Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through accurate diagnosis and classification of faults in three-phase induction motors, using artificial intelligence techniques, by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network, using the scanning method with multiple training and validation iterations, with the introduction of new data. The results of these tests showed that the network had excellent generalization in all the situations evaluated, achieving the following accuracy rates: Motor without fault = 94.2%, Unbalance fault = 95%, Bearings with fault =98% and Stator with fault = 95%.

This article describes the study and digital implementation of a system onboard a TMS 3208F28335 ® DSP for vector control of the bearing motor speed with 4 poles split winding with 250W of power. Smart techniques: ANFIS and Neural Networks were investigated and computationally implemented to evaluate the bearing motor performance under the following conditions: operating as an estimator of uncertain parameters and as a speed controller. Therefore, the MATLAB program and its toolbox were used for the simulations and the parameter adjustments involving the structure ANFIS (Adaptive-Network-Based Fuzzy Inference System) and simulations with the Neural Network. The simulated results showed a good performance for the two techniques applied differently: the estimator and a speed controller using both a model of the induction motor operating as a bearing motor. The experimental part for velocity vector control uses three control loops: current, radial position, and speed, where the configurations of the peripherals, that is, the interfaces or drivers for driving the bearing motor.

This article provides insight into flow measurement techniques in water injection wells in oil production fields, with a particular focus on the initial phases of operation. Consequently, the method created by Ramey in 1962, originally intended for estimating the temperature of the injection fluid, has been adapted to calculate the flow rate. In this technique, the calculation This procedure is based on the correlation between the thermal flux formed in the well. The discrepancy between the temperatures of the injected liquid and the geothermal temperature of the reservoir is the main source of the systematic errors in Ramey’s technique. To a lesser extent, but still significant, failure to observe the injection time in a fluid variation also results in an error problem that needs the failure to adhere to the scheduled injection time for a fluid alteration also yields a notable error dilemma that needs to be fixed. The reduction of listed systematic errors is the product of the main part of this article.

A mathematical model, composed of two non-linear differential equations that describe the population dynamics of CD4 T cells in the human immune system, as well as viral HIV particles, is proposed. The invariance region is determined, classical equilibria stability analysis is performed using the basic reproduction number, and numerical simulations are carried out, in order to illustrate stability results. Later, the model is modified with a delay term, which describes the time that cells require for immunological activation. This generates a two-dimensional integro-differential system, which is transformed into a system with three ordinary differential equations, via auxiliary variable use. For the new model, equilibrium points are determined, their local stability is examined, and results are studied by way of numerical simulation.

Every simplification has the potential to add a systematic error to the measured value. Sometimes, the addition does not render the measurement useless. In the case of oil well flow measurements based on the thermal properties of the fluid in the well, the addition of a systematic error of up to 20% can occur if the variation in the internal energy of the fluid and, most importantly, the transient nature of heat transfer in the completion are not taken into account. This error is a mathematical consequence of the simplifications chosen. To work within the first hours of good operation, given the need for immediate application in a prototype water injection well developed and installed at the UFRN, this article presents an analytical solution for calculating the flow rate by solving the differential equations obtained by applying the principles of mass and heat transfer. As a result, at the cost of greater complexity, the systematic error drops to values of less than 1% in the first two hours of operation of the well, as seen throughout this document.

Landscape changes based on spectral responses allow showing plant cover changes through diversity, composition, and ecological connectivity. The spatial and temporal vegetation dynamics of the Bijagual Massif from 1986 to 2021 were analyzed as a measure of ecological integrity, conservation, and territory. The covers identified were high open forest (Hof), dense grassland of non-wooded mainland (Dgnm), a mosaic of pastures and crops (Mpc), lagoons (Lag), and bare and degraded lands (Bdl). The Bijagual Massif has 8,574.1 ha. In 1986, Dgnm occupied 42.6% of the total area, followed by Mpc (32.8%) and Hof (24.5%); by 2000, Mpc and Hof increased (43.7 and 28.1%, respectively), while Dgnm decreased (28%); by 2021, Dgnm was restricted to the northeastern zone and continued to decrease (25.2%), Mpc occupied 52.9%, Hof 21.7% and Bdl 0.1%. Of the three fractions of the connectivity probability index, only dPCintra and dPCflux contribute to ecological connectivity. Hof and Dgnm show patches with biota habitat quality and availability. Between 1986 and 2021, Dgnm lost 1,489 ha (41%) and Hof 239.5 ha (11%). Mpc replaced various covers (1,722.2 ha; 38%) in 2021. Bijagual has a valuable biodiversity potential limited by Mpc. Territorial planning and sustainable agroecological and ecotourism proposals are required due to the context of the ecosystems.

Introduction: At the end of 2019 the Covid-19 pandemic caused a huge amount of restrictions across the world which affected teaching around the world as it was not prepared to move from traditional face-to-face to an online conversion overnight, so the aim of this paper is to determine the changes in undergraduate anatomy teaching strategies post-pandemic compared to pre-pandemic. Methods: A systematic review was conducted based on the PRISMA statement. Data were retrieved from the searches conducted in the different databases, and the articles to be retrieved were selected with the help of the Rayyan platform. Using the OPMER and JBI tools, the quality of evidence was assessed for the selection of the articles included in the review. Results: The teaching of medicine, specifically anatomy, presented serious problems due to the fact that throughout history it has been taught in the classroom by means of cadaveric dissection and when this ceased to be available, new methods arose through the use of digital resources to try to make up for the needs of the lack of dissection laboratories which lost a great deal of content in this restructuring, which have been improving with the passing of the courses taught and the development of new material that has been better adapted to the needs of the students. Discussion: The authors agree that in the future the best way to teach anatomy will continue to be face-to-face dissection without neglecting the strategies developed during this pandemic and to be taken if necessary to restructure curricula in the future to improve teaching.

Tissue inhibitors of metalloproteinases (TIMPs) are essential players during tumor progression in various types of cancer, including breast cancer. TIMPs regulate the activity of matrix metallo-proteinases (MMPs) at different levels. However, a dual role for TIMPs in breast carcinogenesis, either promoting or inhibiting tumor progression, has been observed. By a comprehensive bioin-formatics analysis using different databases, we report that of the four TIMPs, only TIMP-1 is overexpressed in tumor breast tissue. However, TIMP-2, TIMP-3, and especially TIMP-4 present lower levels in breast tumor tissue than in normal tissue. Interestingly, high levels of TIMP-1 are associated with shorter survival in breast cancer patients. In contrast, low TIMP-2, TIMP-3, and TIMP-4 levels are associated with poor survival. Function enrichment analysis showed that TIMP-3 has the highest interactions with its target proteins and a higher number of processes re-lated to mammary carcinogenesis. We found differential expression of TIMP-1 concerning TIMP-3 and TIMP-4 in breast cancer and normal tissue. TIMP-4 has a significant relationship with cancer staging, including tumor size, lymph node spread, and metastasis. The correlation between immune cell infiltrates and TIMP expression is of great importance as they provide in-formation on breast cancer diagnosis, prognosis, progression, and response to treatment.

Background. The treatment for patients with high-grade gliomas includes radiation therapy and temozolomide. However, some patients do not respond to temozolomide because they have a methylation reversal mechanism through the enzyme O6-methylguanine-DNA-methyltransferase. This biomarker has been used as a prognostic factor in patients receiving treatment with temozolomide. However, not all patients respond in the same way, which suggests the existence of other genes involved in resistance to temozolomide. Materials and Methods. A group of 31 patients with high-grade gliomas was recruited and were clinically, image pattern, and pathologically characterized. The sequencing of 324 genes related to different types of cancer was performed to detect mutations. Subsequently, a statistical analysis was conducted to determine the mutated genes that were most related to resistance to treatment. Results. The genes related to the second relapse of patients with high-grade glioma after the use of temozolomide according to Stupp protocol and metronomic dose were PIK3C2B, KIT, ERBB3, and MLH1. Conclusions. Considering the results obtained, we suggest that the mutations in the four genes and the methylation of the gene promoter that codes for MGMT protein could be related to the clinical evolution of patients with high-grade gliomas and its response to treatment with temozolomide.

With the widespread use of electric machines there is a growing need to extract information from the machines to optimize their control systems and maintenance management. The present work shows the development of an embedded system to perform the monitoring of an squirrel cage induction motor rotor physical variables. The system comprises: circuit to acquire desirable rotor variable(s) value(s) and send it to the computer; a rectifier and power storage circuit that besides converting an alternating current in continuous also store energy for a certain amount of time to wait the motor engine shutdown; and magnetic generator that harvest energy from rotating field to power the circuits mentioned above. The embedded system is set on the rotor, making it difficult to power the system because it is rotating, problem solved with the construction of the magnetic generator eliminating the need of using batteries or collector rings and send data to the computer using a wireless NRF24L01 module. For the proposed system validation a temperature sensor (DS18b20) was used, variable known as the most important when identifying the need for maintenance and control systems. Tests were made getting satisfactory results proving the viability of using sensors on the rotor.

The concept of geostationary VHTS (Very High Throughput Satellites) is based on multibeam coverage with intensive frequency and polarization reuse in addition to the use of larger bandwidths in the feeder links, in order to provide high capacity satellite links at a reduced cost per Gbps in orbit. The dimensioning and design of satellite networks based on VHTS imposes the analysis of multiple trade-offs to achieve an optimal solution in terms of cost, capacity and figure of merit of the user terminal. In this paper, we propose a new method for sizing VHTS satellite networks based on an analytical expression of the forward link CINR (Carrier-to-Interference-plus-Noise Ratio) that is used to evaluate the trade-off of different combinations of system parameters. The proposed method considers both technical and commercial requirements as inputs including the constraints to achieve the optimum solution in terms of the user G/T, the number of beams and the system cost. The cost model includes both satellite and ground segments. Exemplary results are presented with feeder links using Q/V bands, DVB-S2X and transmission methods based on CCM and VCM (Constant and Variable Coding and Modulation, respectively) in two scenarios with different service areas.

Malaria remains a life-threatening disease in many tropical countries. Honduras has successfully reduced malaria transmission as different control methods have been applied focusing mainly on indoor mosquitoes. The selective pressure exerted by the use of insecticides inside the households could modify the feeding behavior of the mosquitoes forcing them to search for available animal hosts outside the houses. These animal hosts in the peridomicile could consequently become an important factor in maintaining vector populations in endemic areas. Herein, we investigated the blood meal sources and Plasmodium spp. infection on anophelines collected outdoors in endemic areas of Honduras. Individual PCR reactions with species-specific primers were used to detect five feeding sources on 181 visibly engorged mosquitoes. In addition, a subset of these mosquitoes where chosen for pathogen analysis by a nested PCR approach. Most mosquitoes fed on multiple hosts (2 to 4), and 24.9% of mosquitoes were fed on a single host, animal or human. Chicken and bovine were the most frequent blood meal sources (29.5% and 27.5% respectively). The average human blood index (HBI) was 22.1%. None of the mosquitoes was found to be infected with Plasmodium spp. Our results show the opportunistic and zoophilic behavior of Anopheles mosquitoes in Honduras.

This paper presents the implementation of a control strategy based on fuzzy logic artificial intelligence (AI) for speed regulation of an electromagnetic frequency regulator (EFR) prototype, aiming to eliminate the dependence on knowledge of physical parameters in the most diverse operating conditions. Speed multiplication is one of the most important steps in power generation wind. Gearboxes are generally used for this purpose. However, they have a reduced lifespan and a high failure rate and are still noise sources. The search for new ways to match the speed (and torque) between the turbine and the generator is an important research area to increase the energy, financial and environmental efficiency of wind systems. The EFR device is an example of an alternative technology that this team of researchers has proposed. It counts the fact of taking advantage of the main advantages of an induction machine with the rotor in a squirrel cage positively. In the first studies, the EFR control strategy consisted of the conventional PID controllers, which has several limitations widely discussed in the literature. This strategy also limits the EFR's performance, considering its entire operating range. The simulation program was developed using the Matlab/Simulink platform, while the experimental results were obtained in the laboratory emulating the EFR-based system. The EFR prototype used has 2 poles, a nominal power of 2.2 kW, and a nominal frequency of 60 Hz. Experimental results were presented to validate the efficiency of the proposed control strategy.

This paper has the objective of implementing the radial rotor position control of a three-phase bearingless induction machine with split winding and optimized drive structure, using the ADRC technique. Because it is a multivariable, nonlinear, time-varying system with coupled variables, it is necessary to use advanced control strategies in order for the system to operate efficiently and with good dynamic performance. The ADRC controller considers the total disturbance composed of unmodeled dynamics, nonlinearities, uncertainties, and load variations, as a new system state, to be estimated in real-time through an extended state observer. In this mode, disturbances are compensated in real-time, eliminating regime errors and with good response to disturbances in general.

This article presents a critical review of the current and future state of “business architecture” within the enterprise architecture frameworks. The review is conducted in the following four stages. In the first stage of analysis, the research and publication questions are established. In the second stage, the search for information is designed and implemented using the Prisma methodology, and once the selection criteria were applied, 75 article-type documents were selected from Scopus, WoS, and Dimensions databases. In the third stage, the information analysis is conducted, and then it is synthesized. Finally, in fourth stage, the discussion is elaborated. This research contributes in understanding the current and future trends of business architecture in Industry 4.0. Considering the challenges of the changing market, the results show the need to implement architectures and dynamic business models, including their artifacts, in a globalized context immersed in new technologies.

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s.

In recent times, using image-based deep learning techniques has been very useful for classifying fruits. However, mango is a particular challenge because its shelf life is directly related to the degree of maturity at the time of harvest, which is determined mainly through the visual assessment of the operators when sorting them, doing it incorrectly, coupled with the blows and compressions exposed during storage and transport, trigger the deterioration in their quality, hence the importance of this operation. In this sense, the purpose of this study focuses on designing a computational model based on Deep Learning that uses a convolutional neural network (CNN) parameterized to select mangoes according to their color tone based on their maturity stage. A dataset was established from 201 labeled images, then a CNN architecture was built, obtaining a model on which 3 criteria were evaluated: the number of convolution and grouping layers, the resolution of the input images and the number of training epochs required. As a result, an input image resolution of 32 x 32 pixels was achieved, the process included a convolution layer followed by pooling that fed another layer that processed the results and ended in an output layer with 2 neurons under the One Hot Encoding representation. The model required 20 epochs to perfect its training, achieving 96.04 % accuracy in mango image selection. This study supports the potential of CNNs in quality analysis through mango color selection, even in uncertainty scenarios.

(1) Background: During physical exercise, ventilation (VE) adapts to the intensity and characteris-tics of the effort. The breathing pattern is defined as the relationship between the tidal volume (VT) and breathing frequency (BF) for a given VE. The aim of this study was to evaluate whether inspiratory muscle training influenced the response of the breathing pattern during an incremen-tal effort in amateur cyclists. (2) Methods: 18 amateur cyclists completed an incremental test to exhaustion with gas analysis on a cycle ergometer and spirometry. Cyclists were randomly as-signed to two groups (IMTG=9; CON=9). The IMT group completed 6 weeks of inspiratory muscle training (IMT) using a PowerBreathe K3® device at 50% of the maximum inspiratory pressure (Pimax). The workload was adjusted weekly. After the 6-week intervention, the cyclists repeated the incremental exercise test with gas analysis and spirometry. The response of the breathing pat-tern was evaluated during the incremental exercise test. (3) Results: Pimax increased in the IMTG (p<0.05; +19.62%). Variables related to the breathing pattern response showed no differences be-tween groups after the intervention (EXPvsCON; p>0.05). Likewise, no differences were found in the IMTG after training (PREvsPOST; p>0.05). (4) Conclusions: IMT improved the strength of in-spiratory muscles and sports performance in amateur cyclists. These changes were not attributed to alterations in the response of the breathing pattern.

Followed by the aspect of respect for rights 20, the social and environmental issue has a score of 19.9, the dialogue issue is 19. In short, the proximity plan indicates that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín environmental dialogue table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. The proximity plan shows us that the policies used regarding the issue of generating employment and economic growth (Empresa Mantaro Perú SAC), continue to be the strategic variables in the first instance, but that they are far from the social actors (Rural Communities of Aco , Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and Ombudsman's Office (rights) that are currently closer together. The actor from the Archbishop of Huancayo referred to communication is one of the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are closer together today. The actor from the Archbishopric of Huancayo referred to communication, is within the driving variables of power. but they are far from the social actors (rural communities of Aco, Vico, Cruz Pampa and others) and environmental (Junín Environmental Dialogue Table) and the Ombudsman's Office (rights) that are currently closer together. The actor of the Archbishopric of Huancayo referred to communication, is within the driving variables of power.

La minería es una industria intensiva en energía que requiere una fuente estable de electricidad. Con el aumento de la demanda de minerales y la disminución de las leyes, se espera que la demanda de energía aumente en un 36% para el 2035. Este aumento de la demanda depende del uso de combustibles fósiles ya que la electricidad producida y comprada en las empresas mineras se basan principalmente en el caso de los combustibles fósiles, los costos de los sistemas de almacenamiento de energía solar y eólica han caído a una escala sin precedentes, lo que alienta a las empresas mineras a probar estas tecnologías.Entre los factores que influyen en el uso de energía renovable, se encuentra el beneficio a la comunidad en la etapa de operación y cierre o post-cierre de un proyecto minero, el siguiente factor es el perfil de consumo eléctrico o demanda es importante para el cálculo de su almacenamiento o no, de las energías renovables a utilizar. Como tercer factor está el cálculo del consumo en los procesos. Los rangos de penetración determinarán si es posible su almacenamiento de energía, y si es posible utilizar control híbrido (solar - eólico), para ello es importante el uso de herramientas digitales como el programa Holmer, es una herramienta importante que técnica y económica.El gobierno debe tener una perspectiva que ayude en los temas regulatorios necesarios para impulsar proyectos de energía renovable con diferentes incentivos e intereses según el contexto minero.

Background and objectives: In the current context, the 2021 presidential elections in Peru distance from the social objective, not being objectively represented, that is why we analyze their validity, we determine the distances between the parties if they are extreme, the correspondences with the departments and their prospects, in the surveys we propose which departments influence the results. Methods: We use a mixed methodology, qualitative analysis, it will be multidimensional with the support of statistical methods and programs such as R Studio, worddj, Gephi, and Iramuteq. The quantitative analysis will be through factor analysis, correspondence and discriminant analysis to the data of the election Results and conclusions: The textual analysis mentions that there are dimensions such as the social issue, the results of the surveys and democracy that are far apart, regarding the electoral issue. This inculcates to work both on the part of the organisms that carry out these processes, as well as the initiative of the candidates, and the media. Regarding the quantitative analysis, it is detailed that the representative parties must be greater than 4.0 million voters, to make representative parties at the national level. The classification regarding the percentage of votes is given in three groups, highlighting the independence of the Peru Libre political party in these . In the correspondence analysis we can detail that both Fuerza Popular and Peru Libre are extremist parties. The vast majority of Peruvian citizens have an ideological tendency, intermediate between both parties. The prospective projections with a cross section give a victory to the Peru Libre party. In the discriminant analysis, the sample for the surveys focuses on 7 departments that does not include the Peruvian capital.

The La Oroya metallurgical complex reveals a process of structural or power pathology, where economic and environmental social policies lead to a citizenry without systemic freedom, characterized by environmental contamination (PAMA executed on average 67%, some processes with 0%) and sanitary (plumbosis 8.39 to 10.28 μg / dL), the economic dependence of this industry is also observed, which caused the weakening of the communities to maintain autonomy over their own destinies.The Doe Run Peru, operated the metallurgical complex, creating added value to the extractive-export chain of mineral concentrate, but also took advantage of the legal loopholes, the little coordination between the State institutions and the little authority of the supervisory institutions to get the most out of it, complying with the law in some cases, but without moral quality in some others, such as health in the oroinos.Government institutions must have strong links that provide a normative framework with prospects with sustainable development, where the beneficiary is society and not only certain interest groups. The economic benefits of the metallurgical complex operation are multiple, which begin with labor, the purchase and sale of materials and services, the collection of taxes and energy.

This article analyzes laws, normative reforms and regulations surrounding the practices and mechanisms of control over the consumption, cultivation and commercialization of cannabis. On the one hand, I investigate regulatory practices that deny rights for the consumption of cannabis classified as recreational. On the other hand, I study regulatory practices that extend rights for the production, consumption and commercialization of cannabis classified as medicinal and industrial. The empirical moment is composed of four stages and methodological tools. First, I conduct an analysis of state policies. Then, I analyze nine laws and codes on cannabis regulation from 1970 to the present. Next, I perform five semi-structured interviews. Finally, I develop two life stories of former people deprived of their liberty for cannabis cultivation and trade. In this way, I trace forms of regulation on multiple demands, knowledge, interests and beliefs. This research focuses on state regulation. Cannabis regulatory regimes are conceived as constructions of political, social and economic truths, meanings and rights. In sum, the answer to the question of how collective demands around cannabis are regulated in Ecuador stipulates that state action reinforces stigmatization and criminalization. In addition, it legitimizes moral entrepreneurs and counter-movements around cannabis.

In recent years, music education in Ibero-America has been losing ground within the school environment in favor of the development of curricular systems that benefit academic results in standardized tests. Despite this, several studies in the field of cognitive neurosciences have found evidence of great relevance and in which it can be observed how music education can favor cognitive development and performance in practically all stages of human development, with important results in language tasks, attention, and executive functions such as planning, inhibition, cognitive flexibility and working memory.

Road markings play an important role in road safety because they provide significant information to drivers about the road. For that reason, they must be replaced when they are not correctly perceived by road users. To analyse which are the main factors that affect road marking perception over time, a test section was designed in a two-lane rural highway, running actual traffic over 18 different types of markings fabricated with different combinations of drop-on materials. Chromatic coordinates, luminance and retroreflectivity of each sample were measured during 18 months in order to study their evolution over time. The results obtained show different behaviours depending on the aggregates and application method used. An increment of the durability has been observed with the use of different layers and mixtures of glass microbeads with different sizes.

Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE) and is considered one of the leading causes of mortality. Multiple immunological pathways are involved in the pathogenesis of SLE, which makes it imperative to deepen our knowledge about this disease's immunopathological complexity and explore new therapeutic targets. Since an altered redox state contributes to immune system dysregulation, this document briefly addresses the role of oxidative stress (OS), oxidative DNA damage, antioxidant enzymes, mitochondrial function, and mitophagy in SLE and LN. Although adaptive immunity's participation in the development of autoimmunity is undeniable, increasing data emphasize the importance of innate immunity elements, particularly the Toll-like receptors (TLRs) that recognize nucleic acid ligands, in inflammatory and autoimmune diseases. Here, we discuss the intriguing role of TLR7 and TLR9 in developing SLE and LN. Also included are the essential characteristics of conventional treatments and some other novel and little-explored alternatives that offer options to improve renal function in LN.

This paper has a twofold objective. The first aim is to present a comprehensive bibliographical analysis of journal articles and book chapters on fuzzy-random option pricing (FROP) over the WoS and SCOPUS databases. It follows PRISMA criteria and takes special care of developments in continuous time. Thus, we described the principal findings about the research streams, outlets and authors of this topic and lets us suggest further research. The second contribution is motivated by the fact that the bibliographical revision has identified a lack of developments on equilibrium models on the yield curve. This question motivates extending Vasicek’s yield curve equilibrium model to introduce fuzziness in the parameters that govern interest rate movements (speed of reversion, equilibrium short-term interest rate, and volatility). Likewise, this paper develops an empirical application on the term structure of fixed income public bonds with the highest credit rating in the Euro area.

This paper presents a methodology to detect the erosion and progradation effects in the Atrato river delta, located in the Gulf of Urabá in Colombia using SAR (Synthetic Aperture Radar) data. Other authors have studied this area by using optical images, encountering difficulties given the persistent presence of clouds in this region. SAR imagery is rescaled, despeckled, composed, classified, registered and processed to obtain a heatmap that shows the overall changes during eight years (2016–2023) in one single image. Since this technology uses microwave active signals, the presence of clouds and other environmental phenomena was not a problem, and all the images from the selected dates could be processed and analyzed. Also, detection of the dynamic processes was obtained for every period considered in this study with respect to the previous one in time, calculating areas and detecting the regions subject to the erosion and progradation processes. The multitemporal analysis performed found that the newly created mouth is the most active area for these processes, coinciding with other studies. A comparison of these findings with the Oceanic Niño Index (ONI) showed a relative delayed coupling to the erosion process and a coupling of progradation with dry and wet seasons.

This study describes the design and implementation of an electronic nose, which was applied to classify and identify hazardous gases generated in underground coal mines. For this purpose, an electrochemical sensor array was used to detect a set of toxic gases. This work illustrates the electronic components of a wireless multisensory system for the toxic gases detection on indoor environments (i.e., underground mines), which was performed with reduced size, low cost and low electrical consumption, in order to detect different compounds using the basic principle of operation of each component to be applied to the target gas. Furthermore, the sample collection, data communication and data processing in real time obtained an excellent performance for gas sensing and even to measure the concentration level of the chemical volatile compounds transmitted from different points of the detection zone. The results demonstrated that using a wireless electronic nose for toxic gases detection was possible to reach a success rate of discrimination of 97%, using principal components analysis (PCA) and Linear Discriminant Analysis (LDA).

Dump design and scheduling are critical elements to effective mine planning, especially if several of them are required in large-scale open pit mines. Infrastructure capital and transportation costs are considerable from an early stage in the mining project, and through the life-of-mine as these dumps gradually become immense structures. Delivered mining rates, as well as certain spatial and physical constraints, provide a set of parameters of mathematical and economic relationship that creates opportunities for modelling and thus facilitates the measuring and optimization of ultimate dump design by using programming and empirical techniques while achieving economic objectives. This paper presents a methodology to model and optimize the design of a mine dump by minimizing the total haulage costs. The proposed methodology consists on: (i) Formulation of a dump model based on a system of equations relying on multiple relevant parameters; (ii) Solves by minimizing the total cost using linear programming and determines a ‘preliminary’ dump design; (iii) Through a series of iterations, modifies the ‘preliminary’ footprint by projecting it to the topography and creates the ultimate dump design. Finally, an example application for a waste rock dump illustrates this methodology.

Abstract: Despite existing several techniques for distributed sensing (temperature and strain) using standard Single Mode optical Fiber (SMF), compensating or decoupling both effects is mandatory for many applications. Currently, most of the decoupling techniques require special optical fibers and are difficult to implement with high spatial resolution distributed techniques, such as ϕ-PA-OFDR. So, this work’s objective is to study the feasibility of decoupling temperature and strain out of a ϕ-PA-OFDR readouts taken over an SMF. For this purpose, the readouts will be subjected to a study using several Machine Learning algorithms, among them, Deep Neural Networks. The motivation which underlies this target is the current blockage in the widespread use of Fiber Optic Sensors in situations where both strain and temperature change, due to the coupled dependence of currently developed sensing methods. Instead of using other types of sensors or even other interrogation methods, the objective of this work is to analyze the available information in order to develop a sensing method capable of providing information about strain and temperature simultaneously.

We study in this work a dilute granular gas immersed in a thermal bath made of smaller particles with nonnegligible masses as compared with the granular ones. The kinetic theory for this system is developed and described by an Enskog--Fokker--Planck equation for the one-particle velocity distribution function. Granular particles are assumed to have inelastic and hard interactions, losing energy in collisions as accounted by a constant coefficient of normal restitution. The interaction with the thermal bath is based on a nonlinear drag force plus a white-noise stochastic force. To get explicit results of the temperature aging and steady states, Maxwellian and first Sonine approximations are developed. The latter takes into account the coupling of the excess kurtosis with the temperature. Theoretical predictions are compared with direct simulation Monte Carlo and event-driven molecular dynamics simulations. While good results for the granular temperature are obtained from the Maxwellian approximation, a much better agreement, especially as inelasticity and drag nonlinearity increase, is found when using the first Sonine approximation. The latter approximation is, additionally, crucial to account for memory effects like Mpemba and Kovacs-like ones.

Background: and objectives: Covid-19 is a global pandemic that requires a culturally integrated response in all Latin American government systems. In this study, we investigate the multidimensional associations of actions in Latin American indigenous peoples against Covid-19 Methods: We performed a data mining analysis of scientific literature records from 19 academic journals, using the topics Covid-19, mortality, indigenous peoples, and future measures, rights and actions. The literature is analyzed in a multidimensional way, either isolated or combined. It uses switchable statistical methods, the R Studio, worddj, Gephi, and Iramuteq for both textual and multidimensional analysis. Results and Conclusions: In this analysis, the literature is classified into 4 linked groups which are the indigenous people, the rights of territorial protection, the indigenous health culture, the Covid-19 pandemic with its protection efforts. Taking the indigenous peoples as an axis, we observe that the covid pandemic and the protection of their territories do not have the same force in the current context, the indigenous people are more linked to the protection of their territory. Indigenous peoples have a link of cultural strength in traditional health independent of Covid treatment. It should be noted that government agencies are taking measures regarding the covid pandemic with an effort not marked within the health culture of indigenous peoples.

One challenging problem is the representation of three-dimensional datasets that vary with time. These datasets can be though as a cloud of points that gradually deforms. But point-wise variations lack of information about the overall deformation pattern, and more importantly, about the extreme deformation locations inside the cloud. The present article applies a technique in computational mechanics to derive the strain-rate state of a time-dependent and three-dimensional data distribution, by which one can characterize its main trends of shift. Indeed, the tensorial analysis methodology is able to determine the global deformation rates in the entire dataset. With the use of this technique, one can characterize the significant fluctuations in a reduced multivariate description of an urban system and identify the possible causes of those changes: calculating the strain-rate state of a PCA-based multivariate description of an urban system, we are able to describe the clustering and divergence patterns between the districts of the city and to characterize the temporal rate in which those variations happen.

The Roman economy of the Iberian Peninsula has habitually been characterised in terms of prestige goods and economic activities such as trade, mining and metallurgy. The analysis of plant-based foods –less prestigious but more essential in everyday life– has commonly been marginalised in state-of-the-art reviews. The O Areal saltworks is exceptional in terms of the large number of organic materials it preserves, and the excellent state of that preservation. After its abandonment (end of the 3rd/4th century AD), the saltworks was briefly used as a dumping ground for the surrounding area. The site's archaeobotanical remains, preserved under anoxic, waterlogged conditions, consist of the building materials used at the saltworks, tools and other artefacts, organic objects employed in activities such as fishing, and refuse. The assemblage suggests a wide diversity of species to have been introduced into northwestern Iberia during the Roman Period, including the mulberry, peach, fig, plum, grapevine, and melon. The notable presence of other edible fruit species that normally grew wild during this period, such as chestnut, walnut, stone pine, and cherry trees, might be related to the start of their cultivation.

This work demonstrated the potential of CNC as a substitute for PEG as an additive in ultrafiltration membrane fabrication. Two sets of modified membranes were fabricated using the phase inversion technique with polyethersulfone (PES) as the base polymer, and 1-N-methyl-2 pyrrolidone (NMP) as the solvent. The first set was fabricated with 0.075 wt% CNC, while the second set was fabricated with 2 wt% PEG. All membranes were characterized using SEM, EDX, FTIR, and contact angle measurements. The SEM images were analyzed for surface characteristics using WSxM 5.0 Develop 9.1 software. The membranes were tested, characterized, and compared for their performance in treating both synthetic turbid water and real restaurant Both membranes exhibited improved hydrophilicity, morphology, pore structure, and roughness. Both membranes also exhibited similar water flux for real and synthetic polluted water. However, the membrane prepared with CNC gave the higher turbidity removal and COD removal when raw restaurant water was treated. The membrane compared well with the UF membrane containing 2 wt% PEG in terms of morphology and performance when synthetic turbid water and raw restaurant water were treated.

This paper presents a novel improvement of MuSIASEM (Multiscale Integrated Analysis of the Societal and Ecological Metabolism) by incorporating a new regional analysis of the electricity metabolism across levels. An analysis of Mexico illustrates this toolkit and shows that the industry sector had Economic Energy Intensity (EEI) with 40.3 MWh/MMXN reached a higher value than the commerce and services sector with 0.84 MWh/MMXN. Regarding the Economic Labor Productivity (ELP) indicator (AV/h), the industrial sector with 208.5 TMXN/Kh reached a higher value than the Commercial and services sector with 114.3 TMXN/Kh. With reference to the Exosomatic Metabolic Rate (EMR), the Household sector obtained 59.3 KWh/Kh whereas the paid sector reached 2,486.4 KWh/Kh. Disaggregation of the EMR indicator into economic sectors shows that the Industrial sector obtained 8.4 KWh/Kh and the commercial and services sector reached 0.10 KWh/Kh. The lack of complete data for the agricultural sector does not allow us to calculate EEI, ELP, and EMR indicators accurately. Considering the analysis of indicators by regions, regarding the EEI rate, the Central region had the lowest value with 12.7 MWh/MMXN, followed by the Northeast with 21.4 MWh/MMXN and Western with 29.4 MWh/ MMXN, which means that they require less electricity to produce more added value. In terms of the ELP indicator, it was found regions with higher values were Northeast with 182.3 MMXN/Mh, Central with 177.5 MMXN/Mh, and North with 127 MMXN/Mh, which means that these regions produce more added value per hour of work. About the EMR, it is observed that regions in the northern part of the country had higher values: Northwest with 337 KWh/Kh, Baja California Sur with 334 KWh/Kh, Baja California with 328 KWh/Kh, North with 313 KWh/Kh and Northeast with 274 KWh/Kh, which means that these regions consume more electricity per hour. This innovative approach is useful for governance because it helps to understand and reduce asymmetries across regions, resulting in more social equality and a better economic equilibrium across sectors and regions.

Anthropogenic change is associated with population growth, land use change, and changing economies. However, internal migration patterns and armed conflicts are also key drivers behind anthropogenic and demographic processes. To better understand this sort of change, we explore the spatial relationship between forced migration due to armed conflict and changing demographic factors in Colombia, a country which has a recent history of 7 million internal migrants. In addition, we use remote sensing, Google Earth Engine, as well as spatial statistical analyses of demographic data in order to measure anthropogenic change between 1984 and 2008; and we look into spatiotemporal relationships between both demographic and anthropogenic changes, which are caused by forced migration. We find, thus, that the latter is significantly and positively related to an increasing rural-urban kind of migration which originates in armed conflict, and results show that it is also negatively associated with interregional expulsion. Indeed, anthropogenic prints (term hereafter used to denote changes in nighttime satellite imagery) pertaining to different regions have had different sensitivities towards forced migration, and across different time periods. Finally we discuss how social and political phenomena such as Colombia’s armed conflict can have significant effects on the dynamics and motions of humans and territories in countries of the Global South.

The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles, fortifications, etc.), has few conservation plans, with many remains included on the Red List of Spanish Heritage ly-ing abandoned. This poses a challenge for landscape conservation and the optimal use of the province’s tourism potential. In this paper we present the early implementation of an ongoing methodological approach designed to alleviate this situation. It takes advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS, GIS and fieldwork that will be applied across five archaeological case studies. It has been tested for the first time in the village of Magaña. Three main objectives and phases of this methodological approach have been designed: 1. Preparation of an Environmental Risk Chart with which to answer such ques-tions as “How have climate change, anthropic alterations and environmental characteris-tics affected the state of conservation of certain heritage sites?” 2. Preparation of HBIM models to foster monitorisation and conservation policies for the main archaeological remains and improving their protection based on digital and technological tools. 3. Building a Smart Tourism App for mobile devices aimed at promoting smart tourism by digitalising and virtualising tourist itineraries and archaeological remains. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural assets and their surroundings in a simple, quick and cost-effective man-ner.

The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles and fortifications), has few conservation plans, with many remains included on the Red List of Spanish Heritage lying abandoned. This presents a problem for the conservation of the landscape and the optimal use of the province’s tourism potential. Through the study case of Magaña hamlet two actions are proposed to alleviate this situation: The creation of an archaeological and environmental risk chart with which to answer such questions as “How have climate change, anthropic alterations and environmental charac-teristics affected the state of conservation of certain heritage sites?” and to put forward proposals for improving their protection using as a basis digital and technological tools, such as remote sensing (SAR), taking advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS and GIS. To foster the promotion of smart tourism by digitalising and virtualising tourist routes and archaeological remains by building a Smart Tourism App for mobile devices. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural as-sets and their surroundings in a simple, quick and cost-effective manner.

Several life contingency agreements are based on the assumption that policyholders have impaired life expectancy attributable to factors such as lifestyle, social class, or preexisting health issues. Quantifying two crucial variables, augmented death probabilities and the discount rate of projected cash flows, is essential for pricing such agreements. Information regarding the correct values of these parameters is subject to vagueness and imprecision, which further intensifies if impairments must be considered. This study proposes modelling mortality and interest rates using a generalization of fuzzy numbers (FNs), known as intuitionistic fuzzy numbers (IFNs). Consequently, this paper extends the literature on life contingency pricing with fuzzy parameters, where uncertainty in variables, such as interest rates and death probabilities, is modelled using FNs. While FNs introduce epistemic uncertainty, the use of IFNs adds bipolarity to the analysis by incorporating both positive and negative information regarding actuarial variables. Our analysis focuses on two agreements involving policyholders with impaired life expectancy: determining the annuity payment in a substandard annuity and pricing a life settlement over a whole life insurance policy. In particular, we emphasize modelling interest rates and survival probabilities using triangular intuitionistic fuzzy numbers (TIFNs) owing to their ease of interpretation and implementation.

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

We propose a conceptual model for the closeness parameter $\epsilon$, which characterizes exotic compact objects (ECOs). To estimate $\epsilon$, a thin spherical dust shell is considered, which gravitationally contracts from a specific position $r(t_{0})$ to near its gravitational radius $r(t_{2})=r_{s} + \epsilon$, in a finite time $t_{2}$, measured in the frame of a fiducial observer (FIDO). For an external observer, the shell’s kinematics is characterized by two clearly distinguishable phases: one of rapid contraction, where the shell is far away from the gravitational radius, $r(t_{0})\gg r_{s}$, and a second phase quasi-stationary, $r(t)\sim r_{s}$, where all of the shell’s mass is concentrated around the associated horizon, such that for a FIDO, a black hole (BH)is undistinguishable from a shell configured as a black shell (BS). \\ In the semi-classical approximation $E\ll \kappa_{0}l_{p}^{2}$ and tends to zero when the observation time of collapse $t_{2}$, measured by FIDO, tends to infinity; $\kappa_{0}$ and $l_{p}$ are surface gravity and Planck length, respectively. The quantum effects are significant when $\epsilon\ll r(t_{2})$ and $\epsilon$ tends to $\kappa_{0}l_{p}^{2}$. \\ Without knowing details on quantum gravity, parameter $\epsilon$ is calculated, which, in general, allows distinguishing the ECOs from BHs. Specifically, a BS (ECO) is undistinguishable from a BH.

Tuberculosis (TB) is an infectious disease declared a global emergency by the World Health Organization and continues as one of the world’s top ten causes of death. TB diagnosis is more critical in developing countries where demanded infrastructure for detection, and treatment complicates the efforts against the disease. These aspects related to limited resources are significant, especially in areas away from the main cities, with few mechanisms to make a timely diagnosis that contributes to successfully addressing the possible patients. Artificial intelligence has begun to be essential in providing additional strategies in the diagnosis processes for health professionals’ support. This paper uses natural language processing (NLP) and machine learning (ML) techniques to create models that can supply TB diagnosis when the needed infrastructure is unavailable. Two different sources were explored: text extracted from electronic medical records (EMR) and patient clinical data (CD). Four proposals using five different machine learning models were implemented. The first two models employed ML and each data source independently. Then, two additional approaches developed a data fusion from both sources. This strategy’s employment was analyzed with physicians according to their pertinence in the process and understanding of the EMR. Finally, the results of the data fusion were compared to each source, obtaining better performance at using only the CD, where an area under the ROC curve of 69.9±2.3% was obtained. However, the advantage of analyzing physician’s reports is the availability of this information contrasted to clinical-specific data, which can be more useful in places far from the main cities without enough basic structure for its obtaining.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

The purpose of this article is to present an IoT architecture implemented in an intelligent environment, focusing on the optimization of initial conditions and device management. A general control system was developed using an open-source platform, integrating aWi-Fi network and YAML coding to enable communication and control of the IoT architecture devices. The implementation includes actuators, sensors, and control devices that, through a graphical interface, allow the visualization of the historical record of activities and real-time measurements of connected devices. The design includes specific mechanisms and the analysis of materials through tensile tests on specimens, following the ASTM E8 standard, and deformation test simulations, in which PLA demonstrated better performance compared to materials like ABS and resin. The significance of the implemented IoT system lies in an energy saving of 58% compared to conventional structures. Additionally, the study of response times for network connections showed that using an Ethernet cable resulted in a response time of 1.21 seconds, representing a reduction of 1.65 seconds compared to the second lowest value obtained.

Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

Biosensors are analytical devices that utilize biological interactions to detect and quantify clinical biomarkers, contaminants, allergens, and microorganisms. They combine different disciplines including analytical chemistry, molecular biology, and electrical engineering. Biosensors operate by coupling a bioreceptor, such as nucleic acid or proteins, with a transducer that converts the biological interaction into an electrical signal. Electrochemical and optical transduction are commonly used approaches due to their high detection capability and compatibility with miniaturization. Biosensors provide both high specificity and sensitivity and can be integrated into low-cost microfluidic platforms for rapid and point-of-care applications. These attributes make these devices valuable tools in analytical chemistry, particularly for early diagnostic applications. However, conventional biosensors face challenges related to the immobilization of biorecognition elements on the transducer surface, leading to issues like lost of sensitivity and selectivity. To address these problems, the introduction of nanomaterials, particularly magnetic nanoparticles (MNPs) and magnetic beads (MBs), has been implemented. MNPs combine their magnetic properties with other interesting characteristics such as small size, high surface-to-volume ratio, and excellent biocompatibility. They can be tailored for specific applications and have been extensively used in various fields, including biosensing and clinical diagnosis. Furthermore, MNPs simplify sample preparation by isolating target analytes through magnetic separation, thus improving sensitivity, and reducing analysis time and interference phenomena. The synthesis and modification of MNPs play a crucial role in adjusting their properties for different applications. This review presents an overview of the synthesis and surface modifications of magnetic nanoparticles, their role in the development of biosensors and bioassays, and their applications across various scientific areas.

The adoption of ADS-B in Latin America has been gradual but steady. Some countries, such as Brazil, Chile and Mexico, have made significant progress in the implementation of this system, while others are still in the early stages. It should be noted that the implementation of ADS-B is used in both military and civilian environments. For this reason, this research aims to provide information on the different characteristics that make up the surveillance systems. However, there are fundamental factors to promote the adaptation of ADS-B in Latin America, since they reduce the risk of collisions and air accidents by providing accurate and real-time information on the position and altitude of aircraft. On the other hand, it allows optimizing flight routes and reducing waiting time on the ground, which translates into fuel and cost savings for airlines. ADS-B increases the number of aircraft that can safely operate in the same airspace, which is especially important in regions with growing air traffic. ADS-B adoption in Latin America is expected to continue to grow in the coming years. The drivers for ADS-B adoption remain in place, and governments in the region are taking steps to accelerate its implementation.

: One of the challenges facing operations management is the design, choice and imple-mentation of action plans adapted to the magnitude of the deviations of the variables of an industrial process. Making conscious and quick decisions is crucial to achieving im-provements in productivity. This will be achieved if the organization's internal and external communication model is strategically designed, considering specific cultural factors and the symmetry or asymmetry required in the communication model. However, how do we organize ourselves and through what channels do we communicate within a production process to generate Big Data that combines data from technologies and the perception, comprehension and projection of experienced humans? Our purpose is to create an operations management model that facilitates the incorporation of technology, allows a prompt response to failures, and promotes the sustainability of processes, without losing sight of its alignment with the organizational strategy. The developed model was implemented in an operations management team, following the guidelines of an action researcher methodology. This resulted in the design of an operations management model and a detailed methodology for its implementation, achieving sig-nificant improvements in the metrics of the current process, making it a success story.

(1) Background: Neglected tropical diseases (NTDs) have been overlooked on the global health agenda and in the priorities of national systems in low- and middle-income countries (LMICs). In 2012, the Sustainable Development Goals (SDGs) were created to ensure healthy lives and promoting well-being for all. This roadmap set out to accelerate work to overcome the global impact of NTDs. Almost a decade has passed since NTDs were re-launched as a global priority. Investment in research and development, as well as the production of scientific literature on NTDs, is expected to have increased significantly. (2) Methods: A bibliometric analysis of the scientific production of Latin America and the Caribbean (LAC) was carried out in relation to 19 endemic NTDs. These data were compared with the scientific production in malaria, tuberculosis and HIV / AIDS. The database available from Thomson Reuters Web of Science (WoS) was used. In addition, the average annual growth percentage was calculated for each disease. (3) Results: In the last decade, the NTDs with the highest number of publications in the world were dengue and leishmaniasis. The United States was the most prolific country in the world in 15 out of 19 NTDs analyzed. In the LAC region, Brazil was the largest contributor for 16 of the 19 NTDs analyzed. Arboviral diseases showed the highest average annual growth. The number of publications for malaria, tuberculosis and HIV /AIDS was considerably higher than for NTDs. The contribution of most LAC countries, especially those considered as LMICs, is inadequate and does not reflect the relevance of NTDs for the public health of the population. (4) Conclusion: This is the first bibliometric analysis to assess the trend of scientific documents on endemic NTDs in LAC. Our results could be used by decision makers both to strengthen investment policies in research and development in NTDs.

The burdens of malnutrition, protein and micronutrient deficiency, and obesity cause enormous costs to society. Crop nutritional quality has been compromised by the emphasis on edible yield and through the loss of biodiversity due to the introduction of high-yielding uniform cultivars. Heirloom crop cultivars are traditional cultivars grown for a long time (> 50 years), and whose heritage has been preserved by regional, ethnic or family groups. Heirlooms are recognized for their unique appearance, names, uses and historical significance. They are gaining in popularity because of their unique flavors and cultural significance to local cuisine, and their role in sustainable food production for small-scale farmers. As a contrast to modern cultivars, heirlooms may offer a welcome alternative in certain markets. Recently, market channels have emerged for heirloom cultivars in the form of farmer-breeder-chef collaborations and seed savers organizations. There is therefore urgent need to know more about the traits available in heirloom cultivars, particularly for productivity, stress tolerance, proximate composition, sensory quality and flavor. This information is scattered and the intention of this review is to document some of the unique characteristics of heirloom cultivars that may be channeled into breeding programs for developing locally adapted high value cultivars.

This paper introduces the spatial component in cross-section econometric estimations and specifically, the spatial dependence effect inherent in some of the variables involved in the modelling process. First, the spatial structure of the data from thematic maps is observed and Moran's spatial autocorrelation indicators are presented. Subsequently, the spatial weights matrix is built under different specifications. Finally, several modelling specification strategies are shown and the interpretation of the estimated coefficients. The theoretical concepts are illustrated with examples and their corresponding R software codes. This code and databases are available in a freely accessible repository in the BE2SHARE-EUDAT platform so that they can be easily reproduced.

Finding the proper entropy functional associated with the inelastic Boltzmann equation for a granular gas is a yet unsolved challenge. The original H-theorem hypotheses do not fit here and the H-functional presents some additional measure problems that are solved by the Kullback–Leibler divergence (KLD) of a reference velocity distribution function from the actual distribution. The right choice of the reference distribution in the KLD is crucial for the latter to qualify or not as a Lyapunov functional, the “homogeneous cooling state” (HCS) distribution of the freely cooling system being a potential candidate. Due to the lack of a formal proof, the aim of this work is to support this conjecture aided by molecular dynamics simulations of inelastic hard disks and spheres in a wide range of values for the coefficient of restitution (α). Our results reject the Maxwellian distribution as a possible reference, whereas reinforce the HCS one. Moreover, the KLD is used to measure the amount of information lost on using the former rather than the latter, and reveals a nonmonotonic dependence with α. Additionally, a Maxwell-demon-like velocity-inversion experiment highlights the microscopic irreversibility of the granular gas dynamics.

Being able to forecast events is of great importance in many fields, from brain behavior to earthquakes or stock markets. Because each dynamical system has intrinsic features, different statistical tools have to be used for each system. Here we study the time series of the output intensity of a fiber laser with an ordinal patterns analysis, and we look for temporal correlations in order to statistically forecast the most intense events. We set two thresholds, a low one and a high one, to distinguish between low intensity versus high intensity events. We find that when the time series is performing events below the low threshold it shows some preferred temporal patterns before performing events above a high threshold.

The purpose of this systematic review is to examine the various articles published in the field of neuropsychology of anxiety research in terms of publication type and volume, methodology, and subject matter. The significance of this topic derives from the clinical and functional consequences of cognitive symptoms. Generalized anxiety disorder refers to the cluster of clinical symptoms characterized by predominant anxious anticipation and difficulty controlling worry. In order for the review to be systematic and reproducible, a search of the PsycINFO, Scopus, PubMed, Elsevier databases and an analysis using the PRISMA method are suggested. After analyzing the research data, empirical studies evaluating the clinical neuropsychology of generalized anxiety disorder are discussed.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Although prognosis continually improved along years, a significant proportion of patients still relapse from the disease due to leukemia resistance to therapy. Methotrexate (MTX), a folic acid antagonist, is a chemotherapy agent commonly used against ALL and a immune-system suppressant for rheumatoid arthritis, that presents multiple and complex mechanisms of action and resistance. Previous studies have shown that MTX modulates the nuclear factor kappa B (NF-κB) pathway, an important family of transcription factors involved in inflammation, immunity, cell survival, and proliferation, frequently hyperactivated in ALL. Using gene set enrichment analysis (GSEA) on publicly available gene expression data from 161 newly diagnosed pediatric ALL patients, we found “TNF-α signaling pathway” to be the most enriched Cancer Hallmark in MTX poor responder patients. Transcriptomic analysis in a panel of ALL cell lines (6 BCP-ALL and 7 T-ALL) also identified the same pathway as differentially enriched among MTX resistant cell lines, as well as in slowly dividing cells. To better understand the crosstalk between NF-κB activity and MTX resistance, we genetically modified the cell lines to express luciferase under a NF-κB biding site promoter. We observed that the fold change in NF-κB activity triggered by TNF-α (but not MTX) treatment correlated with MTX resistance and proliferation across the lines. At the individual gene level, NFKB1 expression was directly associated with a poorer clinical response to MTX, and with both an increased TNF-α-triggered NF-κB activation and MTX resistance in the cell lines. Despite these results, pharmacological inhibition (with BAY 11-7082 and parthenolide) or stimulation (with exogenous TNF-α supplementation) of the NF-κB pathway did not alter MTX resistance of the cell lines significantly, evidencing a complex interplay between MTX and NF-κB in ALL.

This review article seeks to systematically identify appropriate ways to measure managerial skills. The systematic literature review was conducted according to the criteria in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide, and the eligibility criteria were declared through the PICOS tool (population, interventions, comparators, outcomes and study); a tool based on 6,803 records identified bibliometrically in the Web of Science database. The literature review stages determined a reduced set of articles that presented valid and reliable measurement scales covering some determinant constructs on managerial skills for employability such as fundamental, emotional, managerial, leadership, decision-making, communication and teamwork skills.

Speech signals are degraded in real-life environments, product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions.To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long and short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combination of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation has been made based on quality measurements of the signal's spectrum, training time of the networks and statistical validation of results. Results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, with advantages in efficiency, but without a significan drop in quality.

This paper shows the firsts results of a methodological approach in which Remote Sensing technologies (Satellite imagery and LiDAR) combined with Least Cost Path algorithm on GIS are being tested like tools to overcome locally-based interpretations about populational patterns and communication routes during Flavian municipality in the Comarca de El Condado region. The identification of fourteen hypothetical communication routes between already known archaeological sites thanks to a recent archaeological prospection, and the confirmation of three sections of them and a new archaeological site demonstrate that the combined use of the aforementioned technics has a very useful to delve more deeply, more widely and at a variety of different scales into the relation between the twelve known/selected Early Roman Empire sites and the surrounding landscape in the right bank of Guadalén River. It is expected that, in next phases, this methodological approach will allow us to go deeper in analysing and understanding spatial distribution, exploitation, and interrelation between different Early Roman Empire sites, helping us to fill the aforementioned gaps in our understanding of the archaeological continuum in the region reducing time and costs.

This research article presents the integration of participatory modeling and system dynamics as a novel methodology for the consolidation of social dynamic models for the subsequent evaluation and prioritization of green projects in Colombian post-conflict communities. In the first instance, through participatory work carried out along with the community, it was possible to identify, evaluate and systematize citizen factors in relation to the problems and needs of the region. Second, based on the results obtained, to calibrate a simulation model based on system dynamics that facilitates decision making with regard to the evaluation of green projects. The proposed methodology leads to the conclusion that, with the participation of the community and with a model based on the dynamics of variables such as supply and demand for natural resources of water and land, it is possible to warn decision makers about the variables that can lead to the maximization of investments and thus prioritize and select the most appropriate environmental, social or economic initiatives, that certainly meet the needs or expectations of the involved community. In the future, the model could be used to facilitate the management, administration and control of water and land resources by creating alerts called reserve margins.

The aim of this article was to assess how the professional competence of PETs (Physical Education Teachers) can predict student satisfaction in high school and student satisfaction with life itself. In line with these aims, this study was completed as a cross-sectional study, which was carried out in a total 890 Physical Education (PE) students. Of the research group, 50.3% were female and 49.7% were male. Age average was 15.49 years old for females (SD 1.79) and 15.00 years old for males (SD 2.00). The data collection instrument was the Intrinsic Satisfaction Classroom Scale and Satisfaction with Life Scale. The results are presented as descriptive statistics, correlations and structural equation modelling analysis, and they show that the competences of the PE teacher determine in great measure student’s satisfaction with school and with their own personal lives.

The purpose of this study is to bring out a scientometric analysis of the advances of digital transformation in the global banking sector. For this, a background search of the term "Digital Transformation Banking " has been brought out from the Web of Science (Wos) Database, covering records published between 2002 and 2023. A set of 312 publications was identified, which mainly includes a set of 264 academic articles, 29 articles published without cataloging a specific volume of a journal, 9 articles under review, 5 editorial material documents, 4 conference articles, and 1 document is a book chapter. Nowadays the scientific production is low but showing growing development presenting 123 publications and 7,754 citations for the year 2022. Among the scientific production, 43 publications from the Republic of China (8.8%) and 35 publications from Ukraine (7.8%) stand out. ,2%). It is also worth mentioning that the most relevant authors on this topic are Ricardo Palomo, professor and dean of San Pablo University, and Robert J Kauffman, professor at Singapore Management University in terms of influence. Main institutions in terms of scientific production, mainly: the Massachusetts Institute of Technology (MIT) of the United States and the Russian Academy of Sciences of Russia.

Autophagy is an essential mechanism to maintain cellular homeostasis. Besides its role in controlling the quality of cytoplasmic components, it participates in nutrient obtaining and lipid mobilization under stressful conditions. Furthermore, autophagy is involved in the regulation of systemic metabolic, a function mainly performed by neuronal populations of the arcuate nucleus of the hypothalamus. Several studies have shown that blockade of autophagy in these neurons can affect central regulation of metabolism and impact body energy balance. Moreover, hypothalamic autophagy can be altered during obesity. However, neurons are not the only cell type involved in the central regulation of metabolism. Astrocytes, essential cells for brain homeostasis, are key metabolic regulators. They can sense metabolic signals in the hypothalamus and modulate systemic functions as glucose homeostasis and feeding response. Moreover, the response of astrocytes to obesity has been widely studied. Astrocytes are important mediators of brain inflammation and can be affected by increased levels of saturated fatty acids associated to obesity. Although autophagy plays important roles for astrocyte homeostasis and functioning, the contribution of astrocyte autophagy to systemic metabolism has not been analysed. Furthermore, how obesity can impact astrocyte autophagy is poorly understood. More studies are needed in other to understand the contribution of astrocyte autophagy to metabolism.

SARS-CoV-2 infection can lead to life-threatening clinical manifestations. Patients with cardiovascular disease (CVD) are at higher risk for severe courses of COVID-19. However, strategies to predict the course of SARS-CoV-2 infection in CVD patients at the time of hospital admission are still missing. Here, we investigated whether this prediction is achievable by prospectively analysing the blood immunophenotype of 94 participants, including 20 uninfected and 37 acutely SARS-CoV-2-infected CVD patients and 37 healthy donors, using a 36-colour spectral flow cytometry panel. Unsupervised data analysis revealed little differences between healthy donors and CVD patients, whereas the distribution of the cell populations changed dramatically in SARS-CoV-2-infected CVD patients. The latter had more mature NK cells, activated monocyte subsets, central memory CD4+ T cells, and plasmablasts but fewer dendritic cells, CD16+ monocytes, innate lymphoid cells, and CD8+ T cell subsets. Moreover, we identified an immune signature characterised by mucosal-associated invariant T (MAIT) cells, intermediate effector CD8+ T cells, and natural killer T (NKT) cells that is predictive for CVD patients with a severe course of COVID-19. Thus, intensified immunophenotype analyses can help identify patients at risk of severe COVID-19 at hospital admission, improving clinical outcomes through specific treatment.

New proposals for the equation of state of four- and five-dimensional hard-hypersphere mixtures in terms of the equation of state of the corresponding monocomponent hard-hypersphere fluid are introduced. Such proposals (which are constructed in such a way so as to yield the exact third virial coefficient) extend, on the one hand, recent similar formulations for hard-disk and (three-dimensional) hard-sphere mixtures and, on the other hand, two of our previous proposals also linking the mixture equation of state and the one of the monocomponent fluid but unable to reproduce the exact third virial coefficient. The old and new proposals are tested by comparison with published molecular dynamics and Monte Carlo simulation results and their relative merit is evaluated.

Coronaviruses are an extensive family of viruses that can cause disease in both animals and humans. The current classification of coronaviruses recognizes 39 species in 27 subgenera that belong to the family Coronaviridae. From those, at least seven coronaviruses are known to cause respiratory infections in humans. Four of these viruses can cause common cold-like symptoms, while others that infect animals can evolve and become infectious to humans. Three recent examples of this viral jumps include SARS CoV, MERS-CoV and SARS CoV-2 virus. They are responsible for causing severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and the most recently discovered coronavirus disease during 2019 (COVID-19).COVID-19, a respiratory disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organization (WHO) on 11 March 2020. The rapid spread of the disease has taken the scientific and medical community by surprise. Latest figures from 14 April 2020 show more than 2 million people had been infected with the virus, causing more than 120,000 deaths in over 210 countries worldwide. The large amount of information we receive every day concerning this new disease is so abundant and dynamic that medical staff, health authorities, academics and the media are not able to keep up with this new pandemic. In order to offer a clear insight of the extensive literature available, we have conducted a comprehensive literature review of the SARS CoV-2 Virus and the Coronavirus Diseases 2019 (COVID-19).

We analyze the transformation of a very broad class of metrics that can be put in terms of static coordinates. Starting from a general ansatz, we obtain a relation for the parameters in which one can impose further symmetries, or restrictions. One of the simplest restrictions leads to FLRW cases, while transforming from the initial static to other static-type coordinates can lead to near horizon coordinates, Wheeler-Regge, isotropic coordinates, among others. As less restrictive cases, we show an indirect route for obtaining Kruskal-Szekeres within this approach, as well as Lema\^{\i}tre coordinates. We use Schwarzschild spacetime as prototype for testing the procedure in individual cases. However, application to other spacetimes, such as de-Sitter, Reissner-Nordstr\"{o}m or Schwarzschild de Sitter, can be readily generalized.

Symmetry-breaking is a phenomenon that is observed in various contexts, from the early universe to complex organisms, and is considered a key puzzle in understanding the emergence of life. The presence of enantiomeric amino acids and proteins highlights its critical role. However, the origin of symmetry-breaking has yet to be comprehensively explained, particularly from an energetic standpoint. This article explores a novel approach by considering energy dissipation, specifically lost free energy, as a crucial factor in elucidating symmetry-breaking. Through a thorough thermodynamic analysis applicable to enantiomeric systems at various scales, from elementary particles to aggregated matter like crystals, we present experimental findings that establish a direct correlation between non-equilibrium free energy and energy dissipation during formation processes. Results emphasize the pivotal role of energy dissipation not only as an outcome but as the trigger for symmetry-breaking. This insight suggests that understanding the origins of complex systems, from cells to living beings and the universe itself, requires a lens focused on non-equilibrium processes, ultimately facilitating the formation of non-racemic systems.

Baeyer-Villiger monoxygenases (BVMOs) are flavin-dependant oxidative enzymes capable to catalyse the insertion of an oxygen atom between a carbonylic Csp2 and the Csp3 at the alpha position, therefore transforming linear and cyclic ketones into esters and lactones. These enzymes are dependent on nicotinamides (NAD(P)H) for the flavin reduction and subsequent reaction with molecular oxygen to furnish peroxyflavin, the ultimate responsible for the substrate oxidation. BVMOs can be included in cascade reactions, coupled to other redox enzymes such as alcohol dehydrogenases (ADHs) or ene-reductases (EREDs), so that the direct conversion of alcohols or α,β-unsaturated carbonylic compounds to the corresponding esters can be achieved. This way, it is possible to develop smart synthetic strategies with a convenient cofactor recycling, both using whole cells (native or genetically engineered) as well as isolated enzymes, via multi-steps reaction through sequential or parallel methodologies. Some examples will be commented dealing with these biotransformations, highlighting the advantages of the coupling of enzymatic steps.

Application of protocols without parameter standardization and rigorous controls has led manual therapy (MT) and other physiotherapy approaches to controversial outcomes. Thus, there is an urgency to carefully define standard protocols that elevate physiotherapy treatments to rigorous scientific demands. One way this can be achieved is by studying gene expression and additional physiological changes that associate to particular, parameter-controlled, treatments in animal models and translating this knowledge to properly design objective, quantitatively-monitored clinical trials. Here, we propose a Molecular Physiotherapy Approach (MPTA), requiring multidisciplinary teams, to uncover the scientific reasons behind the numerous reports of MT that historically attribute benefits to these treatments. The review focuses in the identification of MT-induced physiological and molecular responses that could be used for the treatment of fibromyalgia (FM) and CFS/ME. The systemic effect associated to mechanical-load responses is considered of particular relevance as it suggests that defined, low-pain areas could be selected for treatments with overall benefits, an aspect that might result essential to treat FM. Additionally, MT can provide muscle conditioning to sedentary patients without demanding strenuous physical effort, detrimental for CFS/ME patients, placing MT as a real option for integrative medicine programs to treat FM and CFS/ME.

Brain-computer interfaces (BCI) from electroencephalography (EEG) provide a practical approach to support human-technology interaction. In particular, motor imagery (MI) is a widely-used BCI paradigm that guides the mental trial of motor tasks without physical movement. Here, we present a deep learning methodology, named Kernel-based Regularized EEGNet (KREEGNet), leveled on Centered Kernel Alignment and Gaussian Functional Connectivity, explicitly designed for EEG-based MI classification. The approach proactively tackles the challenge of intra-subject variability brought on by noisy EEG records and the lack of spatial interpretability within end-to-end frameworks applied for MI classification. KREEGNet is a refinement of the widely accepted EEGNet architecture, featuring an additional kernel-based layer for regularized Gaussian functional connectivity estimation based on CKA. The superiority of KREEGNet is evidenced by our experimental results from binary and multi-class MI classification databases, outperforming the baseline EEGNet and other state-of-the-art methods. Further exploration of our model’s interpretability is conducted at individual and group levels, utilizing classification performance measures and pruned functional connectivities. Our approach is a suitable alternative for interpretable end-to-end EEG-BCI based on deep learning.

The nuclear receptor superfamily comprises a large group of proteins with functions essential for cell signaling, survival and proliferation. There are multiple distinctions between nuclear superfamily classes defined by hallmark differences in function, ligand binding, tissue specificity, and DNA binding. In this review, we utilize the initial classification system, which defines subfamilies based on structure and functional difference. The defining feature of the nuclear receptor superfamily is that these proteins function as transcription factors. The loss of transcriptional regulation or gain of functioning of these receptors is a hallmark in numerous diseases. For example, in prostate cancer the androgen receptor is a primary target for current prostate cancer therapies. Targeted cancer therapies for nuclear hormone receptors have been more feasible than others to develop due to ligand availability and cell permeability of hormones. To better target these receptors, it is critical to understand their structural and functional regulation. Given that late-stage cancers often develop hormone insensitivity, we will explore the strengths and pitfalls of targeting other transcription factors outside of the nuclear receptor superfamily such as the signal transducer and activator of transcription (STAT).

This paper delves into the strategic dimensions influencing the adoption of environmental innovations (EIs) in small and medium-sized manufacturing enterprises (SMEs) in Mexico City. Employing a mixed-methods approach combining documentary analysis and expert interviews, the study aims to provide a comprehensive understanding of integrating EI practices in the context of manufacturing SMEs. Qualitative methodology was chosen to explore experiences, perceptions, and practices related to EI adoption, acknowledging its ability to capture the complexity and richness of data. Through systematic literature review and semi-structured interviews with Corporate Social Responsibility experts, causal relationships and key factors influencing EI adoption were identified. The data analysis involved constructing causal maps and applying the Viable System Model to comprehend the interactions and dynamics within the organizational systems.

. Background/Objectives: Crohn's disease is a chronic and debilitating intestinal disorder that alternates between remission and active flare-ups, often leading to hospitalization. Social support is known to enhance adaptation to the disease and modulate stress perception in patients, while stress may exacerbate symptoms. The aim of this study was to examine the roles of perceived stress and social support in Crohn's disease and their impact on the frequency of flare-ups. Methods: A cross-sectional observational study was conducted, assessing stress and social support in a cohort of 91 patients with Crohn's disease during flare-up and remission phases. The Perceived Stress Scale (PSS-14) and a Social Support Questionnaire were utilized for evaluation. We examined the relationship between stress and social support in Crohn's disease. The interaction between the variables studied was also studied, considering the stage of the disease. Finally, we realized analysis of the influence of these two variables on the development of flares in Crohn's disease. Results: The study revealed that patients experience higher stress levels during flare-ups and that these levels are amplified by a lack of social support. A significant relationship was identified between the levels of social support and the occurrence of flare-ups, indicating that better social support is associated with fewer flare-ups.. Conclusions: Patients with Crohn's disease in the flare-up phase are subject to considerable stress. Deficit in social support is linked to an increase in stress levels. The interplay of social support and stress plays a critical role in the development of flare-ups.

In recent decades, particularly intense changes have occurred in Spanish urban areas. This is the result of demographic and urbanizing transformations that have led to a change in the city model. The predominance of compact forms has been replaced by the growing prominence of urban sprawl. Structures are now more extensive, characterized, in turn, by fragmentation and notable consumption of land in the peripheries. In medium-sized cities, the municipalities bordering the central cities have concentrated the processes of suburbanization and periurbanization. This paper addresses the process of change for 34 cities and their urban areas located in inland Spain. A proposal is made for the delimitation and characterization of urban areas, taking the municipality as the unit of analysis. At the methodological level, six variables are used, analyzed by means of multicriteria statistical techniques combined with the use of GIS tools. An Urban Transformation Index (ITU) has been developed that synthesizes urbanizing, demographic and socioeconomic dynamics. In the territorial area, the 20-minute isochrone is used as a reference. In the temporal domain, the period of analysis addresses the events of the first two decades of the twenty-first century.

The administration of regional neuraxial analgesia for pain relief during labor is widely recognized as a safe and effective method involving medication delivery into the epidural or subarachnoid space in the lower back. This study proposes an innovative semantic image segmentation methodology emphasizing enhanced interpretability using convolutional Random Fourier Features and layer-wise weighted class-activation maps tailored explicitly for foot segmentation in regional analgesia monitoring. Namely, our contribution is twofold: i) a novel Random Fourier Features layer is introduced to deal with image data to enhance three well-known architectures (FCN, UNet, and ResUNet); ii) three novel quantitive measures are presented to evaluate the interpretability of a given deep learning model devoted to segmentation tasks. Our approach is rigorously evaluated on a demanding dataset of foot thermal images from pregnant women who received epidural anesthesia. Its small size and considerable variability characterize the dataset. Our validation results demonstrate that the proposed methodology not only achieves competitive foot segmentation performance but also significantly enhances the explainability of the process, rendering it well-suited for applications such as epidural insertion during labor.

Abstract: Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability in the farmer's economy. In this study we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using remotely sensed spectral vegetation indices (VI). A total of 10 VI (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. In the present study, highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA indicated a clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimate the performance, showing greater precision at 51 DAS. The use of RPAS to monitor crops allows optimizing resources and helps in making timely decisions in agriculture in Peru.

In this paper, we introduce the application of Type-I fuzzy inference systems (FIS) as an alternative to improve the prioritization in the FMECA analysis applied in cyber-power grids. Classical FMECA assesses the risk level through the Risk Priority Number (RPN). The multiplication between three integer numbers computes this, called risk factors, representing the severity, occurrence, and detectability of each failure mode and are defined by a team of experts. The RPN does not consider any relative importance between the risk factors and may not necessarily represent the real risk perception of the FMECA team members, usually expressed by natural language; this is the main FMECA shortcoming criticized in the literature. Our approach considers fuzzy variables defined by FMECA experts to represent the uncertainty associated with the human language and a rule base consisting of 125 fuzzy rules to represent the risk perception of the experts. To test our approach, we select a cyber-power grid previously analyzed by the authors using the classical FMECA. The results reveal our proposed fuzzy approach as promissory to represent the uncertainty associated with expert knowledge and to perform an accurate prioritization of failure modes in the context of electrical power systems.

Development of non-conventional hybrids (NCHs) responds to the demand for the Elotes Occidentales 'EO' race for production of pozole. The effect of: crop cycle (2019, 2020 and 2021) on physical characteristics, flowered grain quality and phytochemical content of two HNC (Pz1 and Pz2) of pozolero maize, as well as the effect of the presence or absence of pedicel, type of pollination (open and controlled, 2019), and progenitor lines (female and male, 2020) on flowered grain quality and content of phytochemical compounds were evaluated. Size, hardness, color, total phenols and anthocyanins in unprocessed grain were determined, and yield, volume and puncture strength in flowered grain. Results were analyzed with a factorial arrangement in a completely randomized design. There were significant differences (p≤0.05) in most of the variables studied by effect of crop cycle and genotype. NCHs had large grains (40 g 100 grains-1), soft endosperm (IF&gt; 60), pink-purple color, and phenol and anthocyanin contents similar to those reported for the 'EO' race. The presence or absence of the pedicel did not affect flowered grain quality. Controlled pollination favored anthocyanin synthesis. The anthocyanin content of NCHs was determined by the female line. Thermal processing reduced anthocyanins by 60%; however, they leached into the flowering broth, so that the pozole made from NCHs improves the nutraceutical value of the pozole, relative to that of pozole made with Cacahuacintle maize.

A whole-exome capture and next-generation sequencing applied to an 11 y/o patient with a clinical history of congenital hypotonia, generalized motor and cognitive neurodevelopmental delay, severe cognitive deficit, without any identifiable Syndromic pattern, and to her parents, disclosed a de novo heterozygous pathogenic mutation, c.697_699del p.Phe233del (rs786204835)(ACMG classification PS2, PM1, PM2, PP5), harbored in the PURA gene (MIM*600473) (5q31.3), associated to Autosomal Dominant Mental Retardation 31 (MIM # 616158). We used the significant improvement in the accuracy of protein structure prediction recently implemented in AlphaFold that incorporates novel neural network architectures and training procedures based on the evolutionary, physical, and geometric constraints of protein structures. The wild-type (WT) sequence and the mutated one, missing the Phe233, were reconstructed. The predicted local Distance Difference Test (lDDT) for the PURA WT and the PURA-Phe233del showed that the occurrence of the Phe233del affects between 220-320 amino acids. The distortion in the PURA structural confor-mation in the ~5Å surrounding area after the p.Phe233del produces a conspicuous disruption of the repeat III, where the DNA and RNA helix unwinding capability occurs. PURA Protein-DNA Docking corroborated these results in silico Analysis that showed a loss of the contact of the PURA-Phe233del III repeat domain model with the DNA. Together, i) the energetic and stereochemical, ii) the hydropathic indices and polarity surfaces, and iii) the hybrid Quantum Mechanics-Molecular Mechanics (QM-MM) analyses of the PURA molecular models demarcate at the atomic resolution the specific surrounding region affected by these mutations and paves the way for future cell-based functional analysis. To the best of our knowledge, this is the first report of a de novo mutation underpinning a PURA syndrome in a Latin American patient and highlights the importance of predicting the molecular effects in protein structure using artificial intelligence algorithms and molecular and atomic resolution stereochemical analyses.

Electrical rotating machines like Induction Motors (IMs) are widely used in several industrial applications since their robust elements, provide high efficiency and give versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions, hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs and its appearance is due to electrical stresses leads to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology for the assessment and detection of incipient ITSC in IMs, the proposed method is based on the processing of vibration, stator currents and magnetic stray-flux signals. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as, their fusion and reduction through the Linear discriminant Analysis technique within a feature-level fusion approach. Furthermore, the fusion and reduction of information from different physical magnitudes leads to perform the automatic fault detection and identification by a simple Neural-Network (NN) structure. The proposed method is evaluated under a complete set of experimental data and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) allows to improve the accuracy during the detection of ITSC in IMs , the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

This study investigates the intricate relationship between Venezuelan migration and urban growth in Colombia from 2018 to 2021. The study employs remote sensing data and social network metrics to uncover migration patterns and their impact on urban expansion. The methodology consists of three stages. Firstly, nighttime satellite imagery is used to analyze year-over-year urban growth in Colombia. Secondly, social network data estimates Venezuelan migration, overcoming challenges of underreporting and informal border crossings. Lastly, an econometric analysis explores the quantitative link between Venezuelan migration and urban growth, integrating socioeconomic variables to address endogeneity. The findings reveal the complex interplay of Venezuelan migration, socioeconomic factors, and urban growth. The study outlines remote sensing analysis, introducing the Anthropogenic Footprint Expansion Index (AFEI) to quantify urban growth. Facebook API data estimates migration trends and explores socioeconomic impacts on urban expansion. The analysis uncovers migration, poverty, aging, and urban population proportion as key factors affecting Colombia's urban landscape. Furthermore, the research underscores how Venezuelan migration affected short-term urban expansion pre- and post-COVID-19. Migration had a notable effect before the pandemic, but this influence waned afterward. The study highlights migration's short-term nature and emphasizes age demographics' role in medium-term dynamics.

There is a disproportionate increase in the incidence of diet-related cardiometabolic disorders in racial and ethnic minority groups. This systematic review examines the extent to which diet-induced changes in health outcomes have been discussed by race or ethnicity in randomized controlled trials recruiting both minority and non-Hispanic White groups. Databases i.e. PubMed, Cochrane library and Web of Science were searched up to November 2019. Studies that discussed effects of defined dietary interventions on health outcomes by racial or ethnic minority group vs. non-Hispanic Whites (n=29) were included in the review. Most studies were conducted in Black vs. White people testing effects of energy restriction, macronutrient modification, sodium reduction, or variations of the Dietary Approaches to Stop Hypertension (DASH) diet on cardiometabolic outcomes. There was limited focus on other minority groups. Evidence suggests greater blood pressure reduction for Black people compared to Whites particularly on DASH (or similar) diets. Overall, there was limited consideration for group-specific eating patterns and diet acceptability in most studies. Adequately powered studies are needed for accurate interpretation of race by diet effects. With emerging precision nutrition initiatives, it is imperative to ensure adequate representation of racial and ethnic subgroups for addressing nutrition-related health disparities.

Introduction: Individuals with gout are at higher risk of developing chronic conditions, such as diabetes, chronic kidney disease (CKD), and cardiovascular diseases. In this study, the association between urate-lowering therapy (ULT) use and the prevalence of these conditions was evaluated. Methods: This observational, cross-sectional pharmacoepidemiologic study used the 2013-2018 biannual cycles of the National Health and Nutrition Examination Survey. The inclusion criteria were adults 30 years of age and older with a diagnosis of gout. The association between ULT treatment status and dyslipidemia, coronary heart disease, heart failure, hypertension, and chronic kidney disease was evaluated, as well as associations with select clinical laboratory biomarkers. Results: Use of ULT was 28.9% (95%CI 24.3%-33.9%). There was no significant association between ULT use and the prevalence of heart failure, coronary heart disease, hypertension, or dyslipidemia (p>0.05). Those on ULT had a lower mean eGFR compared to those not on treatment (68.03 versus 74.74 mL/min/1.73m², p=0.014). LDL- and HDL- and total cholesterol were significantly lower among those receiving ULT treatment (p<0.05). Conclusion: ULT use continues to be low among those with gout. Those on ULT were more likely to be diagnosed with CKD, college graduates and above, older males, and obese. Clinical guidelines conditionally recommend ULTs in co-morbid kidney disease; while our results may reflect guidelines recommendation for ULT use in CKD patients, worsening kidney function while taking ULT is unlikely. Further research is necessary to determine the long-term impact of ULTs on kidney function and cardiovascular biomarkers.

The sympathetic restoration and conservation of built cultural heritage play a significant role in the management and preparedness for future climate scenarios by facilitating adaptive reuse, enhancing cultural resilience, preserving traditional knowledge, and boosting tourism. The importance of restoring damaged heritage sites after an earthquake drew international attention to Nepal after the 2015 Gorka Earthquake. UNESCO established an office in Kathmandu to promote the restoration of tangible and intangible heritage in the area. This included developing structural analyses of buildings with historical and cultural value that due to their nature cannot be intervened with the same methodology as modern buildings. In this paper, the case study of the earthquake-damaged Gopinath temple, is discussed. First, an initial visual inspection phase and the following diagnosis of the structure are discussed. Then, the results from a series of static and dynamic structural analyses performed to determine the safety level of the structure, together with a sensitivity analysis, are presented. A sympathetic intervention proposal capable of increasing the temple safety level and based on the addition of timber plates, has resulted in substantial improvements of the lateral behavior of the structure. The proposed intervention is deemed sustainable and able to increase the resilience of the temple in the face of future hazards.

heterocycles are particularly common moieties within marine natural products. Specifically, tetrahydrofuranyl rings are present in a variety of compounds which present complex structures and interesting biological activities. Focusing on terpenoids, a high number of tetrahydrofuran-containing metabolites have been isolated during the last decades. They show promising biological activities, making them potential leads for novel antibiotics, antikinetoplastid drugs, amoebicidal substances or anticancer drugs. Thus, they have attracted the attention of the synthetic community, and numerous approaches to their total syntheses have appeared. Here, we offer the reader an overview of marine-derived terpenoids and related compounds, with a special focus on their isolation, structure determination, biological profiles and total syntheses.

’Lockdown’ periods in response to COVID-19 have provided a unique opportunity to study the impacts of economic activity on environmental pollution (e.g. NO2, aerosols, noise, light). The effects on NO2 and aerosols have been very noticeable and readily demonstrated, but that on light pollution has proven challenging to determine. The main reason for this difficulty is that the primary source of nighttime satellite imagery of the earth is the SNPP-VIIRS/DNB instrument, which acquires data late at night after most human nocturnal activity has already occurred and much associated lighting has been turned off. Here, to analyze the effect of lockdown on urban light emissions, we use ground and satellite data for Granada, Spain, during the COVID-19 induced confinement of the city’s population from March 14 until May 31, 2020. We find a clear decrease in light pollution due both to a decrease in light emissions from the city and to a decrease in anthropogenic aerosol content in the atmosphere which resulted in less light being scattered. A clear correlation between the abundance of PM10 particles and sky brightness is observed, such that the more polluted the atmosphere the brighter the urban night sky. An empirical expression is determined that relates PM10 particle abundance and sky brightness at three different wavelength bands.

The purpose of this research has been to provide information about the psychological effects of confinement forced by a prolonged quarantine in a suitable adult sample of 596 Bolivians of both sexes. It was sought to explore the perceptions of the participants about their own emotional state: fears, anxieties, depressions, while they were in isolation to reduce the probability of contagion of COVID 19. Additionally, we were interested in verifying the modulating effects of resilience and self-efficacy on such emotional states. The results indicated, in the same direction of similar studies, significant relationships between high perceived loneliness, high levels of stress and anxiety, with relatively high levels of depression. Likewise, it was clear that stress, perceived loneliness, and anxiety are predictors of depression among those in conditions of forced isolation. Finally, it was found that both resilience and self-efficacy exert a clear moderating effect by attenuating the relationships of perceived loneliness and anxiety, on depression.

This research was carried out with the objective of analyzing the principles of social and solidarity economy in the community funds of the rural sector of Pichincha, Ecuador. Small organizations promote microcredits for local, social and economic development, representing an alternative to those managed by traditional banks. The research was descriptive, non-experimental field research. The population analyzed consisted of 220 community funds, and the size of the representative sample was 49 community organizations that practice solidarity finance. The data were collected through online questionnaires using a Likert scale, and the validity of this approach was judged by experts; the reliability of the instrument obtained was 0.95 using the Cronbach’s alpha method. The results highlight that in these organizations, the following traits prevail: associativity, self-management and organization. However, autonomy and solidarity have a negative valuation, which shows that strategies must be rethought to achieve the empowerment of the financial service. This will allow them to be sustainable and to expand with more benefits that promulgate financial activity and promote structures in rural community networks that promote local development and strengthen deficient principles as a basis for generating a greater benefit to the partners.

This article focuses on generating an alternative in order to identify traffic officers during driving. This research employed the latest You Only Look Once (YOLO) model, using a six-phase methodology: data collection, data preparation involving resizing and labeling, implementation of various filters to avoid overfitting, model training, prediction evaluation, and result interpretation. The YOLO model was applied across three iterations using a dataset of 1862 images. To enhance training efficiency and detection speed, the graphics processing unit (GPU) acceleration was utilized, further enhancing the experimental process. The results of this study revealed that the YOLOv8x variant produced the most promising results. This proposed model attained a remarkable F1 score of 0.95, bolstered by a confidence score of 0.631, with the potential for an increase to 0.80 in confidence without significantly compromising the F1-score. These findings are poised to make a substantial contribution to the broader research landscape, particularly in advancing the effectiveness of detection models for traffic officers.

Zinc protection of galvanized steel is initially dissolved in alkaline solutions. However, passive layer is formed over time which protects the steel from corrosion. The behavior of galvanized steel exposed to strong alkaline solutions (pH values of 12.7) with a fixed concentration of sulfate ion of 0.04M is studied. Electrochemical measurement techniques such as corrosion potential, linear polarization resistance and electrochemical impedance spectroscopy are used. Synergistic effect of sulfate ion is also studied together with other anions such as chloride Cl- or bicarbonate ion HCO3- and with other cations such as calcium Ca2+, ammonium NH4+ and magnesium Mg2+. Presence of sulfate ions can depassivate the steel, leading to corrosion density of 0.3 µA/cm2 at the end of the test. The presence of other ions in the solution increases this effect. The increase in corrosion density caused by cations and anions responds to the following order (greater to lesser influence): NH4+&gt;Ca2+&gt;Mg2+ and HCO3- &gt;Cl- &gt;SO42-.

The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of Dynamic Equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio on cryptocurrencies by implementing backtesting techniques and for different risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. The results support our proposal showing that the SNP-DCC model has better performance for a smaller confidence level than the SNP-DECO model, although both models perform similarly for higher confidence levels.

Trichomonas vaginalis is an extracellular parasite protozoan that causes human trichomoniasis, a sexually transmitted infection (STI) that affects approximately 200 million people worldwide. The presence of T. vaginalis adhesion to inert substrates has been described in several reports. Still, very few studies on cluster formation and more detailed analyses of the contact regions between the membranes of the parasites in these aggregate formations have not been carried out. We analyzed the formation of parasite monolayers and clusters using high-resolution scanning electron microscopy, transmission electron microscopy, cytochemistry, TEM tomography, and dye injection. Here, we show the formation of a monolayer of tightly adherent cells, epithelium-like, when the parasites are in contact with an inert material. Based on this observation, we analyzed whether this monolayer behaves as an epithelium, analyzing cell junctions, cell communication, and ultrastructural aspects. We also analyzed the cluster formation and the morphological characteristics of parasite aggregation observed in the culture supernatant. We report that monolayer formation differs from cluster formation in many aspects. The monolayers form strong adhesion, whereas the clusters have fragile attachments. There is no fusion or passage of molecules between neighbor-attached cells; there is no need for different strains to form filopodia, cytonemes, and extracellular vesicles during cluster formation.

: In Chile, most tailings impoundments are in Northern and Central Chile, 20% of them are abandoned. Municipal budgets to manage these environmental liabilities are very limited and the use of native and endemic plant species to remove heavy metals from tailings is a low-cost alternative. Ex-situ phytoremediation experiments were conducted over seven months to evaluate the potential of Lycium chilense and Haplopappus foliosus to remove copper and lead from mine tailings. The results indicate that both species can concentrate high levels of both elements and they present removal efficiencies for Cu close to 50%. The best performance was presented by Haplopappus foliosus, accumulating higher concentrations of both metals than Lycium chilense. Also, it presents a bioconcentration over 1.

This research explored the influence of spatiotemporal variations in phreatic levels and salinity on the patterns and interactions of soil mesofauna communities in a tropical urban coastal wetland. It delineated significant variations in phreatic levels and salinity, with distinct regimes characterizing different habitats and hydroperiods. A total of 120 mesofauna morphotypes across 42 taxa were identified, revealing habitat differences and similarities, and the ecosystem biodiversity. Moderate correlations were established between phreatic levels, salinity, and mesofauna richness and abundance, with habitat type and phreatic level emerging as significant predictors in the regression model. Our findings indicate that shallow phreatic levels in conjunction with oligohaline salinity conditions foster a more favorable abiotic environment, thereby promoting increased biodiversity. The study underscores the dynamic and adaptive nature of mesofauna communities in response to changing environmental conditions, offering valuable insights for effective costal urban wetland management, particularly in the context of multiple stressors such as global and regional climate change, sea level rise, and human activities. The bio-sensor capacity of soil mesofauna emerges as a vital tool for monitoring and facilitating adaptive ecosystem management, thereby ensuring their long-term health and sustainability.

The residual multiparticle entropy (RMPE) of a fluid is defined as the difference, Δs, between the excess entropy per particle (relative to an ideal gas with the same temperature and density), s_ex, and the pair-correlation contribution, s₂. Thus, the RMPE represents the net contribution to s_ex due to spatial correlations involving three, four, or more particles. A heuristic “ordering” criterion identifies the vanishing of the RMPE as an underlying signature of an impending structural or thermodynamic transition of the system from a less ordered to a more spatially organized condition (freezing is a typical example). Regardless of this, the knowledge of the RMPE is important to assess the impact of non-pair multiparticle correlations on the entropy of the fluid. Recently, an accurate and simple proposal for the thermodynamic and structural properties of a hard-sphere fluid in fractional dimension 1 < d < 3 has been proposed [Santos, A.; López de Haro, M. Phys. Rev. E 2016, 93, 062126]. The aim of this work is to use this approach to evaluate the RMPE as a function of both d and the packing fraction ϕ. It is observed that, for any given dimensionality d, the RMPE takes negative values for small densities, reaches a negative minimum Δs_min at a packing fraction ϕ_min, and then rapidly increases, becoming positive beyond a certain packing fraction ϕ₀. Interestingly, while both ϕ_min and ϕ₀ monotonically decrease as dimensionality increases, the value of Δs_min exhibits a nonmonotonic behavior, reaching an absolute minimum at a fractional dimensionality d ≃ 2.38. A plot of the scaled RMPE Δs/|Δs_min| shows a quasiuniversal behavior in the region −0.14 ≲ ϕ − ϕ₀ ≲ 0.02.

The broad global distribution of freshwater clams belonging to the genus Corbicula is driven by multiple hermaphroditic lineages. These lineages, characterized by shared morphological traits and phenotypic plasticity, pose challenges to morphological identification. Genetic markers, such as the mitochondrial COI gene, play a crucial role in delineating these lineages and their ranges. Morphotypes represent observed phenotypic variations, while lineages are defined based on genetic markers. Here, we comprehensively review Corbicula's distribution in Argentina, dis-criminate extant lineages based on both morphological and genetic (COI) data, and describe variation in internal and external morphology using 15 Argentine populations. Haplotype net-works identified two mitochondrial lineages: A/R morphotype (FW5 haplotype) and C/S mor-photype (FW17 haplotype). Strikingly, despite having similar vectors, origins, and invasive stages, Corbicula lineages exhibit virtually segregated distributions. However, intermediate morphotypes and both mitochondrial haplotypes are found in sympatry in northeastern Argen-tina, suggesting the presence of hybrids due to maternal genome retention. These findings con-tribute to the clarification of the identity and distribution of Corbicula lineages in Argentina, where the genus has been found for over half a century. Similar studies are needed in other areas to better understand the invasion patterns of a successful and plastic group.

This article aims at presenting the results that were found in the design and implementation of a new management model of Critical Success Factors for improving the effects of research projects. The model was based on the theory of structural equations derived from factor analyses and multiple linear regression models. For achieving the purpose, the authors developed an empirical evidence analysis founded on a literature review in the knowledge area of project management. Afterwards, the process of identification of the CSFs was carried out for a group of research projects launched in Colombia as a result of the application and measurement of an instrument. They were the basis for the development of a structural relationship modeling. Also, this contributed to determine the CSFs that explain the generation of effects of research projects, and the project management execution process from which it can be carried out based on said measurement indicators.

A reliability-based criterion to estimate strength amplification factors for buildings with asymmetric yielding located within a seismic region presenting different soil conditions is proposed and applied. The approach involves the calculation of the mean annual rate of exceedance of structural demands of systems with different levels of asymmetric yielding. Two simplified mathematical expressions are developed considering different soil conditions of the valley of Mexico. The mathematical expressions depend on the ductility of the structural systems, their level of asymmetric yielding, their fundamental vibration period and the dominant period of the soil. In addition, the proposed expressions are compared with that recommended by the current Mexico City Building Code (MCBC). Since the expressions are developed with the help of simplified structural systems, the validity of such expressions is corroborated by comparing the expected ductility demand of multi-degree of freedom (MDOF) structural systems with respect to that of their equivalent simplified systems. Both structural representations are associated with a given annual rate of exceedance value of an engineering demand parameter. The expressions proposed in this study will be incorporated in the new version of the MCBC.