Search Results (19,706)

With each passing year, water scarcity in the world is increasing, drying up rivers, lakes, and dams. Reverse osmosis technology is a very viable alternative which helps to reduce water shortages. One of the challenges is to make the process more efficient, and this can be achieved by improving the capacity by adapting membranes with nanomaterials in order to increase the permeate flux without exceeding the limits established in the process. In this research, brackish water membranes (BW30) were modified with ZnO nanoparticles by interphase polymerization. The modified membranes and BW30 (unmodified) were characterized by FTIR, AFM, contact angle, and micrometer. The membranes were tested in a cross-flow apparatus using 9000 ppm brackish water, and their permeate flux, salt rejection, and concentration polarization were determined. The salt rejection for the 10 mg ZnO NP membrane was 97.13 and 97.77% at 20 and 30 Hz, respectively, sufficient to generate drinking water. It obtained the best permeate flux of 12.2% compared to the BW30 membrane with 122.63 L m⁻² h⁻¹ at 6.24 MPa and 30 Hz, under these conditions, and the concentration polarization increased. Full article

The determination of the cancer prognosis is paramount for patients and medical personnel so that they can devise treatment strategies. Transcriptional-based signatures and subtypes derived from cancer biopsy material have been used in clinical practice for several cancer types to aid in setting the patient prognosis and forming treatment strategies. Other genomic features in cancer biopsies, such as copy number alterations (CNAs), have been underused in clinical practice, and yet they represent a complementary source of molecular information that can add detail to the prognosis, which is supported by recent work in breast, ovarian, and lung cancers. Here, through a systematic strategy, we explored the prognostic power of CNAs in 37 cancer types. In this analysis, we defined two modes of informative features, deep and soft, depending on the number of alleles gained or lost. These informative modes were grouped by amplifications or deletions to form four single-data prognostic models. Finally, the single-data models were summed or combined to generate four additional multidata prognostic models. First, we show that the modes of features are cancer-type dependent, where deep alterations generate better models. Nevertheless, some cancers require soft alterations to generate a feasible model due to the lack of significant deep alterations. Then, we show that the models generated by summing coefficients from amplifications and deletions appear to be more practical for many but not all cancer types. We show that the CNA-derived risk group is independent of other clinical factors. Furthermore, overall, we show that CNA-derived models can define clinically relevant risk groups in 33 of the 37 (90%) cancer types analyzed. Our study highlights the use of CNAs as biomarkers that are potentially clinically relevant to survival in cancer patients. Full article

Objective: This paper aims to evaluate the quality of scientific production in family medicine in Mexico and identify gaps in the development of research in this medical discipline. Methods: This cross-sectional study analyzed original articles, reviews, case reports, and editorials published from Mexico, from the year 2014 to 2023, in the three family medicine journals edited in the country. Several bibliometric indicators were evaluated. Attributes that confer validity in original articles were analyzed, and through random sampling, 10% were selected to determine their quality using checklists. Results: A total of 627 articles were analyzed; among these, 57.89% were original, 19.61% reviews, 17.06% editorials, and 5.42% case reports. Our analysis revealed significant disparities in research activity across regions in Mexico. Productivity, transience, and isolation indices were 2.79, 78.58%, and 54.05%, respectively, while the Price index was 42.74%. A small percentage of articles received funding and followed guidelines for medical research reporting (0.47% and 0.63%, respectively). The analysis of validity attributes in original articles revealed that 92.83% were observational, 88.98% were unicentric, in 47.38%, no sample size calculation was performed or specified, while in 12.12%, sampling was probabilistic. In the evaluation of the original articles, more than 60% showed limitations that compromised their quality. Conclusions: The number of published articles, along with their bibliometric, validity, and quality attributes, reflect significant gaps in the generation and dissemination of knowledge in family medicine in Mexico. This shows a transgenerational problem, identified in many countries, where the advancement of family medicine as a specialty is limited by low research productivity and methodological weaknesses in reporting. Full article

This article presents a symmetrical reduced-size eight-element MIMO antenna array with high electromagnetic isolation among radiators. The array utilizes easy-to-build techniques to cover the n77 and n78 new radio (NR) bands. It is based on an octagonal double-negative metamaterial split-ring resonator (SRR), which enables a size reduction of over 50% for the radiators compared to a conventional disc monopole antenna by increasing the slow-wave factor. Additionally, due to the extreme proximity between the radiating elements in the array, the modal significance (MS) method was employed to identify which propagation modes had the most impact on the electromagnetic coupling among elements. This approach aimed to mitigate their effect by using an electromagnetic barrier, thereby enhancing electromagnetic isolation. The electromagnetic barriers, implemented with strip lines, achieved isolation values exceeding 20 dB for adjacent elements (<0.023 λ) and approaching 40 dB for opposite ones (<0.23 λ) after analyzing the surface current distribution by the MS method. The elements are arranged in axial symmetry, forming an octagon with each antenna port located on a side. The array occupies an area of 0.32 λ² at 3.5 GHz, significantly smaller than previously published works. It exhibits excellent performance for MIMO applications, demonstrating an envelope correlation coefficient (ECC) below 0.0001, a total active reflection coefficient (TARC) lower than −10 dB for various incoming signals with random phases, and a diversity gain (DG) close to 20 dB. Full article

Background/Objectives: Coronavirus Disease 2019 (COVID-19) can cause liver injury and a deterioration of hepatic function. The Model for End-Stage Liver Disease (MELD) score is a good predictor for poor prognosis of hospitalized COVID-19 patients in the United States, Egypt and Turkey. Nevertheless, the best cut-off value for the MELD score to predict mortality in the Mexican population has yet to be established. Methods: A total of 234 patients with COVID-19 were studied in a tertiary-level hospital. Patients were stratified into survivors (n = 139) and non-survivors (n = 95). Receiver operating characteristic curves, Cox proportional hazard models, Kaplan–Meier method, and Bonferroni corrections were performed to identify the predictors of COVID-19 mortality. Results: MELD score had an area under the curve of 0.62 (95% CI: 0.56–0.68; p = 0.0009), sensitivity = 53.68%, and specificity = 73.38%. Univariate Cox proportional hazard regression analysis suggested that the leukocytes > 10.6, neutrophils > 8.42, neutrophil-to-lymphocyte ratio (NLR) > 8.69, systemic immune-inflammation index (SII) > 1809.21, MELD score > 9, and leukocyte glucose index (LGI) > 2.41 were predictors for mortality. However, the multivariate Cox proportional hazard model revealed that only the MELD score >9 (Hazard Ratio [HR] = 1.83; 95% confidence interval [CI]: 1.2–2.8; P_corrected = 0.03) was an independent predictor for mortality of COVID-19. Conclusions: Although the MELD score is used for liver transplantation, we suggest that a MELD score >9 could be an accurate predictor for COVID-19 mortality at admission to ICU requiring mechanical ventilation. Full article

Introduction: This study investigates how traumatic injuries alter joint movements in the ankle and foot. We used a brain injury model in rats, focusing on the hippocampus between the CA1 and dentate gyrus. Materials and Methods: We assessed the dissimilarity factor (DF) and vertical displacement (VD) of the ankle and metatarsus joints before and after the hippocampal lesion. We analyzed joint movements in rats after the injury or in rats treated with resveratrol, exercise, or a combination of both. Results: Resveratrol facilitated the recovery of DF in both legs, showing improvements in the ankle and metatarsus joints on the third and seventh days post-injury. The hippocampal lesion affected VD in both legs, observed on the third or seventh day after the injury. Both exercise and resveratrol partially recovered VD in the ankle and metatarsus joints on these days. These effects may be linked to increased hippocampal neurogenesis and reduced neuroinflammation. Conclusions: The study highlights the benefits of resveratrol and exercise in motor recovery following brain injury, suggesting their potential to enhance the quality of life for patients with neurological disorders affecting motor function and locomotion. These findings also suggest that resveratrol could offer a promising or complementary alternative in managing chronic pain and inflammation associated with orthopedic conditions, thus improving overall patient management. Full article

Symbolic regression plays a crucial role in machine learning and data science by allowing the extraction of meaningful mathematical models directly from data without imposing a specific structure. This level of adaptability is especially beneficial in scientific and engineering fields, where comprehending and articulating the underlying data relationships is just as important as making accurate predictions. Genetic Programming (GP) has been extensively utilized for symbolic regression and has demonstrated remarkable success in diverse domains. However, GP’s heavy reliance on evolutionary mechanisms makes it computationally intensive and challenging to handle. On the other hand, Particle Swarm Optimization (PSO) has demonstrated remarkable performance in numerical optimization with parallelism, simplicity, and rapid convergence. These attributes position PSO as a compelling option for Automatic Programming (AP), which focuses on the automatic generation of programs or mathematical models. Particle Swarm Programming (PSP) has emerged as an alternative to Genetic Programming (GP), with a specific emphasis on harnessing the efficiency of PSO for symbolic regression. However, PSP remains unsolved due to the high-dimensional search spaces and local optimal regions in AP, where traditional PSO can encounter issues such as premature convergence and stagnation. To tackle these challenges, we introduce Dynamical Sphere Regrouping PSO Programming (DSRegPSOP), an innovative PSP implementation that integrates DSRegPSO’s dynamical sphere regrouping and momentum conservation mechanisms. DSRegPSOP is specifically developed to deal with large-scale, high-dimensional search spaces featuring numerous local optima, thus proving effective behavior for symbolic regression tasks. We assess DSRegPSOP by generating 10 mathematical expressions for mapping points from functions with varying complexity, including noise in position and cost evaluation. Moreover, we also evaluate its performance using real-world datasets. Our results show that DSRegPSOP effectively addresses the shortcomings of PSO in PSP by producing mathematical models entirely generated by AP that achieve accuracy similar to other machine learning algorithms optimized for regression tasks involving numerical structures. Additionally, DSRegPSOP combines the benefits of symbolic regression with the efficiency of PSO. Full article

The increasing prevalence of diabetes worldwide, including in Mexico, presents significant challenges to healthcare systems. This has a notable impact on hospital admissions, as diabetes is considered an ambulatory care-sensitive condition, meaning that hospitalizations could be avoided. This is just one example of many challenges faced in the medical and public health fields. Traditional healthcare methods have been effective in managing diabetes and preventing complications. However, they often encounter limitations when it comes to analyzing large amounts of health data to effectively identify and address diseases. This paper aims to bridge this gap by outlining a comprehensive methodology for non-physicians, particularly data scientists, working in healthcare. As a case study, this paper utilizes hospital diabetes discharge records from 2010 to 2023, totaling 36,665,793 records from medical units under the Ministry of Health of Mexico. We aim to highlight the importance for data scientists to understand the problem and its implications. By doing so, insights can be generated to inform policy decisions and reduce the burden of avoidable hospitalizations. The approach primarily relies on stratification and standardization to uncover rates based on sex and age groups. This study provides a foundation for data scientists to approach health data in a new way. Full article

Background/Objectives: Estrogens and HPV are necessary for cervical cancer (CC) development. The levels of the G protein-coupled estrogen receptor (GPER) increase as CC progresses, and HPV oncoproteins promote GPER expression. The role of this receptor is controversial due to its anti- and pro-tumor effects. This study aimed to determine the effect of GPER activation, using its agonist G-1, on the transcriptome, cell migration, and invasion in SiHa cells and non-tumorigenic keratinocytes transduced with the HPV16 E6 or E7 oncogenes. Methods: Transcriptome analysis was performed to identify G-1-enriched pathways in SiHa cells. We evaluated cell migration, invasion, and the expression of associated proteins in SiHa, HaCaT-16E6, and HaCaT-16E7 cells using various assays. Results: Transcriptome analysis revealed pathways associated with proliferation/apoptosis (TNF-α signaling, UV radiation response, mitotic spindle formation, G2/M cell cycle, UPR, and IL-6/JAK/STAT), cellular metabolism (oxidative phosphorylation), and cell migration (angiogenesis, EMT, and TGF-α signaling) in SiHa cells. Key differentially expressed genes included PTGS2 (pro/antitumor), FOSL1, TNFRSF9, IL1B, DIO2, and PHLDA1 (antitumor), along with under-expressed genes with pro-tumor effects that may inhibit proliferation. Additionally, DKK1 overexpression suggested inhibition of cell migration. G-1 increased vimentin expression in SiHa cells and reduced it in HaCaT-16E6 and HaCaT-16E7 cells. However, G-1 did not affect α-SMA expression or cell migration in any of the cell lines but increased invasion in HaCaT-16E7 cells. Conclusions: GPER is a promising prognostic marker due to its ability to activate apoptosis and inhibit proliferation without promoting migration/invasion in CC cells. G-1 could potentially be a tool in the treatment of this neoplasia. Full article

Trajectory interception is a critical synchronization element in the transportation and manufacturing sectors using robotic platforms. This is usually performed by matching the position and velocity of a target object with the position and velocity of the robot interceptor. However, the synchronization task is exasperated by (i) the proper gain tuning of the controller, (ii) the dynamic response of the robotic platform, (iii) the velocity constraints in the actuators, and (iv) the trajectory profile exhibited by the moving object. This means that the interception time is not controlled, which is critical for energy optimization, resources, and production. This paper proposes a prescribed time trajectory interception algorithm for robot manipulators. The approach uses the finite-time convergence properties of sliding mode control combined with a terminal attractor based on a time base generator. The combined approach guarantees trajectory interception in a prescribed time with robust properties. Simulation studies are conducted using the first three degrees of freedom (DOFs) of a RV-M1 robot under single- and multi-object interception tasks. The results verify the effectiveness of the proposed methodology under different hyperparameter configurations. Full article

When dealing with complex models in real situations, many optimization problems require the use of more than one objective function to adequately represent the relevant characteristics of the system under consideration. Multi-objective optimization algorithms that can deal with several objective functions are necessary in order to obtain reasonable results within an adequate processing time. This paper presents the multi-objective version of a recent metaheuristic algorithm that optimizes a single objective function, known as the Majority–minority Cellular Automata Algorithm (MmCAA), inspired by cellular automata operations. The algorithm presented here is known as the Multi-objective Majority–minority Cellular Automata Algorithm (MOMmCAA). The MOMmCAA adds repository management and multi-objective search space density control to complement the performance of the MmCAA and make it capable of optimizing multi-objective problems. To evaluate the performance of the MOMmCAA, results on benchmark test sets (DTLZ, quadratic, and CEC-2020) and real-world engineering design problems were compared against other multi-objective algorithms recognized for their performance (MOLAPO, GS, MOPSO, NSGA-II, and MNMA). The results obtained in this work show that the MOMmCA achieves comparable performance with the other metaheuristic methods, demonstrating its competitiveness for use in multi-objective problems. The MOMmCAA was implemented in MATLAB and its source code can be consulted in GitHub. Full article

Installation of flat solar collectors (FSCs) has been increasing due to the zero cost of renewable energy. However, the performance of this equipment is limited by the area, the material and the thermophysical properties of the working fluid. To improve the properties of the fluid, metal and metal oxide nanoparticles have mainly been used. This paper presents the performance assessment of the FSCs using simple and hybrid carbon nanofluids of low thermal capacity. Energy and mass balance modeling was performed for this study. A parametric analysis was conducted to examine the impact of key variables on the performance of the solar collectors using simple graphite and fullerene nanofluids, as well as hybrid metal–oxide–carbon nanofluids. From the results of heat transfer in FSCs, using graphite and fullerene nanofluids, it can be concluded that adding these nanoparticles improves the convection coefficient by 40% and 30%, respectively, with 10% nanoparticles. The graphite and fullerene nanoparticles can enhance the efficiency of FSCs by 2% and 1.5% more than base fluid. As the decrease in efficiency using fullerene with magnesium oxide is less than 0.2%, fullerene hybrid nanofluids could still be used in FSCs. Full article

Cattle ranching is an economic activity responsible for the loss of large extensions of tropical dry forest around the world. Several studies have demonstrated that the use of inadequate practices of this activity in tropical forests (e.g., fire, agrochemicals, and lack of rotational grazing systems of cattle in pastures) have negative consequences on dung beetle diversity and their ecological functions. In the present study, the influence of the cattle ranching duration gradient on the diversity of dung beetles and seed removal was evaluated. This study was carried out in pastures with different times of establishment of cattle ranching (between 4 and 40 years) in a tropical dry forest of Mexico. Overall, the species richness of dung beetles was similar along the gradient of grazing ages. However, the diversity of common (q1) and dominant (q2) species decreased and was associated with an increasing abundance of exotic species and a decreasing abundance of native species. Seed removal was mainly carried out by four beetle species, among which the exotic species Digitonthophagus gazella was the most important. The results establish that the duration of cattle ranching primarily influences the composition of dung beetle communities, as reflected in changes in the structure and function of their assemblages in the pastures. Although native dung beetles persist at low abundances along this gradient, the consequences of land use changes are undeniable in other similar ecosystems where these species could definitively disappear. Full article

The present paper tests the use of an agricultural futures minimum tracking error portfolio to replicate the price of the Mexican Hass avocado (a non-commodity). The motivation is that this portfolio could be used to balance the basis risk that the avocado price hedge issuer could face. By performing a backtest of a theoretical avocado producer from January 2000 to September 2023, the results show that the avocado producer could hedge the avocado price by 94%, with the hedge offered by a theoretical financial or government institution. Also, this issuer could balance the risk of such a hedge by buying a coffee–sugar futures portfolio. The cointegrated or long-term relationship shows that using such a futures portfolio is useful for Mexican Hass avocado price hedging. This paper stands as one of the first in testing futures portfolios to offer a synthetic hedge of non-commodities through a commodities’ futures portfolio. Full article

The continuous expansion of the vehicle fleet contributes to escalating emissions, with the transportation sector accounting for approximately 21% of CO₂ emissions, based on 2023 data. Focused on reducing emissions and reliance on fossil fuels, the study observes the shift from internal combustion vehicles to electric and hybrid models since 2017. Despite advancements, these vehicles still lack optimal efficiency and suffer from limited range, deterring potential buyers. This article aims to evaluate the range-extending technologies for electric vehicles, emphasizing efficiency, low pollution, and integration compatibility. An algorithm incorporating equations representing mechanical or electrical component curves is developed for Extended-Range Electric Vehicles, facilitating insight into potential range extender behavior. The core objectives of this study involve optimizing the entire powertrain system to ensure peak efficiency. Experimental tests demonstrate that integrating an auxiliary power unit enhances range, with an internal combustion engine generator configuration extending the travel distance by 35.35% at a constant speed. Moreover, with the use of an Equivalent Consumption Minimization Strategy control, the distance traveled increases up to 39.28% on standard driving cycles. The proposed methodology, validated through practical implementations, allows for comprehensive energy analyses, providing a precise understanding of vehicle platform performance with integrated range extenders. Full article