Transaxillary augmentation mammoplasty with videoendoscopy assistence using silicone endoprosthesis in patients with hypoplastic and dysmorphic breast

Clinica de chirurgie estetică “Neogen”, Chirurgie plastică şi estetică, Chişinău, Republica Moldova, Al XII-lea Congres al Asociației Chirurgilor „Nicolae Anestiadi” din Republica Moldova cu participare internațională 23-25 septembrie 2015Introducere: În studiu sunt reflectate rezultatele cercetării privind alegerea metodei chirurgicale optimale în corecţia hipoplaziei, asimetriei şi dismorfiei glandei mamare. Metoda propusă în acest studiu, oferă avantajele specifice chirurgiei endoscopice miniminvazive. A fost apreciată eficacitatea metodei chirurgicale videoendoscopice în plastia glandei mamare. Au fost evaluate și determinate opțiunile tehnice pentru aplicarea acestei metode. Scopul studiului: Optimizarea tehnicilor chirurgicale privind protezarea glandei mamare în corecţia hipoplaziei, asimetriei şi dismorfiei involutive a glandei mamare. Material şi metode: Studiul este bazat pe analiza rezultatelor intervenţiilor chirurgicale destinate plastiei glandei mamare prin metoda endoscopică transaxilară, cu utilizarea protezelor mamare. Rezultate: Metoda a fost aplicată la 29 de paciente cu vârsta cuprinsă între 19-35 de ani, care s-au adresat în Clinică, în perioada anilor 2012-2015, pentru corecţia hipoplaziei sau dismorfiei involutive a glandei mamare. S-a demonstrat eficacitatea clinică înaltă a metodei evaluate. Concluzii: Metoda chirurgicală videoendoscopică în plastia glandei mamare asigură corecţia estetică scontată.Introduction: The paper presents results of a study on the optimal surgical techniques for breast augmentation, by appropriate development trends of modern medicine. The necessity of this operation occurs in patients with severe hypoplasia of breast, asymmetry after involute changes. This surgical technique offers a full range of benefits of endoscopic surgery, with its minimally invasive characteristics. Aim of study: To optimize surgical techniques of augmentation mammoplasty using silicone implants in patients with hypoplasia and dysmorphia of the mammary glands. Material and methods: The study is based on analysis of the results of augmentation mammoplasty using silicone implants. Results: Method was used in 29 patients, aged 19 to 35 years, who applied to our clinic between 2012 and 2014, with the purpose of breast augmentation reconstruction. The results indicate the high efficiency of endoscopic augmentation mammoplasty in the correction of hypoplastic and dysmorphic shape of mammary gland. Conclusions: Videoendoscopic method in plastic surgery provides expected aesthetic correction of hypoplastic and dysmorphic breasts

Promoting Teaching Practices in IT Higher Education

© 2020 Owner/Author. Lecture-based classes became an old strategy of teaching even in higher education. Education now focuses on student-centered strategies that actively engage students in their learning and how teacher can design classes to facilitate learning process. This paper presents some practices for teaching in higher education

Message from AICCSA\u272022 Program Chairs

Welcome to the 19th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA\u272022), organized from December 5-7, 2022 in Zayed University- Abu Dhabi, United Arab Emirates (UAE). As co-chairs of the Program Committee (PC), we are delighted to introduce this year\u27s technical program and proceedings of AICCSA\u272022

Selectividad de las redes de enmalle para Solea solea (Osteichthyes: Soleidae) en el Mar Adriático

Sole juveniles concentrate along the western Adriatic coast where they are targeted from spring to autumn by small-scale gill netters. As in spring–early summer 20 to 30% of catch biomass consists of individuals smaller than MLS (TL = 20 cm), the selectivity of sole gill nets was investigated in 2004-2005 in order to obtain useful information for developing management measures aimed at reducing the retention of undersized specimens and assuring the sustainability of this fishery. Twenty-eight fishing trips were performed using sole gill nets with 5 mesh openings (64.2, 65.2, 67.8, 70.2 and 71.8 mm) simultaneously. The gill net selectivity was estimated indirectly by applying the SELECT method and between-set analysis. The log-normal curve was found to fit single set data better than other models. The catch yields did not significantly decrease with the increment of mesh size: the biomass of undersized individuals in catches noticeably decreased from 16% down to 9% in the largest mesh size, whilst the sole that were longer than the MLS increased proportionally. In view of the lower economic value of smaller specimens with respect to the larger ones, adopting the 71.8 mm mesh represents a good compromise between the need to protect juveniles and the economic profit of gill netters.Los juveniles de lenguado se concentran a lo largo de la costa oeste adriática donde son capturados desde la primavera hasta el verano con redes de enmalle de tamaño pequeño. En primavera y principios del verano el 20-30% de la biomasa capturada corresponde a individuos menores a la talla mínima legal (longitud total de 20 cm). Este trabajo ha estudiado la selectividad de las redes de enmalle para el lenguado durante 2004-2005 con el objetivo de obtener suficiente información para desarrollar medidas de gestión que reduzcan la retención de individuos demasiado pequeños y asegurar así la sostenibilidad de la pesquería. Durante este estudio se realizaron un total de veintiocho muestreos pesqueros usando simultáneamente redes de enmalle con cinco luces de malla distintas (64.2, 65.2, 67.8, 70.2 y 71.8 mm). La selectividad de la red de enmalle fue estimada indirectamente aplicando el método SELECT. El modelo que se ajustó mejor a los datos fue una curva de tipo log-normal. La captura no descendió significativamente con el incremento de la luz de malla. La biomasa de los individuos pequeños en las capturas bajó notablemente de 16% a 9% con el uso de la red de mayor luz de malla, mientras que los lenguados con mayor tamaño aumentaron proporcionalmente. Como los individuos menores tienen también un valor económico menor, el uso de la red de 71.8 mm de luz de malla es un buen compromiso entre la protección de los juveniles y la obtención de beneficios económicos para los pescadores que usan las redes de enmalle

CropWaterNeed: A Machine Learning Approach for Smart Agriculture

In this paper, we propose an approach CropWaterNeed in order to estimate and predict the future water needs and maximize the productivity in the irrigated areas. Unfortunately, we have not identified data available to be employed in such machine learning process in order to predict plants water needs. The proposed approach consists of extending the classic machine learning process. Particularly, we define a process to build dataset that contains plant water requirements. To collect data, we extract meteorological data from Climwat database and plants water requirements using Cropwat Tool. Then, we aggregate the extracted data into a dataset. Subsequently, we use the dataset to perform the learning process using XGBRegressor, Decision Tree, Random Forest and Gradiant Boost Regressor. Afterward, we evaluate the model generated by each algorithm by measuring the performance measures such as MSE, RMSE and MAE. Our work shows that the model generated by XGBRegressor is the most efficient in our case while Random Forest is the least efficient. As future work, we aim to apply the proposed process to test the performance of other regression algorithms and to test the impact of using deep learning techniques with the extracted data

Towards a Modular Ontology for Cloud Consumer Review Mining

Nowadays, online consumer reviews are used to enhance the effectiveness of finding useful product information that impacts the consumers’ decision-making process. Many studies have been proposed to analyze these reviews for many purposes, such as opinion-based recommendation, spam review detection, opinion leader analysis, etc. A standard model that presents the different aspects of online review (review, product/service, user) is needed to facilitate the review analysis task. This research suggests SOPA, a modular ontology for cloud Service OPinion Analysis. SOPA represents the content of a product/service and its related opinions extracted from the online reviews written in a specific context. The SOPA is evaluated and validated using cloud consumer reviews from social media and using quality metrics. The experiments revealed that the SOPA-related modules exhibit a high cohesion and a low coupling, besides their usefulness and applicability in real use case studies

Towards an explainable irrigation scheduling approach by predicting soil moisture and evapotranspiration via multi-target regression

Significant population growth and ongoing socioeconomic development have increased reliance on irrigated agriculture and agricultural intensification. However, accurately predicting crop water demand is problematic since it is affected by several factors such as weather, soil, and water properties. Many studies have shown that a hybrid irrigation system based on two irrigation strategies (i.e., evapotranspiration and soil-based irrigation) can provide a credible and reliable irrigation system. The latter can also alert farmers and other experts to phenomena such as noise, erroneous sensor signals, numerous correlated input and target variables, and incomplete or missing data, especially when the two irrigation strategies produce inconsistent results. Hence, we propose Multi-Target soil moisture and evapotranspiration prediction (MTR-SMET) for estimating soil moisture and evapotranspiration. These predictions are then used to compute water needs based on Food and Agriculture Organization (FAO) and soil-based methods. Besides, we propose an explainable MTR-SMET (xMTR-SMET) that explains the ML-based irrigation to the farmers/users using several explainable AI to provide simple visual explanations for the given predictions. It is the first attempt that explains and offers meaningful insights into the output of a machine learning-based irrigation approach. The conducted experiments showed that the proposed MTR-SMET model achieves low error rates (i.e., MSE = 0.00015, RMSE = 0.0039, MAE = 0.002) and high R 2 score (i.e., 0.9676)

A machine learning-based approach for smart agriculture via stacking-based ensemble learning and feature selection methods

Smart irrigation has many advantages in optimizing resource usage (e.g., saving water, reducing energy consumption) and improving crop productivity. In this paper, we contribute to this field by proposing a robust and accurate machine learning-based approach that combines the power of feature selection methods and stacking ensemble method to effectively determine the optimal quantity of water needed for a plant. Random Forest, Recursive Feature Elimination (RFE), and SelectKBest are used to assess the importance of the features. Then, based on the best subset of features, a stacking ensemble model is proposed that combines CART, Gradient Boost Regression (GBR), Random Forest (RF) and XGBoost regressors. The different models involved in this approach are trained and tested using a collected dataset about various crops such as tomatoes, grapes, and lemon and encompasses different features such as meteorological data, soil data, irrigation data, and crop data. The experiments demonstrated the performance of RF in analyzing the feature importance. The findings of feature selection highlight the importance level of the evapotranspiration, the depletion, and the deficit to maximize the model’s accuracy. The results also showed that the proposed stacking model (Stacking_GBR+CART+RF+XGB) with the 10 most essential features outperforms individual models and other stacking models by achieving low error rates (i.e., MSE=0.0026, MAE=0.0279, RMSE=0.0509) and high R2 score (i.e., 0.9927)