Delirium como síntoma de presentación atípico de lupus eritematoso sistémico

Systemic lupus erythematosus is a multisystem autoimmune disease that can affect the central and peripheral nervous system in the form of neuropsychiatric lupus. Up to 95% of patients will be able to develop neuropsychiatric lupus at any time of the disease. Multiple factors (autoantibodies, microvascular disease and proinflammatory cytokines) have been associated with its pathogenesis. The diagnosis of neuropsychiatric lupus poses a challenge for the clinician, since the diagnosis will only be achieved once other causes have been ruled out. The management of this pathology is based on the symptomatic treatment associated or not with immunosuppressants.El lupus eritematoso sistémico (LES) es una enfermedad autoinmune multisistémica que puede afectar al sistema nervioso central (SNC) y al sistema nervioso periférico (SNP) en forma de lupus neuropsiquiátrico (LES-NP). Hasta el 95% de los pacientes podrán desarrollar LES-NP en cualquier momento de la enfermedad. Múltiples factores (autoanticuerpos, microvasculopatía y citocinas proinflamatorias) se han asociado con su patogenia. El diagnóstico de LES-NP supone un reto para el clínico, ya que sólo se conseguirá el diagnóstico una vez que se hayan descartado otras causas. El manejo de esta patología se basa en el tratamiento sintomático asociado o no a inmunosupresores

Gross Solids Content Prediction in Urban WWTPs Using SVM

The preliminary treatment of wastewater at wastewater treatment plants (WWTPs) is of great importance for the performance and durability of these plants. One fraction that is removed at this initial stage is commonly called gross solids and can cause various operational, downstream performance, or maintenance problems. To avoid this, data from more than two operation years of the Villapérez Wastewater Treatment Plant, located in the northeast of the city of Oviedo (Asturias, Spain), were collected and used to develop a model that predicts the gross solids content that reaches the plant. The support vector machine (SVM) method was used for modelling. The achieved model precision (Radj2 = 0.7 and MSE = 0.43) allows early detection of trend changes in the arrival of gross solids and will improve plant operations by avoiding blockages and overflows. The results obtained indicate that it is possible to predict trend changes in gross solids content as a function of the selected input variables. This will prevent the plant from suffering possible operational problems or discharges of untreated wastewater as actions could be taken, such as starting up more pretreatment lines or emptying the containers

A Random Forest Model for the Prediction of FOG Content in Inlet Wastewater from Urban WWTPs

The content of fats, oils, and greases (FOG) in wastewater, as a result of food preparation, both in homes and in different commercial and industrial activities, is a growing problem. In addition to the blockages generated in the sanitary networks, it also represents a difficulty for the performance of wastewater treatment plants (WWTP), increasing energy and maintenance costs and worsening the performance of downstream treatment processes. The pretreatment stage of these facilities is responsible for removing most of the FOG to avoid these problems. However, so far, optimization has been limited to the correct design and initial installation dimensioning. Proper management of this initial stage is left to the experience of the operators to adjust the process when changes occur in the characteristics of the wastewater inlet. The main difficulty is the large number of factors influencing these changes. In this work, a prediction model of the FOG content in the inlet water is presented. The model is capable of correctly predicting 98.45% of the cases in training and 72.73% in testing, with a relative error of 10%. It was developed using random forest (RF) and the good results obtained (R2 = 0.9348 and RMSE = 0.089 in test) will make it possible to improve operations in this initial stage. The good features of this machine learning algorithm had not been used, so far, in the modeling of pretreatment parameters. This novel approach will result in a global improvement in the performance of this type of facility allowing early adoption of adjustments to the pretreatment process to remove the maximum amount of FOG

Determination of water depth in ports using satellite data based on machine learning algorithms

One of the fundamental maintenance tasks of ports is the periodic dredging of them. This is necessary to guarantee a minimum draft that will enable ships to access ports safely. The determination of bathymetries is the instrument that determines the need for dredging and permits an analysis of the behavior of the port bottom over time, in order to achieve adequate water depth. Satellite data processing to predict environmental parameters is used increasingly. Based on satellite data and using different machine learning algorithm techniques, this study has sought to estimate the seabed in ports, taking into account the fact that the port areas are strongly anthropized areas. The algorithms that were used were Support Vector Machine (SVM), Random Forest (RF) and the Multi-Adaptive Regression Splines (MARS). The study was carried out in the ports of Candás and Luarca in the Principality of Asturias. In order to validate the results obtained, data was acquired in situ by using a single beam provided. The results show that this type of methodology can be used to estimate coastal bathymetry. However, when deciding which system was best, priority was given to simplicity and robustness. The results of the SVM and RF algorithms outperform those of the MARS. RF performs better in Candás with a mean absolute error (MAE) of 0.27 cm, whereas SVM performs better in Luarca with a mean absolute error of 0.37 cm. It is suggested that this approach is suitable as a simpler and more cost-effective rough resolution alternative, for estimating the depth of turbid water in ports, than single-beam sonar, which is labor-intensive and polluting

Gross Solids Content Prediction in Urban WWTPs Using SVM

The preliminary treatment of wastewater at wastewater treatment plants (WWTPs) is of great importance for the performance and durability of these plants. One fraction that is removed at this initial stage is commonly called gross solids and can cause various operational, downstream performance, or maintenance problems. To avoid this, data from more than two operation years of the Villapérez Wastewater Treatment Plant, located in the northeast of the city of Oviedo (Asturias, Spain), were collected and used to develop a model that predicts the gross solids content that reaches the plant. The support vector machine (SVM) method was used for modelling. The achieved model precision (Radj2 = 0.7 and MSE = 0.43) allows early detection of trend changes in the arrival of gross solids and will improve plant operations by avoiding blockages and overflows. The results obtained indicate that it is possible to predict trend changes in gross solids content as a function of the selected input variables. This will prevent the plant from suffering possible operational problems or discharges of untreated wastewater as actions could be taken, such as starting up more pretreatment lines or emptying the containers

Sand Content Prediction in Urban WWTPs Using MARS

The pre-treatment stage of wastewater treatment plants (WWTP), where most of the larger waste, including sand and fat, is removed, is of great importance for the performance and durability of these plants. This work develops a model that predicts the sand content that reaches the plant. For this purpose, data were collected from one operation year of the Villapérez Wastewater Treatment Plant located in the northeast of the city of Oviedo (Asturias, Spain) and the MARS (Multivariate Adaptive Regression Splines) method was used for modelling. The accuracy of the MARS model developed using the determination coefficient is R2 = 0.74 for training data and R2 = 0.70 in validation data. These results indicate that it is possible to predict trend changes in sand production as a function of input variables changes such as flow rate, pH, ammonia, etc. This will prevent the plant from possible operational problems, as actions could be taken, such as starting up more pre-treatment lines or emptying the containers, so that the arrival of the sand can be assumed without any problem. In this way, the possibility of letting sand contents over the established limits pass that could affect the following processes of the treatment plant is avoided

Análisis de las habilidades socio-profesionales de los estudiantes de Trabajo Social de la Universidad de Alicante

Son las personas las que construyen la realidad social. Esta interacción requiere de habilidades profesionales, pero, por ende de habilidades sociales que el individuo va adquiriendo a lo largo de su vida (Nuñez del Arco, 2005). El objetivo del título de Grado en Trabajo Social es formar a profesionales de la intervención social en metodologías de intervención del Trabajo Social. El plan de estudios debe permitir la adquisición de los conocimientos y competencias necesarias para desarrollar el ejercicio profesional. Se ha de concebir el Trabajo Social como un intento de mejora ecuánime de la sociedad. La finalidad de la investigación es conocer si el actual plan de la titulación se ajusta a las necesidades de aprendizaje de los estudiantes de cara a la adquisición y desarrollo de las habilidades profesionales requeridas para el desempeño de sus funciones. Se pretende conocer las habilidades socio-profesionales de los estudiantes de Trabajo Social de la Universidad de Alicante mediante la realización de un estudio comparativo del nivel de desarrollo de dichas habilidades en cada uno de los semestres de la titulación. Así, se analizará el impacto de la formación sobre la adquisición y evolución de las habilidades de nuestros estudiantes

Análisis de las habilidades socio-profesionales de los estudiantes de Trabajo Social de la Universidad de Alicante

Son las personas las que construyen la realidad social. Esta interacción requiere de habilidades profesionales, pero, por ende de habilidades sociales que el individuo va adquiriendo a lo largo de su vida (Nuñez del Arco, 2005). El objetivo del título de Grado en Trabajo Social es formar a profesionales de la intervención social en metodologías de intervención del Trabajo Social. El plan de estudios debe permitir la adquisición de los conocimientos y competencias necesarias para desarrollar el ejercicio profesional. Se ha de concebir el Trabajo Social como un intento de mejora ecuánime de la sociedad. La finalidad de la investigación es conocer si el actual plan de la titulación se ajusta a las necesidades de aprendizaje de los estudiantes de cara a la adquisición y desarrollo de las habilidades profesionales requeridas para el desempeño de sus funciones. Se pretende conocer las habilidades socio-profesionales de los estudiantes de Trabajo Social de la Universidad de Alicante mediante la realización de un estudio comparativo del nivel de desarrollo de dichas habilidades en cada uno de los semestres de la titulación. Así, se analizará el impacto de la formación sobre la adquisición y evolución de las habilidades de nuestros estudiantes