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RESUMENIntroducción: Las anomalías dentomaxilofaciales son consideradas un problema epidemiológico de significativa importancia, que tiene profundo impacto en los individuos y la sociedad en términos de molestias, limitaciones sociales y funcionales. Las mismas comprenden las alteraciones del crecimiento, desarrollo y fisiologismo de los componentes anatómicos que conforman el sistema estomatognático. Un frenillo lingual corto, insertado muy cerca de la punta de la lengua, es un cuadro bastante común descrito como “lengua presa” pudiendo provocar, en el futuro, problemas de deglución, periodontales y del habla.Objetivo: El objetivo de la presente investigación fue realizar una evaluación del frenillo sublingual como parte de la atención estomatológica integral al menor de 19 años.Métodos: Se realizó una revisión bibliográfica de la literatura disponible. Los artículos se identificaron a través de la búsqueda automatizada en las bases de datos: PubMed, SciELO y Google Académico, en el período de enero a junio 2023. Fueron seleccionados 39 textos científicos, escritos en los idiomas español e inglés, publicados entre 2019 y 2023 (37 artículos científicos y 2 libros). Conclusiones:el diagnóstico de la enfermedad y la planificación quirúrgica y miofuncional deben tener en cuenta la clasificación del frenillo, la edad del paciente y la anatomía de la región. El tratamiento requiere de un equipo multidisciplinario para evitar la recidiva.

ABSTRACT This study explores the dynamics of immune gene expression, ruminal metabolome, and gut microbiota in cows due to the duration of high-grain feeding, shedding light on host response and microbial dynamics in parallel. Cows consumed forage for a week, then gradually transitioned to a high-grain diet, which they consumed for 4 weeks. Immune response was evaluated in ruminal papillae by expression of genes related to the nuclear factor-kappaB (NFkB) pathway and correlated with the microbiota. Rumen metabolome was evaluated with high-performance liquid chromatography coupled with mass spectrometry and anion-exchange chromatography. Rumen and fecal microbiota were evaluated with 16S rRNA gene amplicon sequencing. In the rumen, expression of inflammation-associated genes increased with the duration on high grain, indicating activation of pro-inflammatory cascades; microbial diversity decreased with a high-grain diet but stabilized after week 3 on high grain. Changes in microbial relative abundance and metabolite enrichment were observed throughout the 4 weeks on high grain, with increments in propionogenic taxa (i.e., Succinivibrionaceae). Metabolite enrichment analysis showed that at the start of high-grain feeding, simple carbohydrates were enriched; then, these were substituted by their fermentation products. There were correlations between certain ruminal bacterial taxa (i.e., Ruminococcaceae UCG-005) and expression of genes of the NFkB pathway, suggesting the influence of these taxa on host immune response. In feces, microbial diversity and several Ruminococcaceae members initially declined but recovered by weeks 3 and 4. Overall, despite the stabilization of microbial diversity, changes in microbial relative abundance and proinflammatory genes were observed throughout high-grain feeding, suggesting that cows need more than 4 weeks to fully adjust once consuming a high-grain diet.IMPORTANCEDespite the stepwise diet transition typically assumed to serve for animal adaptation, expression of signaling receptors, mediators, and downstream targets of nuclear factor-kappaB pathway were found throughout the 4 weeks on high grain, which correlated with changes in the rumen microbial profile. In addition, although microbial diversity recovered in the feces and stabilized in the rumen in week 3 on high grain, we observed changes in microbial relative abundance throughout the 4 weeks on high grain, suggesting that cows need more than 4 weeks to adjust once consuming this diet. Findings are particularly important to consider when planning experiments involving dietary changes.

This paper presents a quantitative study of the effects of using arbitrary-order operators in Neural Networks. It is based on a Recurrent Wavelet First-Order Neural Network (RWFONN), which can accurately identify several chaotic systems (measured by the mean square error and the coefficient of determination, also known as R-Squared, r2) under a fixed parameter scheme in the neural algorithm. Using fractional operators, we analyze whether the identification capabilities of the RWFONN are improved, and whether it can identify signals from fractional-order chaotic systems. The results presented in this paper show that using a fractional-order Neural Network does not bring significant advantages in the identification process, compared to an integer-order RWFONN. Nevertheless, the neural algorithm (modeled with an integer-order derivative) proved capable of identifying fractional-order dynamical systems, whose behavior ranges from periodic and multi-stable to chaotic oscillations. That is, the performances of the Neural Network model with an integer-order derivative and the fractional-order network are practically identical, making the use of fractional-order RWFONN-type networks meaningless. The results deepen the work previously published by the authors, and contribute to developing structures based on robust and generic neural algorithms to identify more than one chaotic oscillator without retraining the Neural Network.

Abstract MicroRNAs (miRNAs), as important post-transcriptional regulators, are ubiquitous in various tissues. The aim of this exploratory study was to determine the presence of miRNAs in rumen fluid, and to investigate the possibility of miRNA-mediated cross-talk within the ruminal ecosystem. Rumen fluid samples from four cannulated Holstein cows were collected during two feeding regimes (forage and high-grain diet) and DNA and RNA were extracted for amplicon and small RNA sequencing. Epithelial biopsies were simultaneously collected to investigate the co-expression of miRNAs in papillae and rumen fluid. We identified 377 miRNAs in rumen fluid and 638 in rumen papillae, of which 373 were shared. Analysis of microbiota revealed 20 genera to be differentially abundant between the two feeding regimes, whereas no difference in miRNAs expression was detected. Correlations with at least one genus were found for 170 miRNAs, of which, 39 were highly significant (r > |0.7| and P

ABSTRACT: This study evaluated the effects of duration of high-concentrate feeding on ruminal and fecal fermentation profile, as well as selected systemic health biomarkers in nonlactating cows supplemented with or without a phytogenic feed additive (PHY). In addition, ruminal degradation kinetics and total-tract nutrient digestibility were evaluated when feeding either only forage or a high-concentrate diet. Nine nonlactating, cannulated Holstein cows were used in a crossover design. Each period included 1 wk of forage feeding (wk 0), diet transition, and 4 wk on the high-concentrate diet (1, 2, 3 and wk 4; 65% dry matter basis). Cows received PHY or not (control). Compared with wk 0, from wk 1 onward, cows on high concentrate showed greater reticular, ruminal, and fecal total volatile fatty acids (VFA), with a greater level of VFA in the rumen than in the hindgut. However, ruminal fermentation was modulated differently by PHY, which showed increased total VFA in wk 1 and increased butyrate in wk 2 in the particle-associated fluid of rumen. In the hindgut, PHY increased propionate in wk 3. Cows fed a high-concentrate diet from wk 1 and onward also showed greater ruminal lactate, as well as lower ruminal and fecal pH, independent of PHY. In addition, compared with cows in wk 1 on a high-concentrate diet, cows in wk 4 had a greater total VFA in free fluid of the rumen and lower fecal pH. Compared with cows at wk 0, cows at wk 1 on high concentrate onward showed greater serum amyloid A and greater activity of glutamate dehydrogenase. In contrast, the high-concentrate diet decreased in situ ruminal degradability of grass silage but increased degradability of corn grain as well as total-tract nutrient digestibility, with total-tract neutral detergent fiber digestibility being greater for cows on the PHY treatment. Overall, from the start of high-concentrate feeding, gut fermentation increased, but differently according to location or PHY, with a stronger build-up of VFA in the rumen compared with the hindgut. In addition, a longer duration on high concentrate exacerbated gut acidification. The enhancing effects of PHY on total VFA and butyrate in particle-associated fluid of the rumen suggest beneficial effects of PHY on particle-associated bacteria, likely contributing to the increased neutral detergent fiber digestibility. The greater production of ruminal butyrate with PHY may be beneficial for the host, given the health benefits of this acid, but more research is needed to elucidate the effects on gut microbiota and the effects of increased butyrate in nonlactating dairy cows.

There is a limited research focus on evaluating the detrimental effects of prolonged zearalenone (ZEN) intake on dairy cows’ health under controlled conditions. This experiment was conducted to evaluate whether the length of exposure to a ZEN-contaminated total mixed ration (TMR) at a level of 9.45 mg per day can negatively influence animal health parameters, such as milk composition, rumen and fecal fermentation, and the chewing activity of lactating dairy cows. For this experiment, we used 18 lactating Simmental cows that were fed a diet of 60% forage and 40% concentrate (on dry matter basis) for 26 consecutive days. The first 4 days were for adaptation prior to the first sampling day (day 0). The sampling events took place on day 0 (baseline) without ZEN, followed by day 1, day 7, day 14, and day 21 (with toxin). Dry matter intake (DMI) and ruminating chews per minute increased on the third week of ZEN inclusion; meanwhile, ruminating, eating, and drinking times were not affected. Most milk composition variables were also unaffected. Rumen fluid osmolality increased on day 21 and total short-chain fatty acids (SCFA) of ruminal fluid decreased on day 7. Fecal SCFA increased on day 21 and the acetate-to-propionate ratio increased from day 1 onwards, showing the influence of toxin intake. Animal health parameters, like heart rate, respiratory rate, and body temperature, were negatively influenced by ZEN intake, all increasing consistently on days 4 and 6, 9 and 12, and 16 and 18, respectively. The liver enzyme glutamate dehydrogenase decreased in response to ZEN intake on day 7. A total daily ZEN intake at the level of 9.45 mg did not show detrimental effects on DMI. Nevertheless, certain health parameters were negatively affected, including body temperature, respiratory rate, and heart rate, starting from the 7th day of ZEN intake, with additional signs of possible loss of water balance on the last sampling day.

Hoy en día los usuarios demandan servicios y aplicaciones (realidad aumentada, virtual, cirugía remota, entre otros) cada vez más especializados, lo que incide en los últimos años en un rápido crecimiento y desarrollo de dispositivos inteligentes, sensores de Internet de las Cosas (IoT), etc. En consecuencia, los requerimientos de red se han vuelto más exigentes, críticos y de mayor dinamismo con el objetivo de lograr altas tasas de transferencias de datos, conectividad masiva, ultra bajas latencias, y alta confiabilidad. Las redes de nueva generación (5G/B5G) manejan una gran cantidad de parámetros que dificultan su gestión con el modelo tradicional. Aún es insuficiente con la ayuda e implementación de tecnologías como Redes Definidas por Software (SDN), Virtualización de Funciones de Red (NFV) y Segmentación de Red (NS), que mejoran considerablemente el desempeño de la red al enfocarla en servicios. En la relación cliente-proveedor de servicios de telecomunicaciones, este último debe cumplir con los Acuerdos a Nivel de Servicio (SLA) y contar con un mecanismo de gestión automatizada en todos los niveles. En este artículo se resumen las características principales de la red 5G, así como las tecnologías que la apoyan. Además, se expone una propuesta para lograr un esquema de gestión automatizada que permita una mejor repartición (aprovisionamiento) de los recursos en la red.

Improving the quality of service (QoS) and meeting service level agreements (SLAs) are critical objectives in next-generation networks. This article presents a study on applying supervised learning (SL) algorithms in a 5G/B5G service dataset after being subjected to a principal component analysis (PCA). The study objective is to evaluate if the reduction of the dimensionality of the dataset via PCA affects the predictive capacity of the SL algorithms. A machine learning (ML) scheme proposed in a previous article used the same algorithms and parameters, which allows for a fair comparison with the results obtained in this work. We searched the best hyperparameters for each SL algorithm, and the simulation results indicate that the support vector machine (SVM) algorithm obtained a precision of 98% and a F1 score of 98.1%. We concluded that the findings of this study hold significance for research in the field of next-generation networks, which involve a wide range of input parameters and can benefit from the application of principal component analysis (PCA) on the performance of QoS and maintaining the SLA.

Microbial composition and activity in the gastrointestinal tract (GIT) of cattle has important implications for animal health and welfare, driving the focus of research toward ways to modify their function and abundance. However, our understanding of microbial adaption to nutritional changes remains limited. The aim of this study was to examine the progressive mechanisms of adaptation in the rumen and hindgut of cattle receiving increasing amounts of starch with or without dietary supplementation of a blended phytogenic feed additive (PFA; containing menthol, thymol and eugenol). We used 16S rRNA gene amplicon sequencing to assess the microbial composition and predicted metabolic pathways in ruminal solid and liquid digesta, and feces. Furthermore, we employed targeted liquid chromatography-mass spectrometry methods to evaluate rumen fluid metabolites. Results indicated a rapid microbial adaptation to diet change, starting on the second day of starch feeding for the particle associated rumen liquid (PARL) microbes. Solid rumen digesta- and feces-associated microbes started changing from the following day. The PARL niche was the most responsive to dietary changes, with the highest number of taxa and predicted pathways affected by the increase in starch intake, as well as by the phytogenic supplementation. Despite the differences in the microbial composition and metabolic potential of the different GIT niches, all showed similar changes toward carbohydrate metabolism. Metabolite measurement confirmed the high prevalence of glucose and volatile fatty acids (VFAs) in the rumen due to the increased substrate availability and metabolic activity of the microbiota. Families Prevotellaceae, Ruminococcaceae and Lachnospiraceae were found to be positively correlated with carbohydrate metabolism, with the latter two showing wide-ranging predicted metabolic capabilities. Phytogenic supplementation affected low abundant taxa and demonstrated the potential to prevent unwanted implications of feeding high-concentrate diet, such as reduction of microbial diversity. The inclusion of 50% concentrate in the diet caused a major shift in microbial composition and activity in the GIT of cattle. This study demonstrated the ability of microorganisms in various GIT niches to adjust differentially, yet rapidly, to changing dietary conditions, and revealed the potential beneficial effects of supplementation with a PFA during dietary adaptation.

Software-defined networking (SDN) is the fastest growing and most widely deployed network infrastructure due to its adaptability to new networking technologies and intelligent applications. SDN simplifies network management and control by separating the control plane from the data plane. The SDN controller performs the routing process using the traditional shortest path approach to obtain end-to-end paths. This process usually does not consider the nodes’ capacity and may cause network congestion and delays, affecting flow performance. Therefore, we evaluate the most conventional routing criteria in the SDN scenario based on Dijkstra’s algorithm and compare the found paths with our proposal based on a cellular genetic algorithm for multi-objective optimization (MOCell). We compare our proposal with another multi-objective evolutionary algorithm based on decomposition (MOEA/D) for benchmark purposes. We evaluate various network parameters such as bandwidth, delay, and packet loss to find the optimal end-to-end path. We consider a large-scale inter-domain SDN scenario. The simulation results show that our proposed method can improve the performance of data streams with TCP traffic by up to 54% over the traditional routing method of the shortest path and by 33% for the highest bandwidth path. When transmitting a constant data stream using the UDP protocol, the throughput of the MOCell method is more than 1.65% and 9.77% for the respective paths.

Saliva facilitates feed ingestion, nutrient circulation, and represents an important pH buffer for ruminants, especially for cattle fed high-concentrate diets that promote rumen acidification. This experiment evaluated the short-term effects of nine phytogenic compounds on salivation, saliva physico-chemical composition as well as ingested feed boli characteristics in cattle. A total of nine ruminally cannulated Holstein cows were used. Each compound was tested in four of these cows as part of a high-concentrate meal (2.5 kg of total mixed ration in dry matter basis for 4 h) in low or high dose, and was compared to a control meal without compound. Saliva was sampled orally (unstimulated saliva) for physico-chemical composition analysis. Composition of the ingested saliva (stimulated saliva), salivation and feed boli characteristics were assessed from ingesta collected at the cardia during the first 30 min of the meal. Analysis of unstimulated saliva showed that supplementation with capsaicin and thyme oil increased buffer capacity, while supplementation with thymol, L-menthol and gentian root decreased saliva pH. In addition, supplementing angelica root decreased saliva osmolality. Regression analysis on unstimulated saliva showed negative associations between mucins and bicarbonate as well as with phosphate when garlic oil, thyme oil or angelica root was supplemented. Analysis of stimulated saliva demonstrated that supplementation with garlic oil increased phosphate concentration, thyme oil tended to increase osmolality, capsaicin and thymol increased buffer capacity, and ginger increased phosphate content. Furthermore, salivation rate increased with ginger and thymol, and tended to increase with garlic oil, capsaicin, L-menthol and mint oil. Feed ensalivation increased with capsaicin. A positive association was found between feed bolus size and salivation rate when any of the phytogenic compounds was supplemented. Overall, our results demonstrate positive short-term effects of several phytogenic compounds on unstimulated and stimulated saliva physico-chemical properties, salivation or feed boli characteristics. Thus, the phytogenic compounds enhancing salivary physico-chemical composition have the potential to contribute to maintain or improve ruminal health in cattle fed concentrate-rich rations.

Switching diets from forage to a high-grain (HG) diet increases the risk of rumen fermentation disorders in cattle. However, the effects of the duration of the HG feeding, after the diet switch, on animal behavior and health have received considerably less attention. This experiment primarily aimed to assess the effects of the duration of an HG diet on the chewing, eating, and lying behavior and salivation dynamics in a control group (CON) and a group of cows receiving a phytogenic feed supplement (TRT) at 0.04% (DM basis), which included L-menthol, thymol, eugenol, mint oil, and cloves powder. The experiment was a crossover design with nine non-lactating cows, and two experimental periods with an intermediate washout of four weeks. In each period, the cows were first fed a forage diet for a week to collect baseline measurements representing week 0; then, the diet was switched over a week to HG (65% concentrate), which was fed for four continuous weeks (week 1, week 2, week 3, and week 4 on an HG diet, respectively). The cows were divided in two groups of four and five animals and were randomly allocated to CON or TRT. The data analysis revealed that at the start of the HG feeding, the dry matter intake and the cows’ number of lying bouts increased, but the eating time, rumination time, and meal frequency decreased, resulting in a greater eating rate. We also found that an advanced duration on an HG diet further decreased the rumination time, total chewing time, chewing index, and sorting in favor of short feed particles, with the lowest values in week 4. The feed bolus size increased but feed the ensalivation decreased in week 4 compared to week 0. The dietary switch increased salivary lysozyme activity, and the advanced duration on the HG diet increased salivary pH, but salivary phosphate decreased in weeks 1 and 2 on the HG diet. Supplementation with TRT increased sorting in favor of physically effective NDF (peNDF) in week 2 and increased salivary pH in week 4 on an HG diet. Overall, the negative effects of the HG diet in cattle are more pronounced during the initial stage of the HG feeding. However, several detrimental effects were exacerbated with the cows’ advanced duration on feed, with host adaptive changes still observed after 3 and 4 weeks following the diet switch. The TRT mitigated some of the negative effects through the temporal improvement of the salivary properties and the intake of peNDF, which are known to modulate rumen fermentation.

Nowadays, industrial robot applications are required to customize the manufacturing of diverse products to reduce both downtime and standoff variability. The two methods for robot programming are regularly implemented to carry out that goal. The first one, online programming, requires a specialized operator to guide the robot through desired poses, and the quality of the result is directly limited by his skill level. On the other side, off-line programming uses software packaging to simulate robot applications before their implementation. It reduces downtime with respect to online programming but requires additional calibration steps. In this paper, a novel procedure is presented to obtain accurate surface approximations by combining linear interpolations generated during online programming with a triangulated surface reconstruction of a workpiece surface representation. The method uses a point cloud instead of a predefined mesh to reduce the standoff variability between the robotic tool center point and the surface. Additionally, a technique based on a penalized least squares method was implemented to smooth the trajectory, including position and orientation. The proposed methodology was validated with three well-known case studies involving real trajectories, with simulations in MATLAB and RobotStudio, as well as by experimentation with an industrial ABB robot. The quality of the results demonstrates a great efficiency of this method for path generation based on surface reconstruction.

Multi-unmanned aerial vehicle (multi-UAV) systems have become popular in applications such as precision agriculture, remote sensing, and pollution monitoring. Commonly, multi-UAV systems require to reach and maintain a specific flight formation during mission execution. This can be achieved by using a distributed UAV formation control strategy in which each UAV has a flight controller whose function is to calculate the control actions for the UAV actuators such that the UAV formation is maintained. To perform this task, the control strategy requires the reliable and timely exchange of information within the UAV formation. The information that is needed by the controller is commonly referred to as state information (SI). It has been assumed that SI can be properly disseminated by means of multi-hop communications, i.e., by deploying a flying ad-hoc network (FANET). In this sense, multi-hop broadcast protocols (MBPs) that were previously proposed for mobile and vehicular ad-hoc networks seem to be suited for this task. However, previous work dealing with distributed UAV formation control has made communication and networking assumptions that would be hard to fulfill in actual FANET deployments. Moreover, the efficiency of the MBPs to disseminate SI within a FANET remains unexplored. The goal of this paper is to analyze how the network performance offered by different MBPs impacts the effectiveness of distributed UAV formation control to maintain UAV formation. An evaluation framework to perform this task is proposed in this paper. The simulation results demonstrate the relevance of MBP performance in SI message dissemination and thus in the ability of the controller to maintain a formation.

Feeding with high-concentrate diets increases the risk of subacute ruminal acidosis (SARA). This experiment was conducted to evaluate whether supplementing a phytogenic feed additive based on L-menthol, thymol, eugenol, mint oil (Mentha arvensis) and cloves powder (Syzygium aromaticum) (PHY) can amend the ruminal fermentation profile, modulate the risk of SARA and reduce inflammation in cattle. The experiment was designed as a crossover design with nine non-lactating Holstein cows, and was conducted in two experimental runs. In each run, cows were fed a 100% forage diet one week (wk 0), and were then transitioned stepwise over one week (0 to 65% concentrate, wk adapt.) to a high concentrate diet that was fed for 4 weeks. Animals were fed diets either with PHY or without (CON). The PHY group had an increased ruminal pH compared to CON, reduced time to pH < 5.8 in wk 3, which tended to decrease further in wk 4, reduced the ruminal concentration of D-lactate, and tended to decrease total lactate (wk 3). In wk 2, PHY increased acetate, butyrate, isobutyrate, isovalerate, and the acetate to propionate ratio compared to CON. Phytogenic supplementation reduced inflammation compared to CON in wk 3. Overall, PHY had beneficial effects on ruminal fermentation, reduced inflammation, and modulated the risk of SARA starting from wk 3 of supplementation.

This article proposes a framework to design and implement e- Health interventions in a comprehensive manner. We draw on complexity science to study the interplay of the ecosystem, the behavior and interactions among its agents. We provide a platform to estimate the Quality of Experience (QoE) to assess the relationship between technology and human factors involved in e-Health projects. Our aim is to estimate QoE in e-Health ecosystems from the perspective of complexity by adopting a methodology that uses fuzzy logic to study the behavior of the ecosystem’s agents. We apply the proposed framework to a remote diagnosis case by means of an ultrasound probe through a satellite link. Despite the ambiguities for determining QoE, the experiment demonstrates the applicability of the framework and allows to stressing the importance of human factors in the implementation of e-Health projects.

The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of Experience (QoE) to users. Service classification allows 5G service providers to accurately select the network slices for each service, thereby improving the QoS of the network and the QoE perceived by users, and ensuring compliance with the Service Level Agreement (SLA). Some projects have developed systems for classifying these services based on the Key Performance Indicators (KPIs) that characterize the different services. However, Key Quality Indicators (KQIs) are also significant in 5G networks, although these are generally not considered. We propose a service classifier that uses a Machine Learning (ML) approach based on Supervised Learning (SL) to improve classification and to support a better distribution of resources and traffic over 5G/B5G based networks. We carry out simulations of our proposed scheme using different SL algorithms, first with KPIs alone and then incorporating KQIs and show that the latter achieves better prediction, with an accuracy of 97% and a Matthews correlation coefficient of 96.6% with a Random Forest classifier.

El presente artículo propone extender el concepto de costo de incertidumbre en la operación de sistemas de energía que contienen fuentes renovables (solar y eólica) a sistemas de potencia con penetración de pequeñas centrales hidroeléctricas (PCHs). La extensión propuesta se logra obtener conociendo la probabilidad de distribución del caudal donde está la PCH. Para ello, se presenta el desarrollo analítico de los costos de incertidumbre en pequeñas centrales hidroeléctricas, obteniendo la probabilidad de potencia inyectada por la central y desarrollando la formulación matemática de los costos por subestimar y sobreestimar la potencia disponible. Adicionalmente, se presenta la validación de la formulación analítica a través de simulaciones de Monte Carlo, comprobando que en los dos casos (costo de incertidumbre con la formula analítica y valor esperado de la simulación de Monte Carlo) se llega al mismo costo de incertidumbre. Adicionalmente, se presentan los códigos que calculan el costo esperado mediante la formulación analítica obtenida y el costo esperado mediante el valor medio de los costos de la simulación de Monte Carlo.

When Internet of Things (IoT) big data analytics (BDA) require to transfer data streams among software defined network (SDN)-based distributed data centers, the data flow forwarding in the communication network is typically done by an SDN controller using a traditional shortest path algorithm or just considering bandwidth requirements by the applications. In BDA, this scheme could affect their performance resulting in a longer job completion time because additional metrics were not considered, such as end-to-end delay, jitter, and packet loss rate in the data transfer path. These metrics are quality of service (QoS) parameters in the communication network. This research proposes a solution called QoSComm, an SDN strategy to allocate QoS-based data flows for BDA running across distributed data centers to minimize their job completion time. QoSComm operates in two phases: (i) based on the current communication network conditions, it calculates the feasible paths for each data center using a multi-objective optimization method; (ii) it distributes the resultant paths among data centers configuring their openflow Switches (OFS) dynamically. Simulation results show that QoSComm can improve BDA job completion time by an average of 18%.

Silvopasture is a type of agroforestry that could deliver ecosystem services and support local livelihoods by integrating trees into pasture-based livestock systems. This study modeled the financial returns from silvopastures, planted forests, and conventional cattle-pasture systems in Amazonas, Peru using capital budgeting techniques. Forests had a lower land expectation value (USD 845 per hectare) than conventional cattle systems (USD 1275 per hectare) at a 4% discount rate. “Typical” model silvopastures, based on prior landowner surveys in the Amazonas region, were most competitive at low discount rates. The four actual silvopastoral systems we visited and examined had higher returns (4%: USD 1588 to USD 9524 per hectare) than either alternative pure crop or tree system, more than likely through strategies for generating value-added such as on-site retail stands. Silvopasture also offers animal health and environmental benefits, and could receive governmental or market payments to encourage these practices.

Many robots are dedicated to replicate trajectories recorded manually; the recorded trajectories may contain singularities, which occur when positions and/or orientations are not achievable by the robot. The optimization of those trajectories is a complex issue and classical optimization methods present a deficient performance when solving them. Metaheuristics are well-known methodologies for solving hard engineering problems. In this case, they are applied to obtain alternative trajectories that pass as closely as possible to the original one, reorienting the end-effector and displacing its position to avoid the singularities caused by limitations of inverse kinematics equations, the task, and the workspace. In this article, alternative solutions for an ill-posed problem concerning the behavior of the robotic end-effector position and orientation are proposed using metaheuristic algorithms such as cuckoo search, differential evolution, and modified artificial bee colony. The case study for this work considers a three-revolute robot (3R), whose trajectories can contain or not singularities, and an optimization problem is defined to minimize the objective function that represents the error of the alternative trajectories. A tournament in combination with a constant of proportionality allows the metaheuristics to modify the gradual orientation and position of the robot when a singularity is present. Consequently, the procedure selects from all the possible elbow-configurations the best that fits the trajectory. A classical numerical technique, Newton’s method, is used to compare the results of the applied metaheuristics, to evaluate their quality. The results of this implementation indicate that metaheuristic algorithms are an efficient tool to solve the problem of optimizing the end-effector behavior, because of the quality of the alternative trajectory produced.

ABSTRACT The USA300 North American epidemic (USA300-NAE) clone of methicillin-resistant Staphylococcus aureus has caused a wave of severe skin and soft tissue infections in the United States since it emerged in the early 2000s, but its geographic origin is obscure. Here we use the population genomic signatures expected from the serial founder effects of a geographic range expansion to infer the origin of USA300-NAE and identify polymorphisms associated with its spread. Genome sequences from 357 isolates from 22 U.S. states and territories and seven other countries are compared. We observe two significant signatures of range expansion, including decreases in genetic diversity and increases in derived allele frequency with geographic distance from the Pennsylvania region. These signatures account for approximately half of the core nucleotide variation of this clone, occur genome wide, and are robust to heterogeneity in temporal sampling of isolates, human population density, and recombination detection methods. The potential for positive selection of a gyrA fluoroquinolone resistance allele and several intergenic regions, along with a 2.4 times higher recombination rate in a resistant subclade, is noted. These results are the first to show a pattern of genetic variation that is consistent with a range expansion of an epidemic bacterial clone, and they highlight a rarely considered but potentially common mechanism by which genetic drift may profoundly influence bacterial genetic variation. IMPORTANCE The process of geographic spread of an origin population by a series of smaller populations can result in distinctive patterns of genetic variation. We detect these patterns for the first time with an epidemic bacterial clone and use them to uncover the clone’s geographic origin and variants associated with its spread. We study the USA300 clone of methicillin-resistant Staphylococcus aureus, which was first noticed in the early 2000s and subsequently became the leading cause of skin and soft tissue infections in the United States. The eastern United States is the most likely origin of epidemic USA300. Relatively few variants, which include an antibiotic resistance mutation, have persisted during this clone’s spread. Our study suggests that an early chapter in the genetic history of this epidemic bacterial clone was greatly influenced by random subsampling of isolates during the clone’s geographic spread.

AIMS: To determine the incidence of congenital toxoplasmosis in Colombian newborns from 19 hospital or maternal child health services from seven different cities of five natural geographic regions (Caribbean, Central, Andean, Amazonia and Eastern). MATERIALS AND METHODS: We collected 15,333 samples from umbilical cord blood between the period of March 2009 to May 2010 in 19 different hospitals and maternal-child health services from seven different cities. We applied an IgM ELISA assay (Vircell, Spain) to determine the frequency of IgM anti Toxoplasma. The results in blood cord samples were confirmed either by western blot and repeated ELISA IgM assay. In a sub-sample of 1,613 children that were negative by the anti-Toxoplasma IgM assay, the frequency of specific anti-Toxoplasma IgA by the ISAGA assay was determined. All children with positive samples by IgM, IgA, clinical diagnosis or treatment during pregnancy were recalled for confirmatory tests after day 10 of life. RESULTS: 61 positive samples for specific IgM (0.39%) and 9 positives for IgA (0.5%) were found. 143 questionnaires were positive for a clinical diagnosis or treatment for toxoplasmosis during pregnancy. 109 out of the 218 children that had some of the criteria for postnatal confirmatory tests were followed. Congenital toxoplasmosis infection was confirmed in 15 children: 7 were symptomatic, and three of them died before the first month of life (20% of lethality). A significant correlation was found between a high incidence of markers for congenital toxoplasmosis and higher mean annual rainfall for the city. CONCLUSIONS: Incidence for congenital toxoplasmosis is significantly different between hospitals or maternal child health services from different cities in Colombia. Mean annual rainfall was correlated with incidence of congenital toxoplasmosis.

Fractional calculus (or arbitrary order calculus) refers to the integration and derivative operators of an order different than one and was developed in 1695. They have been widely used to study dynamical systems, especially chaotic systems, as the use of arbitrary-order operators broke the milestone of restricting autonomous continuous systems of order three to obtain chaotic behavior and triggered the study of fractional chaotic systems. In this paper, we study the chaotic behavior in fractional systems in more detail and characterize the geometric variations that the dynamics of the system undergo when using arbitrary-order operators by asking the following question: is the Lyapunov exponent sufficient to describe the dynamical variations in a chaotic system of fractional order? By quantifying the convex envelope generated by the 2D projection of the system into all its phase portraits, the changes in the area of the system, as well as the volume of the attractor, are characterized. The results are compared with standard metrics for the study of chaotic systems, such as the Kaplan–Yorke dimension and the fractal dimension, and we also evaluate the frequency fluctuations in the dynamical response. It is found that our methodology can better describe the changes occurring in the systems, while the traditional dimensions are limited to confirming chaotic behaviors; meanwhile, the frequency spectrum hardly changes. The results deepen the study of fractional-order chaotic systems, contribute to understanding the implications and effects observed in the dynamics of the systems, and provide a reference framework for decision-making when using arbitrary-order operators to model dynamical systems.

This work reviews the key challenges surrounding teaching Science, Technology, Engineering, and Mathematics subjects known as STEM. The research has uncovered a significant gap between traditional teaching styles and the need to develop and adapt to new remote-learning modalities. The work describes the technological, pedagogical, social, and institutional challenges, finally identifying the importance of their joint interaction. Since the COVID-19 pandemic, it has become evident that STEM educators must increase their awareness and knowledge of instructional models focused on using digital platforms. The current trend is centred on developing remote-learning tools, which will likely become the predominant learning norm as the economy’s viability increases. However, these remote-learning approaches must maintain interaction with the physical world, as understanding real-world phenomena is crucial for improving learning processes. STEM learning through e-learning will have a greater chance of success if academic institutions collaborate with other sectors of society, such as the business sector, to receive feedback for the continuous improvement of the proposed teaching methods.

This paper presents a First-Order Recurrent Neural Network activated by a wavelet function, in particular a Morlet wavelet, with a fixed set of parameters and capable of identifying multiple chaotic systems. By maintaining a fixed structure for the neural network and using the same activation function, the network can successfully identify the three state variables of several different chaotic systems, including the Chua, PWL-Rössler, Anishchenko–Astakhov, Álvarez-Curiel, Aizawa, and Rucklidge models. The performance of this approach was validated by numerical simulations in which the accuracy of the state estimation was evaluated using the Mean Square Error (MSE) and the coefficient of determination (r2), which indicates how well the neural network identifies the behavior of the individual oscillators. In contrast to the methods found in the literature, where a neural network is optimized to identify a single system and its application to another model requires recalibration of the neural algorithm parameters, the proposed model uses a fixed set of parameters to efficiently identify seven chaotic systems. These results build on previously published work by the authors and advance the development of robust and generic neural network structures for the identification of multiple chaotic oscillators.

Operation Sequencing (OS) is one of the most critical tasks in a CAPP system. This process could be modelled as a combinatorial problem where finding a suitable solution within a reasonable time interval is difficult. This work implements a novel Discrete Differential Evolution Algorithm (DDEA) to solve the OS problem, focusing on parts of up to 76 machining operations; the relationships among operations are represented as a directed graph; the contributions of the DDEA are as follows: (1) operates with a discrete representation in the space of feasible solutions; (2) employs mutation and crossover operators to update solutions and to reduce machining and setup costs, (3) possess a local search strategy to achieve better solutions, and (4) integrates a statistical method based on quantiles to measure the quality and likelihood for an achieving a solution. To demonstrate the efficiency and robustness of the DDEA, five prismatic parts with different numbers of machining operations as benchmarks to address the OS problem were selected. The results generated the same OS for parts with a few machining operations (up to 23 machining operations). Conversely, for parts with more machining operations, the DDEA needs more runs to achieve the best solution.