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It is crucial nowadays to predict in a fast and simple manner physical-chemical behaviors like, the size-dependent optical properties of gold nanospheres (Au NSs). The idea behind this experiment is trying to replace (as much as possible) robust and expensive microscopy techniques with UV-vis spectrophotometry and friendly simulations. Students chemically synthesized [approximately]4 and [approximately]15 nm diameter NSs and grew larger ones to [approximately]32, [approximately]50, and [approximately]70 nm diameter using the previously obtained [approximately]15 nm as seeds. They characterized them via UV-vis spectroscopy to ultimately compare the spectra with user-friendly software including MiePlot, nanoHUB.org, and the Amendola algorithm (Wolfram Mathematica). For Au NSs [less than or equal to]25 nm diam., the experimental UV-vis spectra fitted very well with the Amendola algorithm to determine the size. For Au NSs >25 nm diam., the experiment exhibited larger Au NSs than expected as noticed by the systematic red shift of the plasmon band with respect to the simulated one. A SEM image confirmed larger Au NSs; therefore, students were challenged to determine the source of discrepancy between experimental and simulated UV-vis spectra, which was later attributed to the aging of the Au NSs seeds. This experiment demonstrates an easy way to accurately determine the nanoparticle size while exploring and solving potential issues that students may face during the chemical synthesis and simulations.

A computational laboratory experiment is carried out to investigate the size-, geometry-, and chemistry-dependent properties of small molecules known as polycyclic aromatic hydrocarbons (PAHs) at the Facultad de Ingeniería (Universidad Nacional de La Plata, UNLP), Buenos Aires, Argentina. This computational research was adapted for upper-division undergraduate and initial graduate education levels. Due to the adverse circumstances of the pandemic, students were challenged to perform theoretical calculations on a regular computer at home. They were able to model PAH molecules similar to graphene quantum dots (GQDs) and learn how to use various open and freely available softwares, including visual molecular dynamics (VMD), Avogadro, nanoHUB.org, and ORCA. Through these computational tools, students designed PAHs of various sizes, geometries, and functional groups in order to study some of their optoelectronic properties. They simulated UV-vis absorbance spectra based on changes in size, geometry, and chemistry at the edges of the GQDs. Students then calculated the energy of the HOMO-LUMO gap and compared using three different methods included in ORCA corresponding to Hartree-Fock (H-F) approximation, density functional theory (DFT), and time-dependent DFT (TD-DFT). Finally, based on the results obtained, students propose the construction of more efficient solar devices by tuning the size and geometry of GQDs.

The goal of the present laboratory experiment is to deepen the understanding of the Nernst equation and some other concepts that are essential in electrochemistry. In this practical laboratory session, students first learn that the equilibrium potential of an electrode is related to the difference between two equilibrium inner electric potentials (also called Galvani potentials), namely, phi[subscript M] (inner electric potential of the metallic phase) and phi[subscript sol] (inner electric potential of the solution phase). Second, the concept of overvoltage is defined and the method to measure it is described. Finally, it is shown how and why the inner potential of a solution changes with the distance to the working electrode when a current flows through the solution and how the potential difference is distributed in an electrochemical cell when a current is flowing through it. (Contains 2 figures.)

During the last three decades, numerous investigations have been conducted on polyoxidovanadates to treat several illnesses and inhibit enzymes. Numerous decavanadate compounds have been proposed as potential therapies for Diabetes mellitus, Cancer, and Alzheimer’s disease. Only six relevant functional proteins interacting with decavanadate, V10, have been deposited in the PDB. These are acid phosphatase, tyrosine kinase, two ecto-nucleoside triphosphate diphosphohydrolases (NTPDases), the human transient receptor potential cation channel (TRPM4), and the human cell cycle protein CksHs1. The interaction sites in these proteins mainly consist of Arginine and Lysine, side chains binding to the decavanadate anion. To get further knowledge regarding non-covalent interactions of decavanadate in protein environments, guanidinium and spermidinium decavanadates were synthesized, crystallized, and subjected to analysis utilizing various techniques, including FTIR, Raman, 51V-NMR, TGA, and X-ray diffraction. The DFT calculations were employed to calculate the interaction energy between the decavanadate anion and the organic counterions. Furthermore, the Quantum Theory of Atoms in Molecules (QTAIM) and Non-covalent Interaction-Reduced Density Gradient (NCI-RDG) analyses were conducted to understand the non-covalent interactions present in these adducts. Decavanadate can engage in electrostatic forces, van der Waals, and hydrogen bond interactions with guanidinium and spermidinium, as shown by their respective interaction energies. Both compounds were highly stabilized by strong hydrogen bond interactions N−H···O and weak non-covalent interactions C−H···O. In addition, the interactions between guanidinium and spermidinium cations and decavanadate anion form several stable rings. This study provides new information on non-covalent intermolecular interactions between decavanadate and small biomimetic models of arginine and lysine lateral chains in protein environments.

Soccer is a team sport in which players expend high-intensity intermittent efforts that require the production of sprints, accelerations, decelerations, changes of direction, and jumps. The aim of this study was to analyze the effects of training with portable and low-cost flywheel devices, using multi-directional exercises over 8 weeks, on the performance of linear sprint, curve sprint, and change of direction in senior soccer players. Thirty-three players participated in the research, divided into a control group and an experimental group. The latter group performed the training protocol in addition to the technical–tactical soccer training. Before and after the application of the training protocol, the linear and curve sprint and change of direction (V-cut) were evaluated. At the end of the training program, significant improvements (p < 0.05) were found in change of direction and in curve sprint with respect to the control group. The results suggested that resistance training with flywheel devices with low training volumes improves performance in change of direction and in curve sprint, which are key performance variables in soccer. Therefore, this type of training could be added to technical–tactical soccer training to enhance the variables that are essential to reach high-performance metrics in soccer.

This study evaluates the performance of biofuels created from triple blends of fossil diesel, sunflower or castor oil (SVOs), and 2-Ethylhexyl Nitrate (EHN), a low-viscosity, high-cetane (LVHC) solvent. EHN reduces the viscosity of SVOs to enable their use in conventional diesel engines without compromising fuel properties. The results show that the power output from these blends is similar to or greater than that of fossil diesel, with comparable fuel consumption. Furthermore, the blends significantly reduce emissions of carbon monoxide (CO) and soot, though NOx emissions are slightly higher due to the nitrogen content in EHN. However, NOx levels remain within permissible limits. The substitution of fossil diesel could be further enhanced if EHN were produced using green hydrogen and lignocellulosic biomass, making it a renewable and sustainable biofuel component. These findings support the potential of EHN/SVO biofuel blends to replace a significant portion of fossil diesel in conventional diesel engines while maintaining performance and reducing harmful emissions, except for a slight increase in NOx.

In the pursuit of operational excellence and enhanced competitiveness, a wide range of industries have turned to methodologies such as Lean and Six Sigma; however, in the chemical sector, their application is very limited. This paper presents a Lean Six Sigma framework to identify and reduce sources of variability occurring in the final product composition of a Spanish SME fertilizer manufacturer. The company faced important challenges related to product variability, adversely affecting overall productivity. A real-life case of the Lean Six Sigma implementation was conducted over two years, and its applicability and ability to improve productivity performance were thoroughly assessed. The proposed framework successfully integrated Lean and Six Sigma methodologies, i.e., process mapping (value stream mapping), root cause analysis (Ishikawa cause–effect diagram), project management (SIPOC and DMAIC), and statistical process control, and demonstrated practical benefits for the case company by identifying the key variables affecting product variability and determining their optimal levels. A substantial 50% reduction in the variability of several products and a 42% reduction in material preparation time were achieved. These reductions resulted in a 40% reduction in costs associated with product losses and a 54% reduction in costs from raw material losses.

This paper explores the application of Inverse Reinforcement Learning (IRL) in robotics, focusing on inferring reward functions from expert demonstrations of robot arm manipulation tasks. By leveraging IRL, we aim to develop efficient and adaptable techniques for learning robust solutions to complex tasks in continuous state spaces. Our approach combines Apprenticeship Learning via IRL with Proximal Policy Optimization (PPO), expert-trajectory-based features, and the application of a reverse discount. The feature space is constructed by sampling expert trajectories to capture essential task characteristics, enhancing learning efficiency and generalizability by concentrating on critical states. To prevent the vanishing of feature expectations in goal states, we introduce a reverse discounting application to prioritize feature expectations in final states. We validate our methodology through experiments in a simple GridWorld environment, demonstrating that reverse discounting enhances the alignment of the agent’s features with those of the expert. Additionally, we explore how the parameters of the proposed feature definition influence performance. Further experiments on robotic manipulation tasks using the TIAGo robot compare our approach with state-of-the-art methods, confirming its effectiveness and adaptability in complex continuous state spaces across diverse manipulation tasks.

There is growing international interest in assessing population exposure to radiofrequency electromagnetic fields, especially those generated by mobile-phone base stations. The work presented here is an experimental study in which we assess exposure to radiofrequency electromagnetic fields in a university environment, where there is a site with mobile-phone antennas and where a large number of people live on a daily basis. The data were collected with a personal exposure meter in two samplings, one walking at ground level and the other using an aerial vehicle at a height higher than the buildings. The geo-referenced electric-field data were subjected to a process in which a theoretical model was adjusted to the experimental variograms, and heat maps were obtained using kriging interpolation. The research carried out is of great relevance, since it provides detailed measurements of the electromagnetic radiation levels both at ground level and at significant heights, using innovative methodologies such as the use of drones. Furthermore, the results obtained allow for contextualizing the exposures in relation to international safety limits, highlighting the importance of rigorous monitoring in everyday environments.

The increasing integration of distributed generation (DG) and the rise of microgrids have reshaped the operation of distribution systems, introducing both challenges and opportunities for optimization. This study presents a methodology that combines network reconfiguration with the integration of buses with flexible power in order to improve the efficiency and cost-effectiveness of distribution networks. Flexible buses, which aggregate multiple microgrids or controllable distributed resources, function as control points that can dynamically adjust active and reactive power within predefined limits. This capability allows for more precise management of power flows, enabling the system to respond to fluctuations in generation and demand. The proposed optimization framework aims to minimize the total operational costs, including power losses and the use of flexible power, while adhering to system constraints. The methodology is evaluated through case studies on two distribution systems: the Kumamoto and IEEE-33 systems. The results indicate a 43.9% reduction in power losses for the Kumamoto system and a 66.6% reduction for the IEEE-33 system, along with notable cost savings in both cases. These outcomes demonstrate the potential benefits of incorporating flexible power buses in modern radial distribution networks, showing their role in adapting to various operational scenarios and supporting the integration of distributed generation and microgrids.

The existence of solvated electrons has been known for a long time. Key methods for their production (i.e., photoionization of reducing ions, water radiolysis, and the reaction between H[middle dot] and OH[superscript -]) are unsuitable for most school laboratories. We describe a simple experiment to produce liquid ammonia and solvated electrons using materials commonly available, without the dangers associated with the use of solid Na or K metal. (Contains 3 figures.)

This research designed and developed an ultrasonic reactor for a fast and on demand production of cold brew coffee, remarkably reducing the brewing time from 24 h to less than 3 min. The technology was engineered by utilizing resonance to induce ultrasonic waves around the walls of the brewing basket of an espresso machine. The sound transmission system comprised a transducer, a horn and a brewing basket. This arrangement transformed the coffee basket into an effective sonoreactor that injected sound waves at multiple points through its walls, thereby generating multiple regions for acoustic cavitation within the reactor. Furthermore, acoustic streaming induced greater mixing and enhanced mass transfer during brewing. The design was accomplished by modeling the transmission of sound, and acoustic cavitation. Brew characterization and chemical composition analysis was performed, considering factors such as pH, acidity, color, and the composition of caffeine, fatty acids, and volatiles. The efficiency of the extraction increased by decreasing the basket loading percentage (BLP). For instance, sonicating at 100 W doubled the extraction yield and caffeine concentration, from 15.05 % to 33.44 % at BLP = 33 %, and from 0.91 mg/mL to 1.84 mg/mL at BLP = 67 %, respectively. The total fatty acids increased from 1.16 mg/mL to 9.20 mg/mL, representing an eightfold increase, at BLP = 33 %. Finally, a sensory analysis was conducted to evaluate appearance, aroma, texture, flavor, and aftertaste, which demonstrated that coffee brewed for 1 and 3 min in the sonoreactor exhibited almost undistinguishable properties compared to a standard 24 h brewing without ultrasound.

Abstract The coronavirus disease 2019 (COVID-19) pandemic continues to represent a global public health issue. The viral main protease (Mpro) represents one of the most attractive targets for the development of antiviral drugs. Herein we report peptidyl nitroalkenes exhibiting enzyme inhibitory activity against Mpro (K i : 1–10 μM) good anti-SARS-CoV-2 infection activity in the low micromolar range (EC50: 1–12 μM) without significant toxicity. Additional kinetic studies of compounds FGA145, FGA146 and FGA147 show that all three compounds inhibit cathepsin L, denoting a possible multitarget effect of these compounds in the antiviral activity. Structural analysis shows the binding mode of FGA146 and FGA147 to the active site of the protein. Furthermore, our results illustrate that peptidyl nitroalkenes are effective covalent reversible inhibitors of the Mpro and cathepsin L, and that inhibitors FGA145, FGA146 and FGA147 prevent infection against SARS-CoV-2.

This study explores innovative methodologies for estimating the energy baseline (EnBL) of a university classroom building, emphasizing the critical roles of data quality and model selection in achieving accurate energy efficiency assessments. We compare time series models that are suitable for buildings with limited consumption data with univariate and multivariate regression models that incorporate additional variables, such as weather and occupancy. Furthermore, we investigate the advantages of dynamic simulation using the EnergyPlus engine (V5, USDOE United States) and Design Builder software v7, enabling scenario analysis for various operational conditions. Through a comprehensive case study at the UAO University Campus, we validate our models using daily monitoring data and statistical analysis in RStudio. Our findings reveal that model choice significantly influences energy consumption forecasts, leading to potential overestimations or underestimations of savings. By rigorously assessing statistical validation and error analysis results, we highlight the implications for decarbonization strategies in building design and operation. This research provides a valuable framework for selecting appropriate methodologies for energy baseline estimation, enhancing transparency and reliability in energy performance assessments. These contributions are particularly relevant for optimizing energy use and aligning with regulatory requirements in the pursuit of sustainable building practices.

This paper presents a novel parallel manipulator capable of generating two translations (2T), inside a vertical plane, and two rotations (2R), about horizontal axes, which are required in aerospace, manufacturing and rehabilitation fields. These four degrees of freedom are reached by means of a unique RRU and three RSS kinematic chains connected to a rhomboid-shaped mobile platform. The kinematic analysis of the new manipulator is provided, which includes the resolution of the inverse position problem and the velocity equations relating to input and output variables. Additionally, a methodology is proposed for obtaining the workspace free of singularities, collisions and kinematic joint range limitation. This systematic methodology allows designers to identify the critical factors affecting the workspace and, thus, to rearrange the mechanical design accordingly for optimum path planning. We represent the workspace using its two-dimensional subspaces (i.e., translational and rotational workspace). The results are analyzed for different working modes of the manipulator to see its potential use in applications wherein 2T2R motion is necessary.

Individuals with reduced mobility, including the growing elderly demographic and those with spinal cord injuries, often face significant challenges in daily activities, leading to a dependence on assistance. To enhance their independence, we propose a robotic system that facilitates greater autonomy. Our approach involves a functional assistive robotic implementation for picking, placing, and delivering containers using the TIAGo mobile manipulator robot. We developed software and routines for detecting containers marked with an ArUco code and manipulating them using the MoveIt library. Subsequently, the robot navigates to specific points of interest within a room to deliver the container to the user or another designated location. This assistance task is commanded through a user interface based on a web application that can be accessed from the personal phones of patients. The functionality of the system was validated through testing. Additionally, a series of user trials were conducted, yielding positive feedback on the performance and the demonstration. Insights gained from user feedback will be incorporated into future improvements to the system.

Abstract Li‐ion batteries are attracting an increasing attention due to the process of electrification involving different industrial sectors. Many efforts are dedicated to improving battery performance in terms of cyclability, capacity, fast charging and safety to name a few. Therefore, it is of primal importance to identify and understand the degradation modes that stay behind cell failure. In this sense, Loss of Lithium Inventory (LLI) and Loss of Active Material (LAM) are considered fundamental indicators for estimating the state of health of a cell. In this work, we introduce an automated open tool which is able to return LLI, LAM and voltage drop due to and eventual development of an internal resistance by fitting experimental pseudo‐OCV profiles of a cell, at its beginning and end of life, with a simulated curve obtained from the pseudo‐OCP curves of positive and negative electrodes. The tool is based on a mathematical model consisting in linear transformations that keeps it simple and guarantees for low computational cost. The model is validated against bibliographic dataset and tested on experimental curves from lab‐scale coin and commercial cylindrical cell with different chemistries.

Audio steganalysis has been little explored due to its complexity and randomness, which complicate the analysis. Audio files generate marks in the frequency domain; these marks are known as fingerprints and make the files unique. This allows us to differentiate between audio vectors. In this work, the use of the Goertzel algorithm as a steganalyzer in the frequency domain is combined with the proposed sliding window adaptation to allow the analyzed audio vectors to be compared, enabling the differences between the vectors to be identified. We then apply linear prediction to the vectors to detect any modifications in the acoustic signatures. The implemented Goertzel algorithm is computationally less complex than other proposed stegoanalyzers based on convolutional neural networks or other types of classifiers of lower complexity, such as support vector machines (SVD). These methods previously required an extensive audio database to train the network, and thus detect possible stegoaudio through the matches they find. Unlike the proposed Goertzel algorithm, which works individually with the audio vector in question, it locates the difference in tone and generates an alert for the possible stegoaudio. In this work, we apply the classic Goertzel algorithm to detect frequencies that have possibly been modified by insertions or alterations of the audio vectors. The final vectors are plotted to visualize the alteration zones. The obtained results are evaluated qualitatively and quantitatively. To perform a double check of the fingerprint of the audio vectors, we obtain a linear prediction error to establish the percentage of statistical dependence between the processed audio signals. To validate the proposed method, we evaluate the audio quality metrics (AQMs) of the obtained result. Finally, we implement the stegoanalyzer oriented to AQMs to corroborate the obtained results. From the results obtained for the performance of the proposed stegoanalyzer, we demonstrate that we have a success rate of 100%.

Gait and balance have a direct impact on patients’ independence and quality of life. Due to a higher life expectancy, the number of patients suffering neurological disorders has increased exponentially, with gait and balance impairments being the main side effects. In this context, the use of rehabilitation robotic devices arises as an effective and complementary tool to recover gait and balance functions. Among rehabilitation devices, end effectors present some advantages and have shown encouraging outcomes. The objective of this study is twofold: to propose a general classification of devices for gait and balance rehabilitation and to provide a review of the existing end effectors for such purposes. We classified the devices into five groups: treadmills, exoskeletons, patient-guided systems, perturbation platforms, and end effectors. Overall, 55 end effectors were identified in the literature, of which 16 were commercialized. We found a disproportionate number of end effectors capable of providing both types of rehabilitation (2/55) and those focused on either balance (21/55) or gait (32/55). The analysis of their features from a mechanical standpoint (degrees of freedom, topology, and training mode) allowed us to identify the potential of parallel manipulators as driving mechanisms of end effector devices and to suggest several future research directions.

Cigarette butts (CBs) are non-biodegradable harmful residues of synthetic origin and are widespread in marine environments around the world. Although environmental factors are often primarily responsible for the fragmentation of microplastics in the marine environment, biotic factors have recently been shown to be equally important in plastic debris. This study evaluates the role of the Hediste diversicolor polychaete in the fragmentation of CBs in the marine environment. Polychaetes were exposed to three concentrations of CB (0 (as the control), 0.25, and 1 butt L−1) at two different temperatures (15 °C and 23 °C) for 28 days. At each temperature, aquaria without polychaetes were used to study the effect of the burrowing activity of the polychaete on CB fragmentation. Toxicants analysed from exposed sediments increased their concentration in a dose-dependent manner to the CB concentration at a temperature of 15 °C but not at 23 °C. CBs did not directly decrease Hediste survival, but prolonged elevated temperatures increased the polychaetes’ susceptibility. The negative effects of CBs on burial success and burrowing behaviour could not be offset by the reduced start time caused by elevated temperatures. Regardless of temperature, both the weight loss and physical fragmentation of CBs buried in polychaete-contaminated sediments were significantly higher than those without Hediste, with no differences between the two concentrations tested. FTIR-ATR analysis used to evaluate CB degradation in relation to cellulose acetate decomposition showed a greater degradation of this compound in treatments with Hediste than in those without polychaetes (~2.75 times), but these differences were not significant. This study is a promising initial step for future research, as any factor that facilitates the fragmentation of this prevalent and hazardous waste must be carefully studied to extract the maximum benefit to help to reduce CBs in the marine environment.

Abstract Two-dimensional materials present exceptional crystal elasticity and provide an ideal platform to tune electrical and optical properties through the application of strain. Here we extend recent research on strain engineering in monolayer molybdenum disulfide using an adamantane plasma polymer pinning layer to achieve unprecedented crystal strains of 2.8%. Using micro-reflectance spectroscopy, we report maximum strain gauge factors of −99.5 meV/% and −63.5 meV/% for the A and B exciton of monolayer MoS2, respectively, with a 50 nm adamantane capping layer. These results are corroborated with photoluminescence and Raman measurements on the same samples. Taken together, our results indicate that adamantane polymer is an exceptional capping layer to transfer substrate-induced strain to a 2D layer and achieve higher levels of crystal strain.

The Bougainvillea glabra or bougainvillea is a climbing plant native from South America belonging to the Nyctaginaceae family. The bougainvillea is recognized worldwide for its horticultural importance, due to the color of its bracts, commonly known as “flowers,” made up of bracts, which are the striking parts, and the true flowers, which are white and small. Bougainvillea is widely known in traditional medicine to treat respiratory diseases such as cough, asthma, and bronchitis, gastrointestinal diseases, also for its antibacterial and insecticidal capacity. The antimicrobial potential of the involucre of this plant has not been studied, despite research showing a high phytochemical presence of secondary metabolites such as alkanes, phenols, terpenes, and betalains. This review compiles information about the traditional uses of B. glabra, its botanical description, ecological relevance, phytochemistry, antimicrobial and antibiofilm activity, such as the toxicology of bracts and flowers.

Soil organic carbon (SOC) is considered a significant contributor to soil water retention. However, generalizations about the role of SOC in available water-holding capacity (AWHC) may have inaccurately portrayed this relationship. We aim to reexamine the relationship between SOC and water retention using the National Cooperative Soil Survey (NCSS) Database. We focus on regional soil groups within the Pacific Northwest wheat production region, including Haploxerolls, Argixerolls, Haplocambids, and Durixerolls. We evaluated 77 sites based on SOC, total nitrogen (TN), pH, texture, bulk density (BD), field capacity (FC), permanent wilting point (PWP), and AWHC. Our findings indicate that texture and BD were the most significant contributors to AWHC variation, while SOC played a secondary role in explaining this variation. Mid-infrared (MIR) spectroscopy coupled with a random forest (RF) algorithm was used to evaluate the importance of spectral bands in determining changes in FC and PWP. This analysis identified mineral bands related to inner-surface hydroxyl groups in kaolinite (3700 cm −1) and Si-O-Si overtones (1870 cm −1) as the most important spectral contributors to PWP. The water retention at FC was associated with organic absorbances relevant to soil aggregation, such as polysaccharide C–O (~1035 cm −1), while mineral bands were relatively less influential. This study highlights the need to reexamine the impact of SOC as well as the interaction between soil texture and compaction on soil water retention to elucidate the underlying mechanisms responsible for AWHC, thus providing insight into future drought adaptation strategies.

In this report, we present the results on the physicochemical characterization of cadmium telluride quantum dots (QDs) stabilized with glutathione and prepared by optimizing the synthesis conditions. An excellent control of emissions and the composition of the nanocrystal surface for its potential application in monoclonal antibody and biomarker testing was achieved. Two samples (QDYellow, QDOrange, corresponding to their emission colors) were analyzed by dynamic light scattering (DLS), and their hydrodynamic sizes were 6.7 nm and 19.4 nm, respectively. Optical characterization by UV-vis absorbance spectroscopy showed excitonic peaks at 517 nm and 554 nm. Photoluminescence spectroscopy indicated that the samples have a maximum intensity emission at 570 and 606 nm, respectively, within the visible range from yellow to orange. Infrared spectroscopy showed vibrational modes corresponding to the functional groups OH-C-H, C-N, C=C, C-O, C-OH, and COOH, which allows for the formation of functionalized QDs for the manufacture of biomarkers. In addition, the hydrodynamic radius, zeta potential, and approximate molecular weight were determined by dynamic light scattering (DLS), electrophoretic light scattering (ELS), and static light scattering (SLS) techniques. Size dispersion and the structure of nanoparticles was obtained by Transmission Electron Microscopy (TEM) and by X-ray diffraction. In the same way, we calculated the concentration of Cd2+ ions expressed in mg/L by using the Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-OES). In addition to the characterization of the nanoparticles, the labeling of murine myeloid cells was carried out with both samples of quantum dots, where it was demonstrated that quantum dots can diffuse into these cells and connect mostly with the cell nucleus.

The aim of this study was to determine the associations between the front and back squat, countermovement jump (CMJ) and deep squat jump (DSJ) force–time metrics, and weightlifting performance in top-level weightlifters. Thirteen top-level weightlifters who classified for the World Championship 2023 participated. The heaviest successful snatch and clean and jerk were recorded within a preparation session as performance indicators. The front and back squat one-repetition maximums (1RMs) were evaluated in separate training sessions. The average of three maximum CMJs and DSJs were recorded using a force plate, and jump height, propulsive net impulse, and peak power were calculated for further analysis. Pearson’s correlation coefficients were used to determine the associations between variables. Statistical significance was set at p ≤ 0.05. The front and back squat 1RMs were significant and nearly perfectly associated with weightlifting performance (p < 0.001, r = 0.98–0.99). CMJ and DSJ propulsive net impulse displayed nearly perfect associations with weightlifting performance (p < 0.001, r = 0.96–0.99), while jump height is a less promising metric to assess the weightlifters’ ballistic capabilities. This study reinforces that lower body maximum strength and ballistic capabilities are closely associated with top-level weightlifters’ performance and are of practical importance to monitor their neuromuscular function.

Exposure to heavy metals has been documented to induce changes in the expression of plant proteins. Phaseolus vulgaris L., represents a great source of nutrition for millions of people, and is the second most important legume crop. The present study aimed to investigate the effects of Hg stress on germination rate and identify the gene expression profiling of U1-70 kDa by using SDS-PAGE analysis. Seeds of black beans (P. vulgaris L.) variety Jamapa were used. The study was performed in the municipality of Guadalupe in Zacatecas, México; August 2009 to 2011. Embryos were exposed to 10 µmol L-1 HgCl2. Expression and detection of U1-70 kDa was affected by mercury. It was possible to amplify the cDNA for U1-70 kDa in all tested samples, there were also found variations in the mRNA of embryos bean seeds for western blot analysis. Mercury does not affect the germination of bean seeds P. vulgaris L.; there is variation in the expression of the U1-70 kDa protein at different hours of exposure to 10 µmol L-1 of mercury. The presence of U1-70 kDa is identified for the first time in early stages of germination of bean seeds P. vulgaris L.

To establish classifications and to obtain pre- and post-operative information on patient-specific alignments, it is necessary to measure different angular values accurately and precisely, mainly on weight-bearing, full-length anteroposterior X-rays of the lower limbs (LLRs). This study evaluated angular measurements’ validity and absolute reliability on LLRs with a self-developed, computer-aided measurement system (TraumaMeter v.873). Eight independent observers measured the preoperative mechanical hip-knee-ankle (mHKA) angle of 52 lower extremities (26 cases) in a blinded fashion on three occasions separated by two weeks. We obtained an intra-observer mean bias error (MBE) of 0.40°, a standard deviation (SD) of 0.11°, and a 95% confidence interval (CI) of 0.37°–0.43°. We also obtained an inter-observer MBE of 0.49°, an SD of 0.15°, and a 95% C of 0.45°–0.53°. The intra-observer MBE for the measurement pair between the second and the first measurement round (T2T1) was 0.43°, the SD was 0.13°, and the 95% CI was 0.39°–0.47°; the MBE between the third and the second round (T3T2) was 0.37°, with an SD of 0.10° and a 95% CI of 0.34°–0.40°; and the MBE between the third and the first round (T3T1) was 0.40°, with an SD of 0.10° and a 95% CI of 0.37°–0.43°. The interobserver MBE for the first round of measurements was 0.52°, with an SD of 0.16° and a 95% CI of 0.48°–0.56°; the MBE for the second round was 0.50°, with an SD of 0.15° and a 95% CI of 0.46°–0.54°; and the MBE for the third round was 0.46°, with an SD of 0.14° and a 95% CI of 0.42°–0.50°. There were no statistically significant differences in the inter-observer errors for the three tests. In the case of the intra-observer analysis, there were differences between T2T1 and between T3T2, but these differences were minimal, with no overlaps in the lower or upper values, respectively, of the confidence intervals. These results led us to conclude that the TraumaMeter v.873 software extension for measuring lower-limb angles in LLRs is an accurate tool with low intra- and inter-observer variability.

With the increase in the use of Internet interconnected systems, security has become of utmost importance. One key element to guarantee an adequate level of security is being able to detect the threat as soon as possible, decreasing the risk of consequences derived from those actions. In this paper, a new metric for early detection system evaluation that takes into account the delay in detection is defined. Time aware F-score (TaF) takes into account the number of items or individual elements processed to determine if an element is an anomaly or if it is not relevant to be detected. These results are validated by means of a dual approach to cybersecurity, Operative System (OS) scan attack as part of systems and network security and the detection of depression in social media networks as part of the protection of users. Also, different approaches, oriented towards studying the impact of single item selection, are applied to final decisions. This study allows to establish that nitems selection method is usually the best option for early detection systems. TaF metric provides, as well, an adequate alternative for time sensitive detection evaluation.

Frequency-guided sequential demodulation (FSD) is a technique used to recover the phase of closed fringe patterns when only a single interferogram is available. FSD is a sequential method that follows simple and intuitive processing steps for interferogram demodulation, guided by the frequency of the fringes. The objective of this work is to apply an optimizer block that feeds from the first stages of the FSD and make feedback in such a manner as to optimize the search for guided frequencies. This approach significantly reduces demodulation errors, especially when dealing with high-frequency fringe patterns. The results of using the FSD with and without optimizers are compared. The optimizers used are the Harmonic Search Optimization (HSO) as well as the Particle Swarm Optimization (PSO). Results show that FSD with PSO delivers better results compared with other methods here analyzed.

Technical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many factors, such as the market in which they operate, the size of the time window, and so on. This paper focuses on the real-time optimization of the parameters applied for analyzing time series of data. In particular, we optimize the parameters of some technical financial indicators. We propose the combination of several Multiobjective Evolutionary Algorithms. Unlike other approaches, this paper applies a set of different Multiobjective Evolutionary Algorithms, collaborating to construct a global Pareto Set of solutions. Solutions for financial problems seek high returns with minimal risk. The optimization process is continuous and occurs at the same frequency as the investment time interval. This technique permits the application of the non-dominated solutions obtained with different MOEAs at the same time. Experimental results show that Collaborative Multiobjective Evolutionary Algorithms obtain up to 22% of profit and increase the returns of the commonly used Buy and Hold strategy and other multi-objective strategies, even for daily operations.

While there are references available in the literature regarding learning experiences with Dataset-Based Learning (DBL) approaches, there is a noticeable absence of a standardized model for designing DBL activities. This gap was identified in this work after performing a systematic literature review (SLR). In contrast to other active learning methodologies, the lack of a common framework for the DBL methodology makes it challenging to compare different DBL approaches. This paper highlights the knowledge gap in the methodology for designing DBL activities and aims to provide a common approach for sharing the view and details about what DBL entails in higher education and how to design a DBL activity. Additionally, we illustrate these concepts with three case studies in different engineering fields. Based on the SLR results and the review of additional literature, this work defines DBL as an active teaching methodology that focuses on using datasets to promote the learning and understanding of specific concepts and skills. These datasets should contain real data presented in different formats. As a common starting point, in a DBL lesson, the dataset not only provides information and context in the activity statement but also serves as the material to work with, and the solution to the activity is entirely extracted from the information contained in the dataset.

Glaucoma, a disease that damages the optic nerve, is the leading cause of irreversible blindness worldwide. The early detection of glaucoma is a challenge, which in recent years has driven the study and application of Deep Learning (DL) techniques in the automatic classification of eye fundus images. Among these intelligent systems, Convolutional Neural Networks (CNNs) stand out, although alternatives have recently appeared, such as Vision Transformers (ViTs) or hybrid systems, which are also highly efficient in image processing. The question that arises in the face of so many emerging methods is whether all these new techniques are really more efficient for the problem of glaucoma diagnosis than the CNNs that have been used so far. In this article, we present a comprehensive comparative study of all these DL models in glaucoma detection, with the aim of elucidating which strategies are significantly better. Our main conclusion is that there are no significant differences between the efficiency of both DL strategies for the medical diagnostic problem addressed.

Hydrogen sulfide (H2S) was recognized as a gaseous signaling molecule, similar to nitric oxide (-NO) and carbon monoxide (CO). The aim of this review is to provide an overview of the formation of hydrogen sulfide (H2S) in the human body. H2S is synthesized by enzymatic processes involving cysteine and several enzymes, including cystathionine-β-synthase (CBS), cystathionine-γ-lyase (CSE), cysteine aminotransferase (CAT), 3-mercaptopyruvate sulfurtransferase (3MST) and D-amino acid oxidase (DAO). The physiological and pathological effects of hydrogen sulfide (H2S) on various systems in the human body have led to extensive research efforts to develop appropriate methods to deliver H2S under conditions that mimic physiological settings and respond to various stimuli. These functions span a wide spectrum, ranging from effects on the endocrine system and cellular lifespan to protection of liver and kidney function. The exact physiological and hazardous thresholds of hydrogen sulfide (H2S) in the human body are currently not well understood and need to be researched in depth. This article provides an overview of the physiological significance of H2S in the human body. It highlights the various sources of H2S production in different situations and examines existing techniques for detecting this gas.

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic. For the virus to enter the host cell, its spike (S) protein binds to the ACE2 receptor, and the transmembrane protease serine 2 (TMPRSS2) cleaves the binding for the fusion. As part of the research on COVID-19 treatments, several Casiopeina-analogs presented here were looked at as TMPRSS2 inhibitors. Using the DFT and conceptual-DFT methods, it was found that the global reactivity indices of the optimized molecular structures of the inhibitors could be used to predict their pharmacological activity. In addition, molecular docking programs (AutoDock4, Molegro Virtual Docker, and GOLD) were used to find the best potential inhibitors by looking at how they interact with key amino acid residues (His296, Asp 345, and Ser441) in the catalytic triad. The results show that in many cases, at least one of the amino acids in the triad is involved in the interaction. In the best cases, Asp435 interacts with the terminal nitrogen atoms of the side chains in a similar way to inhibitors such as nafamostat, camostat, and gabexate. Since the copper compounds localize just above the catalytic triad, they could stop substrates from getting into it. The binding energies are in the range of other synthetic drugs already on the market. Because serine protease could be an excellent target to stop the virus from getting inside the cell, the analyzed complexes are an excellent place to start looking for new drugs to treat COVID-19.

Given the absence of effective and selective treatments for hepatocellular carcinoma (HCC), the antitumor effect of novel bionanocatalysts was evaluated in two different tissues (liver and lung) of a rat model with HCC using Diffuse Reflectance Spectroscopy. The spectra obtained showed a significant decrease in signal intensity in the presence of tumor tissues with respect to healthy tissue. An increase in signal intensity was observed in diseased rats treated with bionanocatalysts, suggesting their antitumor effect in terms of tumor tissue reduction with respect to the diseased control.

Climate change, socioeconomical pressures, and new policy and legislation are driving a decarbonization process across industries, with a critical shift from a fossil-based economy toward a biomass-based one. This new paradigm implies not only a gradual phasing out of fossil fuels as a source of energy but also a move away from crude oil as a source of platform chemicals, polymers, drugs, solvents and many other critical materials, and consumer goods that are ubiquitous in our everyday life. If we are to achieve the United Nations’ Sustainable Development Goals, crude oil must be substituted by renewable sources, and in this evolution, biorefineries arise as the critical alternative to traditional refineries for producing fuels, chemical building blocks, and materials out of non-edible biomass and biomass waste. State-of-the-art biorefineries already produce cost-competitive chemicals and materials, but other products remain challenging from the economic point of view, or their scaled-up production processes are still not sufficiently developed. In particular, lignin’s depolymerization is a required milestone for the success of integrated biorefineries, and better catalysts and processes must be improved to prepare bio-based aromatic simple molecules. This review summarizes current challenges in biorefinery systems, while it suggests possible directions and goals for sustainable development in the years to come.

Ergonomics and risk factors for work-related musculoskeletal disorders have been studied extensively in various industry fields. However, only a few decades ago, these issues became a concern in the healthcare sector. Total knee arthroplasty (TKA) is one of the most common procedures performed by orthopaedic surgeons, and it would be desirable to perform it with an ergonomically safer technique. This study evaluated the ergonomic risk of different surgeon positions when performing contralateral TKA using the dominant hand. After the authors defined the four possible surgeon positions according to the most common positions used by surgeons in our environment (position A, on the opposite side of the knee to be operated on; position B, on the same side as the knee to be operated on; position C, with the patient’s legs separated and the surgeon standing between them; and position D, facing the knee to be operated on, at the patient’s feet), we performed an ergonomic analysis using the Rapid Entire Body Assessment (REBA) method. The overall REBA scores (lower score values indicate better ergonomics than higher) were between 7 and 6.5 for position A, between 6.17 and 5.5 for position B, between 5.92 and 5.5 for position C, and between 3.75 and 3.42 for position D. The test–retest and inter-rater reliability values ranged from substantial agreement to almost perfect agreement. Based on the results, we can conclude that the most ergonomic position for a right-handed surgeon to perform a left TKA is facing the left knee, at the patient’s feet (position D).

In this work, we report the caloric effect for an electronic system of the antidot type, modeled by combining a repulsive and attractive potential (parabolic confinement). In this system, we consider the action of a perpendicular external magnetic field and the possibility of having an Aharonov–Bohm flux (AB-flux) generated by a current passing through a solenoid placed inside the forbidden zone for the electron. The energy levels are obtained analytically, and the model is known as the Bogachek and Landman model. We propose to control the caloric response of the system by varying only the AB-flux, finding that, in the absence of an external magnetic field, the maximization of the effect always occurs at the same AB-flux intensity, independently of the temperature, while fixing the external magnetic field at a non-zero value breaks this symmetry and changes the point where the caloric phenomenon is maximized and is different depending on the temperature to which the process is carried. Our calculations indicate that using an effective electron mass of GaAs heterostructures and a trap intensity of the order of 2.896 meV, the modification of the AB-flux achieves a variation in temperature of the order of 1 K. Our analysis suggests that increasing the parabolic confinement twofold increases the effect threefold, while increasing the antidot size generates the reverse effect, i.e., a strong decrease in the caloric phenomenon under study. Due to the great diversity in technological applications that have antidots in electronics, the possibility of controlling their thermal response simply by varying the intensity of the internal current inside the solenoid (i.e., the intensity of AB-flux) can be a platform of interest for experimental studies.

Considering the concerns for emissions reduction in the maritime sector, the present paper evaluates, through modeling and simulation, oxy-fuel combustion in a two-stroke ship engine (2SE) and the best production system configuration to obtain the required oxygen (O2). An initial model of a ship engine is calibrated with the engine manufacturer’s data and then adapted to work with O2 as the oxidant to eliminate nitrogen oxide (NOx) emissions and with exhaust gas recirculation (EGR) to control the in-cylinder combustion temperature. Mixed Ionic–Electronic Conducting (MIEC) membranes produce the necessary O2 from the ambient air, which is heated up and pressurized by a heat exchanger and turbocharging coupled system to provide the air conditions required for the proper operation of the MIEC. Several layouts of this system are evaluated for the full load engine operating point to find the optimum O2 production system configuration. Results reveal that the engine operating under oxy-fuel combustion conditions avoids NOx emissions at the expense of higher brake-specific fuel consumption (BSFC) to obtain the original brake torque, and also expels a stream composed exclusively of CO2 and H2O, which facilitates the separation of CO2 from exhaust gases.

Social Complex Networks in communication networks are pivotal for comprehending the impact of human-like interactions on information flow and communication efficiency. These networks replicate social behavior patterns in the digital realm by modeling device interactions, considering friendship, influence, and information-sharing frequency. A key challenge in communication networks is their dynamic topologies, driven by dynamic user behaviors, fluctuating traffic patterns, and scalability needs. Analyzing these changes is essential for optimizing routing and enhancing the user experience. This paper introduces a network model tailored for Opportunistic Networks, characterized by intermittent device connections and disconnections, resulting in sporadic connectivity. The model analyzes node behavior, extracts vital properties, and ranks nodes by influence. Furthermore, it explores the evolution of node connections over time, gaining insights into changing roles and their impact on data exchange. Real-world datasets validate the model’s effectiveness. Applying it enables the development of refined routing protocols based on dynamic influence rankings. This approach fosters more efficient, adaptive communication systems that dynamically respond to evolving network conditions and user behaviors.

In this study, Ni-Fe alloy nanoparticles were prepared using the proteic sol–gel method, followed by a reduction in H2 at 500 and 700 °C, namely hereafter as NiFe-500 and NiFe-700, respectively. The morphological, structural, and magnetic properties were tuned via the thermal treatment in H2. The samples were studied using XPS, TEM, Mössbauer spectroscopy, DC magnetic measurements, and electrochemical measurements. Ritveld refinements showed that the sample NiFe-500 has FCC (face-centered cubic) and BCC (body-centered cubic) NiFe alloys, while the sample NiFe-700 has only FCC NiFe alloy. For both samples, magnetization measurements in the range of 300–900 K showed the presence of the Griffiths phase, indicating the formation of clusters of either Fe or Ni-Fe alloys rich in Fe. The sample NiFe-500 presented ferromagnetic (FM) transitions at 533, 700, and 834 K, assigned to the alloys Ni37Fe63-FCC, Ni46Fe54-FCC, and Ni55Fe45-FCC, respectively. In contrast, we could not observe the FM transition of the BCC Ni-Fe alloy because of limitations in our experimental setup (T ≤ 900 K). Meanwhile, three FM transitions were observed for the sample NiFe-700 at 480, 655, and 825 K, attributed to the alloys Ni34Fe66-FCC, Ni43Fe57-FCC, and Ni54Fe46-FCC, respectively. At 5 K, the samples NiFe-500 and NiFe-700 have saturation magnetizations of 164.2 and 173.6 emu g−1, respectively. For application in Oxygen Evolution Reaction catalysis, the samples NiFe-500 and NiFe-700 showed different overpotentials of 319 and 307 mV at 10 mA cm−2. These low overpotential values indicate a higher electrochemical activity of the FCC Ni-Fe alloy and, for both samples, a superior electrocatalytic activity in comparison to RuO2 e IrO2 conventional catalysts. Furthermore, the samples showed high electrochemical stability in chrono potentiometric studies for up to 15 h. This current work highlights that the Ni-Fe alloys produced via the proteic sol–gel and with a reduction in H2 methods can be promising for OER systems due to their good performance and low costs.

Several biochars were synthesized from olive stones and used as supports for TiO2, as an active semiconductor, and Pt as a co-catalyst (Pt/TiO2-PyCF and Pt/TiO2-AC). A third carbon-supported photocatalyst was prepared from commercial mesoporous carbon (Pt/TiO2-MCF). Moreover, a Pt/TiO2 solid based on Evonik P25 was used as a reference. The biochars used as supports transferred, to a large extent, their physical and chemical properties to the final photocatalysts. The synthesized catalysts were tested for hydrogen production from aqueous glycerol photoreforming. The results indicated that a mesoporous nature and small particle size of the photocatalyst lead to better H2 production. The analysis of the operational reaction conditions revealed that the H2 evolution rate was not proportional to the mass of the photocatalyst used, since, at high photocatalyst loading, the hydrogen production decreased because of the light scattering and reflection phenomena that caused a reduction in the light penetration depth. When expressed per gram of TiO2, the activity of Pt/TiO2-PyCF is almost 4-times higher than that of Pt/TiO2 (1079 and 273 mmol H2/gTiO2, respectively), which points to the positive effect of an adequate dispersion of a TiO2 phase on a carbonaceous support, forming a highly dispersed and homogeneously distributed titanium dioxide phase. Throughout a 12 h reaction period, the H2 production rate progressively decreases, while the CO2 production rate increases continuously. This behavior is compatible with an initial period when glycerol dehydrogenation to glyceraldehyde and/or dihydroxyacetone and hydrogen predominates, followed by a period in which comparatively slower C-C cleavage reactions begin to occur, thus generating both H2 and CO2.

Remaining useful lifetime (RUL) predictions of electric motors are of vital importance in the maintenance and reduction of repair costs. Thanks to technological advances associated with Industry 4.0, physical models used for prediction and prognostics have been replaced by data-driven models that do not require specialized staff for feature selection, as the model itself learns what features are important. However, these models are usually trained and tested with the same datasets. That makes it difficult to reuse models with different datasets, so they should be retrained with data from the specific motor being analyzed. This paper presents a novel and robust health prognostics technique that predicts the remaining useful lifetime of the bearings of electric motors under different motor conditions (shaft frequency, load, type of bearing) without retraining or fine-tuning the model used. The model integrates the frequency-domain signal analysis and a stacked autoencoder (SAE) with a bidirectional long short-term memory (BiLSTM) neural network. The proposed model is trained with the IMS-bearing dataset and is then tested with IMS, FEMTO, and XJTU-SY datasets without retraining it, providing accurate results in all of them, and proving its robustness with different electric motors and work conditions.

Hydrogen production is mainly based on the use of fossil fuels, but currently, many alternative routes are being developed, among which the photo-reforming of oxygenated organic compounds stands out. Recently, several studies have been carried out in order to develop new techniques to create bio-inspired TiO2 structures. One of these is ‘biotemplating’, a process that replicates a biological system in an inorganic TiO2-based structure. In this study, olive by-products—olive leaves—are valorized as a biotemplate for the synthesis of new Fe-TiO2- and Cu-TiO2-based photocatalysts with the aim of improving the replication of the leaf structure and enhancing hydrogen photoproduction. In conclusion, the incorporation of iron and copper decreases the band gap and increases the energetic disorder at the band edges. Moreover, it is verified by SEM and TEM that the metals are not found forming particles but are introduced into the formed TiO2 structure. The accuracy of the internal and external structure replication is improved with the incorporation of Fe in the synthesis, while the incorporation of Cu substantially improves the production of hydrogen, which is multiplied 14 times under UV light and 6 times under sunlight, as compared to a pure TiO2 structure.

Large volume injection (LVI) in gas chromatography (GC) and online liquid chromatography-gas chromatography (LC-GC) are useful techniques for analyzing the compounds present at very low concentrations in complex samples since they substantially increase the sensitivity of the analysis and simplify sample preparation. LVI avoids the need to concentrate the extract and even the extraction step itself by directly injecting the sample. In online LC-GC, the liquid chromatography (LC) step acts as the sample preparation and/or fractionation step. The main problem in both techniques is the selective elimination of the large volume of solvent without losing the analytes. The TOTAD (through oven transfer adsorption–desorption) interface, based on a widely modified PTV (programmed temperature vaporizer) injector, allows large volumes to be injected into the gas chromatograph using both nonpolar and polar solvents, including water. Consequently, online LC-GC can be carried out whether the LC step is in the normal phase or the reversed phase. Various methods for analyzing trace compounds in food and environmental samples have been developed for LVI and online LC-GC using the TOTAD interface. Such analysis methods require the optimization of several variables common to LVI and online LC-GC and specific variables involved in online LC-GC, which must be optimized by taking into account the nature of the analytes and the characteristics of the sample matrix. This article reviews how each of these variables affects the performance of the analysis.

Synthetic fuels significantly reduce pollutant emissions and the carbon footprint of ICE applications. Among these fuels, oxymethylene dimethyl ethers (OMEX) are an excellent candidate to entirely or partially replace conventional fuels in compression ignition (CI) engines due to their attractive properties. The very low soot particle formation tendency allows the decoupling of the soot-NOX trade-off in CI engines. In addition, innovative piston geometries have the potential to reduce soot formation inside the cylinder in the late combustion stage. This work aims to analyze the potential of combining OMEX with an innovative piston geometry to reduce soot formation inside the cylinder. In this way, several blends of OMEX-Diesel were tested using a radial-lips bowl geometry and a conventional reentrant bowl. Tests were conducted in an optically accessible engine under simulated EGR conditions, reducing the in-cylinder oxygen content. For this purpose, 2-colour pyrometry and high-speed excited state hydroxyl chemiluminescence techniques were applied to trace the in-cylinder soot formation and oxidation processes. The results confirm that increasing OMEX in Diesel improves the in-cylinder soot reduction under low oxygen conditions for both piston geometries. Moreover, using radial lips bowl geometry significantly improves the soot reduction, from 17% using neat Diesel to 70% less at the highest OMEX quantity studied in this paper.