Mexico. They require renal replacement therapy, and in the absence of it, hemodialysis is the major option for their survival. The uremic toxins present in the blood are removed by hemodialysis, which involve membranes. In this study, nonwoven fabrics with modified carbon black nanoparticles in a matrix polymer of Nylon 6 were obtained and evaluated as an adsorbent material of uremic toxins. All nonwoven fabrics were characterized by FTIR, XRD, TGA, SEM, and contact angle measurements and were evaluated as an adsorbent material for the urea toxin and as an albumin retainer. The findings suggest their potential application as a hemodialysis membrane. Nanocomposites had a higher hydrophilic characteristic compared to pure Nylon 6. The average diameter size of the fibers was in the range of 5 to 50 μm. All nanocomposites nonwoven fabrics showed high removal percentages of inulin in a range of 80–85% at 15 min of contact. Most Ny6 Zytel/CB nanocomposites showed a high percentage of urea removal (80 to 90%).

Access to fresh water is an increasing concern worldwide. The contamination of this vital liquid is largely caused by discharges of pollutants into rivers and seas from different types of industries. Waste dyes from different industries have been classified as harmful to health. In this study, polymeric nanomaterials based on polylactic acid (PLA) and nanoparticles of titanium dioxide (TiO2) and zinc oxide (ZnO) modified by ultrasound-assisted extrusion were obtained. These materials were evaluated by FTIR, DRX, TGA, DSC, SEM and methylene blue adsorption. From the results of the physicochemical characterizations, it was possible to observe the presence of TiO2 and ZnO nanoparticles dispersed in the polymeric matrix, increasing the crystallinity and thermal stability of the polymer. In addition, a good dispersion of the nanoparticles could be seen by means of SEM, due to the extrusion assisted by ultrasound. The methylene blue dye adsorption tests revealed that the best result was 98% dye adsorption in a time of 13 min for the 1.5% PZT sample. Additionally, this material could be used for 3 adsorption cycles without affecting its adsorbent properties.

The volcanic ash from the Andes mountain range (Puyehue-Cordon Caulle volcanic complex situated in western South America on the Argentinean-Chilean border) was used as heterogeneous acid catalyst in the suitable synthesis of 3H-1,5-benzodiazepines. The natural ashes were classified according to their particle size to generate the different catalytic materials. The catalysts were characterized by powder X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS), vibrational spectroscopies (FT-IR and Raman), and textural properties were determined by N2 adsorption (SBET). Potentiometric titration with n-butylamine was used to determine the acidic properties of the catalytic materials. Several 3H-1,5-benzodiazepines were obtained by reaction of o-phenylenediamine and substituted 1,3-diphenyl-1,3-propanedione in solvent-free conditions, giving good to excellent yields of a variety benzodiazepines. The method was carried out in environmentally friendly conditions and it was operationally simple. The volcanic ash resulted in a safe and recyclable catalyst.

Autonomous mobile robots are an important focus of current research due to the advantages they bring to the industry, such as performing dangerous tasks with greater precision than humans. An autonomous mobile robot must be able to generate a collision-free trajectory while avoiding static and dynamic obstacles from the specified start location to the target location. Machine learning, a sub-field of artificial intelligence, is applied to create a Long Short-Term Memory (LSTM) neural network that is implemented and executed to allow a mobile robot to find the trajectory between two points and navigate while avoiding a dynamic obstacle. The input of the network is the distance between the mobile robot and the obstacles thrown by the LiDAR sensor, the desired target location, and the mobile robot’s location with respect to the odometry reference frame. Using the model to learn the mapping between input and output in the sample data, the linear and angular velocity of the mobile robot are obtained. The mobile robot and its dynamic environment are simulated in Gazebo, which is an open-source 3D robotics simulator. Gazebo can be synchronized with ROS (Robot Operating System). The computational experiments show that the network model can plan a safe navigation path in a dynamic environment. The best test accuracy obtained was 99.24%, where the model can generalize other trajectories for which it was not specifically trained within a 15 cm radius of a trained destination position.

Essential oils from different plant species were found to contain different compounds exhibiting anti-inflammatory effects with the potential to be a valid alternative to conventional chemotherapy that is limited in long-term use due to its serious side effects. Generally, the first mechanism by which an organism counteracts injurious stimuli is inflammation, which is considered a part of the innate immune system. Periodontitis is an infectious and inflammatory disease caused by a dysbiosis in the subgingival microbiome that triggers an exacerbated immune response of the host. The immune–inflammatory component leads to the destruction of gingival and alveolar bone tissue. The main anti-inflammation strategies negatively modulate the inflammatory pathways and the involvement of inflammatory mediators by interfering with the gene’s expression or on the activity of some enzymes and so affecting the release of proinflammatory cytokines. These effects are a possible target from an effective and safe approach, suing plant-derived anti-inflammatory agents. The aim of the present review is to summarize the current evidence about the effects of essentials oils from derived from plants of the Lamiaceae family as complementary agents for the treatment of subjects with periodontitis and their possible effect on the cardiovascular risk of these patients.

Design of functional materials it is of great importance to address important problems in the areas of health and environment. In the present work, the synthesis and application of poly-meric nanocomposite materials with poly (lactic acid) (PLA) and modified nanoclay (cloisite 20A) with 1,4-diaminobutane dihydrochloride at different reaction times were studied. The concentra-tions of the nanoclays in the PLA matrix were 0.5, 1 and, 5% by weight (wt%). TGA showed that sample C20AM 120 (120 min of treatment) obtained the highest degree of modification considering the weight losses of the analyzed samples. An FT-IR signal at 1443 cm−1 suggests that the organic modifier is intercalated between the galleries of the clay. XRD, SEM and XPS suggest good disper-sion at low concentrations of the nanoclay. Adsorption tests revealed that the highest percentage of removal of uremic toxins and methylene blue was the sample with 5% wt/wt chemically modified nanoclay, suggesting good affinity between the modified nanoclays in the PLA matrix with the nitrogenous compounds.

Several strategies to deal with the trajectory tracking problem of Unmanned Underwater Vehicles are encountered, from traditional controllers such as Proportional Integral Derivative (PID) or Lyapunov-based, to backstepping, sliding mode, and neural network approaches. However, most of them are model-based controllers where it is imperative to have an accurate knowledge of the vehicle hydrodynamic parameters. Despite some sliding mode and neural network-based controllers are reported as model-free, just a few of them consider a solution with finite-time convergence, which brings strong robustness and fast convergence compared with asymptotic or exponential solutions and it can also help to reduce the power consumption of the vehicle thrusters. This work aims to implement a model-free high-order sliding-mode controller and synthesize it with a time-base generator to achieve finite-time convergence. The time-base was included by parametrizing the control gain at the sliding surface. Numerical simulations validated the finite-time convergence of the controller for different time-bases even in the presence of high ocean currents. The performance of the obtained solution was also evaluated by the Root Mean Square (RMS) value of the control coefficients computed for the thrusters, as a parameter to measure the power consumption of the vehicle when following a trajectory. Computational results showed a reduction of up to 50% in the power consumption from the thrusters when compared with other solutions.

Describes an experiment which illustrates the general aspects of intermolecular energy transfer between triplet states in rigid solutions of organic compounds solved in an ethanol-ether mixture. Measurements of quenching and energy transfer processes are made using the chemicals of benzophenone and naphthalene. (CS)

The welding procedure for reinforcing bars used in reinforced concrete takes place at fixed industrial installations. The welders must ensure that the reinforcing bar positions are maintained throughout all stages leading up to concreting. Fusion welding offers an alternative procedure that entails fewer risks for the workers’ health. In this study, we aimed to determine the minimum parameters for the intensity, pressure, and time necessary to ensure the level of performance required by this assembly welding. A total of 2160 joints were manufactured, comprising the different thicknesses. For each thickness measurement, 45 different combinations of parameters were applied. The joints were evaluated based on the resulting dimensions and on the failure force necessary to break the joint. For different thicknesses, the breaking loads were over 7350 N. In addition, the welding cycle times did not exceed three seconds. Based on the results obtained in this study, we concluded that assembling rebar (manufacturing reinforcing bars) by electrical resistance welding is a viable option. This method is fast, safe, and clean, and reduces the risks to the workers’ health.

The aim is to evaluate whether apigenin K and melatonin M5250 were able to stimulate bone formation after tooth extraction at one, two, and three months follow-up. Six male beagle dogs were used. Apigenin K and melatonin M5250 immersed in hemostatic collagen sponges were placed in the third and fourth premolar and the first molar extracted sockets; the second premolar was used as control. At one, two, and three months, bone core biopsies were performed, and picrosirius–hematoxylin was used for the staining process. In the first month, a higher amount of calcified bone tissue was observed in the melatonin (77.87% ± 1.2%) and apigenin K (69.81% ± 1.8%) groups than the control group (57.27% ± 0.54%), with apparent discrepancies in values between the three groups (p < 0.04). In the second month, there was a considerable improvement in the results in the areas with melatonin (79.81% ± 0.11%) than in those of apigenin K (71.65% ± 0.52%) and control (64.77% ± 0.44%) (p < 0.04). In the third month, the number of mature bone was similar to all the groups. The creation of new bone was significant in the melatonin group (82.78% ± 0.87%), followed by the apigenin K group (78.76% ± 0.43%) and the control group (57.27% ± 0.11%). From this experimental study in dogs, it can be concluded that melatonin and apigenin K can accelerate the process of mineralization of the bone matrix, and thus the creation of laminae in the early stages of healing (1 month). Less reabsorption of the post-extraction sockets can be expected with the topical application of melatonin and apigenin K. It seems that the stimulatory effects of bone healing induced by the topical application of melatonin and apigenin K are defect-size-dependent, being more evident in small defects compared to larger defects.

Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater environments, communication, localization, and navigation of AUVs are considered challenges due to the impossibility of relying on radio communications and global positioning systems. For a long time, acoustic systems have been the main approach for solving these challenges. However, they present their own shortcomings, which are more relevant for AUV teams. As a result, researchers have explored different alternatives. To summarize and analyze these alternatives, a review of the literature is presented in this paper. Finally, a summary of collaborative AUV teams and missions is also included, with the aim of analyzing their applicability, advantages, and limitations.

Iron oxide nanoparticles have long been studied as a T2 contrast agent in MRI due to their superparamagnetic behavior. T1-based positive contrast, being much more favorable for clinical application due to brighter and more accurate signaling is, however, still limited to gadolinium- or manganese-based imaging tools. Though being the only available commercial positive-contrast agents, they lack an efficient argument when it comes to biological toxicity and their circulatory half-life in blood. The need arises to design a biocompatible contrast agent with a scope for easy surface functionalization for long circulation in blood and/or targeted imaging. We hereby propose an extremely fast microwave synthesis for fluorescein-labeled extremely-small iron oxide nanoparticles (fdIONP), in a single step, as a viable tool for cell labeling and T1-MRI. We demonstrate the capabilities of such an approach through high-quality magnetic resonance angiographic images of mice.

Los años 2016 y 2017 han permitido consolidar la posición y la importancia de la olimpiada peruana de química a nivel nacional como una actividad que fomenta el estudio de la química y el esfuerzo de superación. Si bien 2016 se saldó con buenos resultados a nivel internacional, los éxitos conseguidos en 2017 han opacado todo lo ya conocido: una medalla de oro y una de bronce en la IChO 2017 y una de oro y tres de plata en la OIAQ lo dicen todo. Han sido los años de la consagración de un sueño a nivel internacional: la obtención de una medalla de oro. A nivel nacional, la Olimpiada Peruana de Química (OPQ) ha mantenido el alto nivel de inscritos y 2017 ha vuelto a superar los dos mil participantes.

The production of magnetic nanoparticles of utmost quality for biomedical imaging requires several steps, from the synthesis of highly crystalline magnetic cores to the attachment of the different molecules on the surface. This last step probably plays the key role in the production of clinically useful nanomaterials. The attachment of the different biomolecules should be performed in a defined and controlled fashion, avoiding the random adsorption of the components that could lead to undesirable byproducts and ill-characterized surface composition. In this work, we review the process of creating new magnetic nanomaterials for imaging, particularly for the detection of atherosclerotic plaque, in vivo. Our focus will be in the different biofunctionalization techniques that we and several other groups have recently developed. Magnetic nanomaterial functionalization should be performed by chemoselective techniques. This approach will facilitate the application of these nanomaterials in the clinic, not as an exception, but as any other pharmacological compound.

Chronic kidney disease (CKD) is a worldwide public health problem. In stages III and IV of CKD, uremic toxins must be removed from the patient by absorption, through a treatment commonly called hemodialysis. Aiming to improve the absorption of uremic toxins, we have studied its absorption in chemically modified graphene nanoplatelets (GNPs). This study involved the reaction between GNPs and diamines with reaction times of 30, 45 and 60 min using ultrasound waves of different amplitudes and frequencies. Functionalized GNPs were analyzed by Fourier Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), Scanning electron microscopy and energy dispersitive spectroscopy (SEM-EDS), and Thermogravimetric analysis (TGA). The analysis of the functional groups confirmed the presence of amide and hydroxyl groups on the surface of the GNPs by reactions of diamines with carboxylic acids and epoxides. Adsorption of uremic toxins was determined using equilibrium isotherms, where the maximum percentage of removal of uremic toxins was 97%. Dispersion of modified graphene nanoplatelets was evaluated in water, ethanol and hexane, as a result of this treatment was achieved a good and effective dispersion of diamines-modified graphene nanoplatelets in ethanol and hexane. Finally, the results of hemolysis assays of the modified graphene with amine demonstrated that it was not cytotoxic when using 500 mg/mL. The samples of modified graphene demonstrated low degree of hemolysis (

Magnesium batteries are a promising technology for a new generation of energy storage for portable devices. Attention should be paid to electrolyte and electrode material development in order to develop rechargeable Mg batteries. In this study, we report the use of the spinel lithium titanate or Li4Ti5O12 (LTO) as an active electrode for Mg2+-ion batteries. The theoretical capacity of LTO is 175 mA h g−1, which is equivalent to an insertion reaction with 1.5 Mg2+ ions. The ability to enhance the specific capacity of LTO is of practical importance. We have observed that it is possible to increase the capacity up to 290 mA h g−1 in first discharge, which corresponds to the reaction with 2.5 Mg2+ ions. The addition of MgCl2·6H2O to the electrolyte solutions significantly improves their electrochemical performance and enables reversible Mg deposition. Ex-situ X-ray diffraction (XRD) patterns reveal little structural changes, while X-ray photoelectron spectrometer (XPS) (XPS) measurements suggest Mg reacts with LTO. The Ti3+/Ti4+ ratio increases with the amount of inserted magnesium. The impedance spectra show the presence of a semicircle at medium-low frequencies, ascribable to Mg2+ ion diffusion between the surface film and LTO. Further experimental improvements with exhaustive control of electrodes and electrolytes are necessary to develop the Mg battery with practical application.