Optimization of a Single Flash Geothermal Power Plant Powered by a Trans-Critical Carbon Dioxide Cycle Using Genetic Algorithm and Nelder-Mead Simplex Method

The usage of renewable energies,including geothermal energy,is expanding rapidly worldwide.The low efficiency of geothermal cycles has consistently highlighted the importance of recovering heat loss for these cycles.This paper proposes a combined power generation cycle(single flash geothermal cycle with trans-critical CO_(2) cycle)and simulates in the EES(Engineering Equation Solver)software.The results show that the design parameters of the proposed system are significantly improved compared to the BASIC single flash cycle.Then,the proposed approach is optimized using the genetic algorithm and the Nelder-Mead Simplex method.Separator pressure,steam turbine output pressure,and CO_(2) turbine inlet pressure are three assumed variable parameters,and exergy efficiency is the target parameter.In the default operating mode,the system exergy efficiency was 32%,increasing to 39%using the genetic algorithm and 37%using the Nelder-Mead method.

Support Vector Machines for Regression: A Succinct Review of Large-Scale and Linear Programming Formulations

Support Vector-based learning methods are an important part of Computational Intelligence techniques. Recent efforts have been dealing with the problem of learning from very large datasets. This paper reviews the most commonly used formulations of support vector machines for regression (SVRs) aiming to emphasize its usability on large-scale applications. We review the general concept of support vector machines (SVMs), address the state-of-the-art on training methods SVMs, and explain the fundamental principle of SVRs. The most common learning methods for SVRs are introduced and linear programming-based SVR formulations are explained emphasizing its suitability for large-scale learning. Finally, this paper also discusses some open problems and current trends.

Hyalinizing clear cell carcinoma-a rare entity in the oral cavity: A case report

BACKGROUND Hyalinizing clear cell carcinoma(HCCC)is an uncommon tumor that originates in the salivary glands.This neoplasia constitutes less than 1%of minor salivary gland tumors.CASE SUMMARY A 67-year-old female visited the maxillofacial surgery department owing to a smooth,slightly yellowish protruding mass on the left side of the floor of the mouth,at the level of the molars;the tumor mass had a soft consistency on palpation and did not adhere to deep planes.The microscopical analysis of the excisional biopsy showed that the lesion was composed of sheets and cords of clear cells separated by thick eosinophilic bands of hyaline collagen.Normal glandular tissue was absent,periodic acid-Schiff with and without diastase stains,and immunohistochemical reactions were performed to confirm the diagnosis.This is the second case reported in the literature of HCCC arising in the floor of the mouth.CONCLUSION HCCC is a rare salivary gland tumor that has not been studied extensively.Its diagnosis is usually challenging,because clinically,it can be confused with a benign neoplasm.

Forecasting the Demand of Short-Term Electric Power Load with Large-Scale LP-SVR

This research studies short-term electricity load prediction with a large-scalelinear programming support vector regression (LP-SVR) model. The LP-SVR is compared with other three non-linear regression models: Collobert’s SVR, Feed-Forward Neural Networks (FFNN), and Bagged Regression Trees (BRT). The four models are trained to predict hourly day-ahead loads given temperature predictions, holiday information and historical loads. The models are trained on-hourly data from the New England Power Pool (NEPOOL) region from 2004 to 2007 and tested on out-of-sample data from 2008. Experimental results indicate that the proposed LP-SVR method gives the smallest error when compared against the other approaches. The LP-SVR shows a mean absolute percent error of 1.58% while the FFNN approach has a 1.61%. Similarly, the FFNN method shows a 330 MWh (Megawatts-hour) mean absolute error, whereas the LP-SVR approach gives a 238 MWh mean absolute error. This is a significant difference in terms of the extra power that would need to be produced if FFNN was used. The proposed LP-SVR model can be utilized for predicting power loads to a very low error, and it is comparable to FFNN and over-performs other state of the art methods such as: Bagged Regression Trees, and Large-Scale SVRs.

Stem cells as an option for the treatment of COVID-19

The application of stem cells is among the many strategies currently available for the treatment of multiple diseases.Stem cells are characterized as undifferentiated cells that have the ability to differentiate towards multiple lineages and selfrenewal,among other attributes.Since the first umbilical cord stem cell transplant for the treatment of Fanconi anemia,the use of stem cells for the treatment of multiple diseases,including coronavirus disease 2019,has increased,showing promising results that require evaluation through research studies that include a longer follow-up time.Therefore,the main objective of this Letter is to provide an update on the use of stem cells in the treatment of severe acute respiratory syndrome coronavirus 2,as well as identify the main challenges and limitations presented by this type of therapy.

Primary maxillary chondrosarcoma: A case report

BACKGROUND Sarcomas of the head and neck region are rare tumors,constituting less than 1%of malignant neoplasms in this area,of which few cases(20%)originate from bone or cartilage.Chondrosarcoma is a malignant neoplasm that develops in bone,with a predilection for the pelvis,chest wall,and scapula,and is uncommon in the maxilla and jaw.Although this type of lesion has locally aggressive behavior,destroying the affected bone,it can metastasize when it is not diagnosed early and compromise the patient's life.CASE SUMMARY On intraoral examination of a 32-year-old female with a tumor in the middle third of the face,a well-defined rise in volume of approximately 3 cm in diameter was observed.Computed tomography with 3-dimensional reconstruction was performed,and we observed that the osteolytic lesion affected the vestibular cortex as the palatal bone.Hematoxylin and eosin staining revealed an appearance that was similar to mature hyaline cartilage,hypercellularity,nuclear and cellular pleomorphism,and multinucleated cells,with significant vacuolization.CONCLUSION Determination of the clinical and histopathological characteristics of rare neoplasms in the maxillofacial region,such as chondrosarcomas,allows the pathologist and surgeon to make the appropriate therapeutic decisions,optimizing the patient’s prognosis.

Analysis of the Temporal and Spatial Evolution of Recovery and Degradation Processes in Vegetated Areas Using a Time Series of Landsat TM Images (1986-2011): Central Region of Chihuahua, Mexico

This paper analyzed the temporal and spatial evolution of vegetation dynamics in various land covers in the basin of the Laguna Bustillos, Region of Cuauhtémoc, Chihuahua, Mexico. We used an NDVI time series for the months of March to April (early spring). The series was constructed from Landsat TM images for the period 1986-2011. The results show an increase of NDVI for vegetated areas, especially in conifer cover, while shrub and grassland showed a positive trend but with lower statistical significance. The increase in minimum temperatures in early spring, during the study period, was the most important factor in explaining the increase of NDVI in vegetated areas. A spatially distributed analysis shows large areas without an NDVI trend, corresponding to areas with sparse vegetation cover (degraded areas). Moreover, there are also areas with a negative trend (loss of vegetation), explained by the exploitation of trees to produce firewood which is mainly carried out by the ejidos in the region. These results help to focus human and financial resources in places where the benefit will be greatest.