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Yadav, Shital Sanjay, and Anup S. Vibhute. "Emotion Detection Using Deep Learning Algorithm." International Journal of Computer Vision and Image Processing 11, no. 4 (October 2021): 30–38. http://dx.doi.org/10.4018/ijcvip.2021100103.
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Automatic emotion detection is a prime task in computerized human behaviour analysis. The proposed system is an automatic emotion detection using convolution neural network. The proposed end-to-end CNN is therefore named as ENet. Keeping in mind the computational efficiency, the deep network makes use of trained weight parameters of the MobileNet to initialize the weight parameters of ENet. On top of the last convolution layer of ENet, the authors place global average pooling layer to make it independent of the input image size. The ENet is validated for emotion detection using two benchmark datasets: Cohn-Kanade+ (CK+) and Japanese female facial expression (JAFFE). The experimental results show that the proposed ENet outperforms the other existing methods for emotion detection.
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Fu, Guang-Hui, Min-Jie Zong, Feng-Hua Wang, and Lun-Zhao Yi. "A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data." International Journal of Analytical Chemistry 2019 (August 1, 2019): 1–12. http://dx.doi.org/10.1155/2019/7314916.
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Elastic net (Enet) and sparse partial least squares (SPLS) are frequently employed for wavelength selection and model calibration in analysis of near infrared spectroscopy data. Enet and SPLS can perform variable selection and model calibration simultaneously. And they also tend to select wavelength intervals rather than individual wavelengths when the predictors are multicollinear. In this paper, we focus on comparison of Enet and SPLS in interval wavelength selection and model calibration for near infrared spectroscopy data. The results from both simulation and real spectroscopy data show that Enet method tends to select less predictors as key variables than SPLS; thus it gets more parsimony model and brings advantages for model interpretation. SPLS can obtain much lower mean square of prediction error (MSE) than Enet. So SPLS is more suitable when the attention is to get better model fitting accuracy. The above conclusion is still held when coming to performing the strongly correlated NIR spectroscopy data whose predictors present group structures, Enet exhibits more sparse property than SPLS, and the selected predictors (wavelengths) are segmentally successive.
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Hendricks, M. "ENet President's Problem." American Journal of Evaluation 6, no. 1 (January 1, 1985): 56–57. http://dx.doi.org/10.1177/109821408500600115.
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Comelli, Albert, Navdeep Dahiya, Alessandro Stefano, Federica Vernuccio, Marzia Portoghese, Giuseppe Cutaia, Alberto Bruno, Giuseppe Salvaggio, and Anthony Yezzi. "Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging." Applied Sciences 11, no. 2 (January 15, 2021): 782. http://dx.doi.org/10.3390/app11020782.
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Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.
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Katzenmeyer, Conrad. "Final ENet board meeting." Evaluation Practice 7, no. 1 (February 1986): 106. http://dx.doi.org/10.1016/s0886-1633(86)80085-x.
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Katzenmeyer, C. "Final ENet Board Meeting." American Journal of Evaluation 7, no. 1 (February 1, 1986): 106. http://dx.doi.org/10.1177/109821408600700117.
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Jabbour, Georges, Melanie Henderson, Angelo Tremblay, and Marie Eve Mathieu. "Aerobic Fitness Indices of Children Differed Not by Body Weight Status but by Level of Engagement in Physical Activity." Journal of Physical Activity and Health 12, no. 6 (June 2015): 854–60. http://dx.doi.org/10.1123/jpah.2013-0337.
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Objective:Moderate-to-vigorous physical activity (MVPA) improves aerobic fitness in children, which is usually assessed by maximal oxygen consumption. However, other indices of aerobic fitness have been understudied.Methods:To compare net oxygen (VO2net), net energy consumption (Enet), net mechanical efficiency (MEnet), and lipid oxidation rate in active and inactive children across body weight statuses.Design:The sample included normal-weight, overweight, and obese children of whom 44 are active (≥30 min of MVPA/d) and 41 are inactive (<30 min of MVPA/d). VO2net, Enet, MEnet and lipid oxidation rate were determined during an incremental maximal cycling test.Results:Active obese participants had significantly lower values of VO2net and Enet and higher MEnet than inactive obese participants at all load stages. In addition, active obese participants showed a significantly higher lipid oxidation rate compared with inactive obese and active overweight and normal-weight participants. VO2net, Enet, and MEnet were similar across active children, regardless of body weight status.Conclusion:Thirty minutes or more of MVPA per day is associated with a potentiation of aerobic fitness indicators in obese prepubertal children. Moreover, the indices of aerobic fitness of inactive obese children are significantly different from those of active obese and nonobese ones.
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TASSIGNON, MJ. "EBO, ENET and accredited courses." Acta Ophthalmologica 87 (September 2009): 0. http://dx.doi.org/10.1111/j.1755-3768.2009.2372.x.
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Silva, Maguino Santos da, Francisco José Gondim Pitanga, Jorge Medeiros Gomes, Úrsula Paula Renó Soci, and Alex Cleber Improta Caria. "Eletroestimulação e treinamento físico: uma revisão narrativa." Research, Society and Development 9, no. 12 (December 30, 2020): e4691211528. http://dx.doi.org/10.33448/rsd-v9i12.11528.
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A Eletroestimulação é uma técnica utilizada por profissionais da área da saúde em tratamentos para diversas doenças e treinamentos esportivos. Pode ser aplicada em academias de ginástica, clínicas de fisioterapia e ambientes esportivos como clubes de futebol, corridas, entre outros. Existem diversas nomenclaturas no mercado relacionando a eletroestimulação, mas todas elas são tecnicamente conhecidas como estimulação neuromuscular elétrica transcutânea (ENET). A ENET de corpo inteiro é uma técnica relativamente nova, e entrou no mercado para potencializar o tratamento e treinamento que já eram utilizados por estimuladores elétricos locais, como a corrente russa, galvânica e outras. Os resultados dos estudos relacionados ao tema divergem bastante, portanto, percebe-se a necessidade de mais pesquisas relacionadas ao tema. Assim, o objetivo da presente revisão é, identificar e discutir os efeitos benéficos da ENET no tratamento de doenças cardiovasculares e neuromusculares, inclusive no auxílio do treinamento físico convencional e os potenciais riscos para saúde humana.
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Stefano, Alessandro, and Albert Comelli. "Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images." Journal of Imaging 7, no. 8 (August 4, 2021): 131. http://dx.doi.org/10.3390/jimaging7080131.
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Background: In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based prediction model. In this context, we present a customized deep learning approach aimed at addressing the real-time, and fully automated identification and segmentation of COVID-19 infected regions in computed tomography images. Methods: In a previous study, we adopted ENET, originally used for image segmentation tasks in self-driving cars, for whole parenchyma segmentation in patients with idiopathic pulmonary fibrosis which has several similarities to COVID-19 disease. To automatically identify and segment COVID-19 infected areas, a customized ENET, namely C-ENET, was implemented and its performance compared to the original ENET and some state-of-the-art deep learning architectures. Results: The experimental results demonstrate the effectiveness of our approach. Considering the performance obtained in terms of similarity of the result of the segmentation to the gold standard (dice similarity coefficient ~75%), our proposed methodology can be used for the identification and delineation of COVID-19 infected areas without any supervision of a radiologist, in order to obtain a volume of interest independent from the user. Conclusions: We demonstrated that the proposed customized deep learning model can be applied to rapidly identify, and segment COVID-19 infected regions to subsequently extract useful information for assessing disease severity through radiomics analyses.
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Leblanc, N., X. Wan, and P. M. Leung. "Physiological role of Ca(2+)-activated and voltage-dependent K+ currents in rabbit coronary myocytes." American Journal of Physiology-Cell Physiology 266, no. 6 (June 1, 1994): C1523—C1537. http://dx.doi.org/10.1152/ajpcell.1994.266.6.c1523.
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The properties and function of Ca(2+)-activated K+ (KCa) and voltage-dependent K+ (IK) currents of rabbit coronary myocytes were studied under whole cell voltage-clamp conditions (22 degrees C). Inhibition of KCa by tetraethylammonium chloride (1-10 mM) or charybdotoxin (50-100 nM) suppressed noisy outward rectifying current elicited by 5-s voltage steps or ramp at potentials > 0 mV, reduced the hump of the biphasic ramp current-voltage relation, and shifted by less than +5 mV the potential at which no net steady-state current is recorded (Enet; index of resting membrane potential). Inhibition of steady-state inward Ca2+ currents [ICa(L)] by nifedipine (1 microM) displaced Enet by -11 mV. Analysis of steady-state voltage dependence of IK supported the existence of a "window" current between -50 and 0 mV. 4-Aminopyridine (2 mM) blocked a noninactivating component of IK evoked between -30 and -40 mV, abolished the hump current during ramps, and shifted Enet by more than +15 mV; hump current persisted during 2-min ramp depolarizations and peaked near the maximum overlap of the steady-state activation and inactivation curves of IK (about -22 mV). A threefold rise in extracellular Ca2+ concentration (1.8-5.4 mM) enhanced time-dependent outward K+ current (6.7-fold at +40 mV) and shifted Enet by -30 mV. It is concluded that, under steady-state conditions, IK and ICa(L) play a major role in regulating resting membrane potential at a physiological level of intracellular Ca2+ concentration, with a minor contribution from KCa. However, elevation of intracellular Ca2+ concentration enhances KCa and hyperpolarizes the myocyte to limit Ca2+ entry through ICa(L).
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Wiedenmann, B. "From ENET to ENETS: A Long Odyssey in the Land of Small and Rare Tumors." Neuroendocrinology 80, no. 1 (2004): 1–2. http://dx.doi.org/10.1159/000080730.
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Kim, Sangwon, Jaeyeal Nam, and Byoungchul Ko. "Fast Depth Estimation in a Single Image Using Lightweight Efficient Neural Network." Sensors 19, no. 20 (October 13, 2019): 4434. http://dx.doi.org/10.3390/s19204434.
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Depth estimation is a crucial and fundamental problem in the computer vision field. Conventional methods re-construct scenes using feature points extracted from multiple images; however, these approaches require multiple images and thus are not easily implemented in various real-time applications. Moreover, the special equipment required by hardware-based approaches using 3D sensors is expensive. Therefore, software-based methods for estimating depth from a single image using machine learning or deep learning are emerging as new alternatives. In this paper, we propose an algorithm that generates a depth map in real time using a single image and an optimized lightweight efficient neural network (L-ENet) algorithm instead of physical equipment, such as an infrared sensor or multi-view camera. Because depth values have a continuous nature and can produce locally ambiguous results, pixel-wise prediction with ordinal depth range classification was applied in this study. In addition, in our method various convolution techniques are applied to extract a dense feature map, and the number of parameters is greatly reduced by reducing the network layer. By using the proposed L-ENet algorithm, an accurate depth map can be generated from a single image quickly and, in a comparison with the ground truth, we can produce depth values closer to those of the ground truth with small errors. Experiments confirmed that the proposed L-ENet can achieve a significantly improved estimation performance over the state-of-the-art algorithms in depth estimation based on a single image.
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Chen, Feng Qin. "Research on the Network Safety Loophole of Enet." Applied Mechanics and Materials 687-691 (November 2014): 1942–44. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1942.
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With the continuous development and popularization of computer network information technology, many colleges all have set up campus network. At present, campus network has been the important project in college base installation. However, information security issue of campus network would directly affect college to launch various pedagogical practices and refer to students’ individual privacy security issue. This paper has pointed out many safety loopholes existing in university campus network, and attempted to explore improvement measure and corresponding countermeasure of domestic university campus network information security.
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Gburzynski, P., and P. Rudnicki. "A note on the performance of ENET II." IEEE Journal on Selected Areas in Communications 7, no. 3 (April 1989): 424–27. http://dx.doi.org/10.1109/49.16875.
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Kayanan, Manickavasagar, and Pushpakanthie Wijekoon. "Stochastic Restricted LASSO-Type Estimator in the Linear Regression Model." Journal of Probability and Statistics 2020 (March 30, 2020): 1–7. http://dx.doi.org/10.1155/2020/7352097.
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Among several variable selection methods, LASSO is the most desirable estimation procedure for handling regularization and variable selection simultaneously in the high-dimensional linear regression models when multicollinearity exists among the predictor variables. Since LASSO is unstable under high multicollinearity, the elastic-net (Enet) estimator has been used to overcome this issue. According to the literature, the estimation of regression parameters can be improved by adding prior information about regression coefficients to the model, which is available in the form of exact or stochastic linear restrictions. In this article, we proposed a stochastic restricted LASSO-type estimator (SRLASSO) by incorporating stochastic linear restrictions. Furthermore, we compared the performance of SRLASSO with LASSO and Enet in root mean square error (RMSE) criterion and mean absolute prediction error (MAPE) criterion based on a Monte Carlo simulation study. Finally, a real-world example was used to demonstrate the performance of SRLASSO.
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Natanzon, Yanina, Madalene Earp, Julie M. Cunningham, Kimberly R. Kalli, Chen Wang, Sebastian M. Armasu, Melissa C. Larson, et al. "Genomic Analysis Using Regularized Regression in High-Grade Serous Ovarian Cancer." Cancer Informatics 17 (January 1, 2018): 117693511875534. http://dx.doi.org/10.1177/1176935118755341.
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High-grade serous ovarian cancer (HGSOC) is a complex disease in which initiation and progression have been associated with copy number alterations, epigenetic processes, and, to a lesser extent, germline variation. We hypothesized that, when summarized at the gene level, tumor methylation and germline genetic variation, alone or in combination, influence tumor gene expression in HGSOC. We used Elastic Net (ENET) penalized regression method to evaluate these associations and adjust for somatic copy number in 3 independent data sets comprising tumors from more than 470 patients. Penalized regression models of germline variation, with or without methylation, did not reveal a role in HGSOC gene expression. However, we observed significant association between regional methylation and expression of 5 genes ( WDPCP, KRT6C, BRCA2, EFCAB13, and ZNF283). CpGs retained in ENET model for BRCA2 and ZNF283 appeared enriched in several regulatory elements, suggesting that regularized regression may provide a novel utility for integrative genomic analysis.
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Sridhara, Shankarappa, Nandini Ramesh, Pradeep Gopakkali, Bappa Das, Soumya Venkatappa, Shivaramu Sanjivaiah, Kamalesh Kumar Singh, et al. "Weather-Based Neural Network, Stepwise Linear and Sparse Regression Approach for Rabi Sorghum Yield Forecasting of Karnataka, India." Agronomy 10, no. 11 (October 26, 2020): 1645. http://dx.doi.org/10.3390/agronomy10111645.
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Sorghum is an important dual-purpose crop of India grown for food and fodder. Prevailing weather conditions during the crop growth period determine the yield of sorghum. Hence, the crop yield forecasting models based on weather parameters will be an appropriate option for policymakers and researchers to develop sustainable cropping strategies. In the present study, six multivariate weather-based models viz., least absolute shrinkage and selection operator (LASSO), elastic net (ENET), principal component analysis (PCA) in combination with stepwise multiple linear regression (SMLR), artificial neural network (ANN) alone and in combination with PCA and ridge regression model are examined by fixing 90% of the data for calibration and remaining dataset for validation to forecast rabi sorghum yield for different districts of Karnataka. The R2 and root mean square error (RMSE) during calibration ranged between 0.42 to 0.98 and 30.48 to 304.17 kg ha−1, respectively, without actual evapotranspiration (AET) whereas, these evaluation parameters varied from 0.38 to 0.99 and 19.84 to 308.79 kg ha−1, respectively with AET inclusion. During validation, the RMSE and nRMSE (normalized root mean square error) varied between 88.99 to 1265.03 kg ha−1 and 4.49 to 96.84%, respectively without AET and including AET as one of the weather variable RMSE and nRMSE were 63.48 to 1172.01 kg ha−1 and 4.16 to 92.56%, respectively. The performance of six multivariate models revealed that LASSO was the best model followed by ENET compared to PCA_SMLR, ANN, PCA_ANN and ridge regression models because of reduced overfitting through penalisation of regression coefficient. Thus, it can be concluded that LASSO and ENET weather-based models can be effectively utilized for the district level forecast of sorghum yield.
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Li, Qingyu, and Zhenjiang Miao. "Light Corner Based Object Detector with Stacked-ENet Backbones." Journal of Physics: Conference Series 1229 (May 2019): 012040. http://dx.doi.org/10.1088/1742-6596/1229/1/012040.
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Sanders, J. R. "A Perspective on the Merger of ENet and ERS." American Journal of Evaluation 6, no. 1 (January 1, 1985): 16–19. http://dx.doi.org/10.1177/109821408500600105.
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إسماعيل, بان صباح. "استخدام الويب كاميرا نوع Enet ككاشف لشدة ضوء مصباح الفلورسنت." Journal of Al-Nahrain University Science 12, no. 3 (September 1, 2009): 16–25. http://dx.doi.org/10.22401/jnus.12.3.28.
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Pintér, Róbert. "A gamer bennük van – Az eNET Internetkutató, az Esportmilla és az Esport1 közös magyar videojátékos és e-sport kutatásának főbb eredményei." Információs Társadalom 18, no. 1 (April 6, 2018): 107. http://dx.doi.org/10.22503/inftars.xviii.2018.1.7.
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A tanulmány az eNET, Esportmilla és Esport1 videojáték és e-sport kutatásának főbb kutatási eredményeit mutatja be. A két kutatás számos témát felölelt, ezek közül a tanulmány először a videojáték kutatás eredményeit ismertetve kitér arra, hogy mennyien játszanak videojátékkal idehaza és ehhez mik a főbb motivációik. Foglalkozik annak vizsgálatával, hogy a nem játszók körében mennyire elterjedtek a videojátékosokkal kapcsolatos negatív sztereotípiák, illetve milyen a szülők viszonya a témához. Ezt követően bemutatja, hogy min és mit játszanak a játékosok, illetve mekkora az e-sport játékkal játszók hazai bázisa. A tanulmány ismerteti az e-sport kutatás eredményeit is, így, hogy mik a főbb játékplatformok, hány órát tesz ki a játékkal töltött idő és az általában vett „screen time”, mi mondható az egyéni fejlődésről és streamek követéséről, valamint, hogy hagyományos értelemben sportolnak-e egyáltalán a gamerek? A tanulmány kísérletet tesz a videojátékokhoz köthető piac magyarországi méretének becslésére is. Végül a befejezésben azt vizsgálja, hogy vajon széleskörű társadalmi elfogadottság előtt áll-e idehaza a videojáték és az e-sport?
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The Gamer Inside Them: the Main Results of Hungarian Esport and Videogames Research by eNET, Esportmilla and Esport1
The study presents the main research results of eNET, Esportmilla and Esport1 video games and esports research. The two research projects covered a few themes, this article first shows the results of the video games research, which demonstrates how much gamers play video games in Hungary and what their main motivations are. It deals with examining how widespread the negative stereotypes are associated with video game players among non-gamers and how parents relate to the topic. It then shows what and how gamers play and how many esports gamers there are. The study also describes the results of esports research, including the main gaming platforms, how much the playing time is and how much the usual "screen time" is, what can be said about individual development and watching of streaming, and whether or not gamers pursue traditional sports. The study also attempts to estimate the size of the video games market in Hungary. Finally, it examines whether video games and esports are about to be widely accepted in Hungary?
Keywords: videogames, esport, research, Hungary
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Moayedi, Hossein, Dieu Tien Bui, Anastasios Dounis, Zongjie Lyu, and Loke Kok Foong. "Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques." Applied Sciences 9, no. 20 (October 15, 2019): 4338. http://dx.doi.org/10.3390/app9204338.
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The heating load calculation is the first step of the iterative heating, ventilation, and air conditioning (HVAC) design procedure. In this study, we employed six machine learning techniques, namely multi-layer perceptron regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient buildings. After that, these approaches were used to specify a relationship among the parameters of input and output in terms of the energy performance of buildings. The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error (RRSE), root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R2), and relative absolute error (RAE). It was found that between the discussed machine learning-based solutions of MLPr, LLWL, AMT, RF, ENet, and RBFr, the RF was nominated as the most appropriate predictive network. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the training dataset to be 0.9997, 0.19, 0.2399, 2.078, and 2.3795, respectively. The RF network outcomes determined the R2, MAE, RMSE, RAE, and RRSE for the testing dataset to be 0.9989, 0.3385, 0.4649, 3.6813, and 4.5995, respectively. These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings.
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Wang, Zhu, Shuangge Ma, Michael Zappitelli, Chirag Parikh, Ching-Yun Wang, and Prasad Devarajan. "Penalized count data regression with application to hospital stay after pediatric cardiac surgery." Statistical Methods in Medical Research 25, no. 6 (September 30, 2016): 2685–703. http://dx.doi.org/10.1177/0962280214530608.
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Pediatric cardiac surgery may lead to poor outcomes such as acute kidney injury (AKI) and prolonged hospital length of stay (LOS). Plasma and urine biomarkers may help with early identification and prediction of these adverse clinical outcomes. In a recent multi-center study, 311 children undergoing cardiac surgery were enrolled to evaluate multiple biomarkers for diagnosis and prognosis of AKI and other clinical outcomes. LOS is often analyzed as count data, thus Poisson regression and negative binomial (NB) regression are common choices for developing predictive models. With many correlated prognostic factors and biomarkers, variable selection is an important step. The present paper proposes new variable selection methods for Poisson and NB regression. We evaluated regularized regression through penalized likelihood function. We first extend the elastic net (Enet) Poisson to two penalized Poisson regression: Mnet, a combination of minimax concave and ridge penalties; and Snet, a combination of smoothly clipped absolute deviation (SCAD) and ridge penalties. Furthermore, we extend the above methods to the penalized NB regression. For the Enet, Mnet, and Snet penalties (EMSnet), we develop a unified algorithm to estimate the parameters and conduct variable selection simultaneously. Simulation studies show that the proposed methods have advantages with highly correlated predictors, against some of the competing methods. Applying the proposed methods to the aforementioned data, it is discovered that early postoperative urine biomarkers including NGAL, IL18, and KIM-1 independently predict LOS, after adjusting for risk and biomarker variables.
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Deng, Qitian, Xu Li, Peizhou Ni, Honghai Li, and Zhiyong Zheng. "Enet-CRF-Lidar: Lidar and Camera Fusion for Multi-Scale Object Recognition." IEEE Access 7 (2019): 174335–44. http://dx.doi.org/10.1109/access.2019.2956492.
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Alimadadi, Ahmad, Ishan Manandhar, Sachin Aryal, Patricia B. Munroe, Bina Joe, and Xi Cheng. "Machine learning-based classification and diagnosis of clinical cardiomyopathies." Physiological Genomics 52, no. 9 (September 1, 2020): 391–400. http://dx.doi.org/10.1152/physiolgenomics.00063.2020.
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Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic classification of different types of cardiomyopathies is critical for precision medicine in clinical practice. In this study, we hypothesized that machine learning (ML) can be used as a novel diagnostic approach to analyze cardiac transcriptomic data for classifying clinical cardiomyopathies. RNA-Seq data of human left ventricle tissues were collected from 41 DCM patients, 47 ICM patients, and 49 nonfailure controls (NF) and tested using five ML algorithms: support vector machine with radial kernel (svmRadial), neural networks with principal component analysis (pcaNNet), decision tree (DT), elastic net (ENet), and random forest (RF). Initial ML classifications achieved ~93% accuracy (svmRadial) for NF vs. DCM, ~82% accuracy (RF) for NF vs. ICM, and ~80% accuracy (ENet and svmRadial) for DCM vs. ICM. Next, 50 highly contributing genes (HCGs) for classifying NF and DCM, 68 HCGs for classifying NF and ICM, and 59 HCGs for classifying DCM and ICM were selected for retraining ML models. Impressively, the retrained models achieved ~90% accuracy (RF) for NF vs. DCM, ~90% accuracy (pcaNNet) for NF vs. ICM, and ~85% accuracy (pcaNNet and RF) for DCM vs. ICM. Pathway analyses further confirmed the involvement of those selected HCGs in cardiac dysfunctions such as cardiomyopathies, cardiac hypertrophies, and fibrosis. Overall, our study demonstrates the promising potential of using artificial intelligence via ML modeling as a novel approach to achieve a greater level of precision in diagnosing different types of cardiomyopathies.
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Thompson, Emmanuel, and Ahmad Mahmoud Talafha. "Regularization-Based Bootstrap Ranking Model: Identifying Healthcare Indicators Among All Level Income Economies." Afrika Statistika 15, no. 3 (June 1, 2020): 2431–49. http://dx.doi.org/10.16929/as/2020.2431.167.
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This study considers the problem of uncertainty of concurrent variables selection among a potential set of healthcare expenditure predictors. It evaluates two regularization (shrinkage) methods: Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ENET). To improve the accuracy of identifying important and relevant predictors of healthcare cost, the present study proposes a new methodology in the form of a bootstrapped-regularized regression with percentile rankings. A simulation study under various scenarios was implemented to learn the performance of the proposed methodology. The proposed methodology was applied to healthcare expenditure data for all level income economies: lower-income, lower-middle-income, upper-middle-income, and high-income.
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Molloy, M. K. "Comments on 'A note on the performance of ENET II' by P. Gburzynski and P. Rudnicki." IEEE Journal on Selected Areas in Communications 7, no. 3 (April 1989): 427–30. http://dx.doi.org/10.1109/49.16876.
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Quintero-Leyva, Barbaro. "On the Impact of the Relativistic Acceleration of ENET on the Electromagnetic Analogy of Gravitational Orbits’ Decay." OALib 06, no. 08 (2019): 1–16. http://dx.doi.org/10.4236/oalib.1105673.
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Patil, Abhijeet R., and Sangjin Kim. "Combination of Ensembles of Regularized Regression Models with Resampling-Based Lasso Feature Selection in High Dimensional Data." Mathematics 8, no. 1 (January 10, 2020): 110. http://dx.doi.org/10.3390/math8010110.
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In high-dimensional data, the performances of various classifiers are largely dependent on the selection of important features. Most of the individual classifiers with the existing feature selection (FS) methods do not perform well for highly correlated data. Obtaining important features using the FS method and selecting the best performing classifier is a challenging task in high throughput data. In this article, we propose a combination of resampling-based least absolute shrinkage and selection operator (LASSO) feature selection (RLFS) and ensembles of regularized regression (ERRM) capable of dealing data with the high correlation structures. The ERRM boosts the prediction accuracy with the top-ranked features obtained from RLFS. The RLFS utilizes the lasso penalty with sure independence screening (SIS) condition to select the top k ranked features. The ERRM includes five individual penalty based classifiers: LASSO, adaptive LASSO (ALASSO), elastic net (ENET), smoothly clipped absolute deviations (SCAD), and minimax concave penalty (MCP). It was built on the idea of bagging and rank aggregation. Upon performing simulation studies and applying to smokers’ cancer gene expression data, we demonstrated that the proposed combination of ERRM with RLFS achieved superior performance of accuracy and geometric mean.
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Wan, Haifeng, Lei Gao, Manman Su, Qirun Sun, and Lei Huang. "Attention-Based Convolutional Neural Network for Pavement Crack Detection." Advances in Materials Science and Engineering 2021 (April 7, 2021): 1–13. http://dx.doi.org/10.1155/2021/5520515.
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Achieving high detection accuracy of pavement cracks with complex textures under different lighting conditions is still challenging. In this context, an encoder-decoder network-based architecture named CrackResAttentionNet was proposed in this study, and the position attention module and channel attention module were connected after each encoder to summarize remote contextual information. The experiment results demonstrated that, compared with other popular models (ENet, ExFuse, FCN, LinkNet, SegNet, and UNet), for the public dataset, CrackResAttentionNet with BCE loss function and PRelu activation function achieved the best performance in terms of precision (89.40), mean IoU (71.51), recall (81.09), and F1 (85.04). Meanwhile, for a self-developed dataset (Yantai dataset), CrackResAttentionNet with BCE loss function and PRelu activation function also had better performance in terms of precision (96.17), mean IoU (83.69), recall (93.44), and F1 (94.79). In particular, for the public dataset, the precision of BCE loss and PRelu activation function was improved by 3.21. For the Yantai dataset, the results indicated that the precision was improved by 0.99, the mean IoU was increased by 0.74, the recall was increased by 1.1, and the F1 for BCE loss and PRelu activation function was increased by 1.24.
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Ai, Xinbo, Yunhao Xie, Yinan He, and Yi Zhou. "Improve SegNet with feature pyramid for road scene parsing." E3S Web of Conferences 260 (2021): 03012. http://dx.doi.org/10.1051/e3sconf/202126003012.
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Road scene parsing is a common task in semantic segmentation. Its images have characteristics of containing complex scene context and differing greatly among targets of the same category from different scales. To address these problems, we propose a semantic segmentation model combined with edge detection. We extend the segmentation network with an encoder-decoder structure by adding an edge feature pyramid module, namely Edge Feature Pyramid Network (EFPNet, for short). This module uses edge detection operators to get boundary information and then combines the multiscale features to improve the ability to recognize small targets. EFPNet can make up the shortcomings of convolutional neural network features, and it helps to produce smooth segmentation. After extracting features of the encoder and decoder, EFPNet uses Euclidean distance to compare the similarity between the presentation of the encoder and the decoder, which can increase the decoder’s ability to restore from the encoder. We evaluated the proposed method on Cityscapes datasets. The experiment on Cityscapes datasets demonstrates that the accuracies are improved by 7.5% and 6.2% over the popular SegNet and ENet. And the ablation experiment validates the effectiveness of our method.
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Ningsih, S. S., and A. Mustadi. "The contribution of friendly character through cooperative-learning model with example and non-example types (CLM-ENET) on energy concepts." Journal of Physics: Conference Series 1280 (November 2019): 032057. http://dx.doi.org/10.1088/1742-6596/1280/3/032057.
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Liu, Xiaofeng, Yuzhuo Han, Song Bai, Yi Ge, Tianxing Wang, Xu Han, Site Li, Jane You, and Jun Lu. "Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11629–36. http://dx.doi.org/10.1609/aaai.v34i07.6831.
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Semantic segmentation (SS) is an important perception manner for self-driving cars and robotics, which classifies each pixel into a pre-determined class. The widely-used cross entropy (CE) loss-based deep networks has achieved significant progress w.r.t. the mean Intersection-over Union (mIoU). However, the cross entropy loss can not take the different importance of each class in an self-driving system into account. For example, pedestrians in the image should be much more important than the surrounding buildings when make a decisions in the driving, so their segmentation results are expected to be as accurate as possible. In this paper, we propose to incorporate the importance-aware inter-class correlation in a Wasserstein training framework by configuring its ground distance matrix. The ground distance matrix can be pre-defined following a priori in a specific task, and the previous importance-ignored methods can be the particular cases. From an optimization perspective, we also extend our ground metric to a linear, convex or concave increasing function w.r.t. pre-defined ground distance. We evaluate our method on CamVid and Cityscapes datasets with different backbones (SegNet, ENet, FCN and Deeplab) in a plug and play fashion. In our extenssive experiments, Wasserstein loss demonstrates superior segmentation performance on the predefined critical classes for safe-driving.
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Gao, Yiming, and Jiangqin Wu. "GAN-Based Unpaired Chinese Character Image Translation via Skeleton Transformation and Stroke Rendering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 646–53. http://dx.doi.org/10.1609/aaai.v34i01.5405.
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The automatic style translation of Chinese characters (CH-Char) is a challenging problem. Different from English or general artistic style transfer, Chinese characters contain a large number of glyphs with the complicated content and characteristic style. Early methods on CH-Char synthesis are inefficient and require manual intervention. Recently some GAN-based methods are proposed for font generation. The supervised GAN-based methods require numerous image pairs, which is difficult for many chirography styles. In addition, unsupervised methods often cause the blurred and incorrect strokes. Therefore, in this work, we propose a three-stage Generative Adversarial Network (GAN) architecture for multi-chirography image translation, which is divided into skeleton extraction, skeleton transformation and stroke rendering with unpaired training data. Specifically, we first propose a fast skeleton extraction method (ENet). Secondly, we utilize the extracted skeleton and the original image to train a GAN model, RNet (a stroke rendering network), to learn how to render the skeleton with stroke details in target style. Finally, the pre-trained model RNet is employed to assist another GAN model, TNet (a skeleton transformation network), to learn to transform the skeleton structure on the unlabeled skeleton set. We demonstrate the validity of our method on two chirography datasets we established.
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Bai, Hao, Tingzhu Bai, Wei Li, and Xun Liu. "A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images." Applied Sciences 11, no. 11 (May 30, 2021): 5069. http://dx.doi.org/10.3390/app11115069.
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Building segmentation is widely used in urban planning, disaster prevention, human flow monitoring and environmental monitoring. However, due to the complex landscapes and highdensity settlements, automatically characterizing building in the urban village or cities using remote sensing images is very challenging. Inspired by the rencent deep learning methods, this paper proposed a novel end-to-end building segmentation network for segmenting buildings from remote sensing images. The network includes two branches: one branch uses Widely Adaptive Spatial Pyramid (WASP) structure to extract multi-scale features, and the other branch uses a deep residual network combined with a sub-pixel up-sampling structure to enhance the detail of building boundaries. We compared our proposed method with three state-of-the-art networks: DeepLabv3+, ENet, ESPNet. Experiments were performed using the publicly available Inria Aerial Image Labelling dataset (Inria aerial dataset) and the Satellite dataset II(East Asia). The results showed that our method outperformed the other networks in the experiments, with Pixel Accuracy reaching 0.8421 and 0.8738, respectively and with mIoU reaching 0.9034 and 0.8936 respectively. Compared with the basic network, it has increased by about 25% or more. It can not only extract building footprints, but also especially small building objects.
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Araldi, Alessandro. "Towards an Integrated Methodology for Model and Variable Selection Using Count Data: An Application to Micro-Retail Distribution in Urban Studies." Urban Science 4, no. 2 (April 28, 2020): 21. http://dx.doi.org/10.3390/urbansci4020021.
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Over the last two decades, a growing number of works in urban studies have revealed how micro-retail distribution is significantly related to specific properties of the urban built environment. While a wide variety of urban form measures have been investigated using sophisticated analytical approaches, the same attention has not equally been found in statistical procedures. Several essential features of micro-retail statistical distribution and modelling assumptions are frequently overlooked, compromising the statistical robustness of outcomes. In this work we focus on four main aspects: (i) the discrete, non-negative and highly skewed nature of store distribution; (ii) its zero-inflation; (iii) assessment of the contextual effect; and (iv) the multicollinearity generated by the inclusion of highly related urban descriptors. To overcome these limitations, we propose an integrated methodological framework for both modelling and variable selection assessment based on generalized linear models (GLMs) and elastic-net (Enet) penalized regression (PR), respectively. The procedure is tested via a real case study of the French Riviera, which is described using a large dataset of 105 street-based urban form measures. The outcomes of this procedure show the superiority of the zero-inflate negative binomial count regression approach. A restricted number of urban form properties are found to be related to the micro-retail distribution depending on the specific scale and morphological context under analysis.
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Abasht, Behnam, Michael B. Papah, and Jing Qiu. "Evidence of vascular endothelial dysfunction in Wooden Breast disorder in chickens: Insights through gene expression analysis, ultra-structural evaluation and supervised machine learning methods." PLOS ONE 16, no. 1 (January 4, 2021): e0243983. http://dx.doi.org/10.1371/journal.pone.0243983.
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Several gene expression studies have been previously conducted to characterize molecular basis of Wooden Breast myopathy in commercial broiler chickens. These studies have generally used a limited sample size and relied on a binary disease outcome (unaffected or affected by Wooden Breast), which are appropriate for an initial investigation. However, to identify biomarkers of disease severity and development, it is necessary to use a large number of samples with a varying degree of disease severity. Therefore, in this study, we assayed a relatively large number of samples (n = 96) harvested from the pectoralis major muscle of unaffected (U), partially affected (P) and markedly affected (A) chickens. Gene expression analysis was conducted using the nCounter MAX Analysis System and data were analyzed using four different supervised machine-learning methods, including support vector machines (SVM), random forests (RF), elastic net logistic regression (ENET) and Lasso logistic regression (LASSO). The SVM method achieved the highest prediction accuracy for both three-class (U, P and A) and two-class (U and P+A) classifications with 94% prediction accuracy for two-class classification and 85% for three-class classification. The results also identified biomarkers of Wooden Breast severity and development. Additionally, gene expression analysis and ultrastructural evaluations provided evidence of vascular endothelial cell dysfunction in the early pathogenesis of Wooden Breast.
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Skandarani, Youssef, Pierre-Marc Jodoin, and Alain Lalande. "Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?" Algorithms 14, no. 7 (July 14, 2021): 212. http://dx.doi.org/10.3390/a14070212.
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Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets.
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Pilaš, Ivan, Mateo Gašparović, Alan Novkinić, and Damir Klobučar. "Mapping of the Canopy Openings in Mixed Beech–Fir Forest at Sentinel-2 Subpixel Level Using UAV and Machine Learning Approach." Remote Sensing 12, no. 23 (November 30, 2020): 3925. http://dx.doi.org/10.3390/rs12233925.
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The presented study demonstrates a bi-sensor approach suitable for rapid and precise up-to-date mapping of forest canopy gaps for the larger spatial extent. The approach makes use of Unmanned Aerial Vehicle (UAV) red, green and blue (RGB) images on smaller areas for highly precise forest canopy mask creation. Sentinel-2 was used as a scaling platform for transferring information from the UAV to a wider spatial extent. Various approaches to an improvement in the predictive performance were examined: (I) the highest R2 of the single satellite index was 0.57, (II) the highest R2 using multiple features obtained from the single-date, S-2 image was 0.624, and (III) the highest R2 on the multitemporal set of S-2 images was 0.697. Satellite indices such as Atmospherically Resistant Vegetation Index (ARVI), Infrared Percentage Vegetation Index (IPVI), Normalized Difference Index (NDI45), Pigment-Specific Simple Ratio Index (PSSRa), Modified Chlorophyll Absorption Ratio Index (MCARI), Color Index (CI), Redness Index (RI), and Normalized Difference Turbidity Index (NDTI) were the dominant predictors in most of the Machine Learning (ML) algorithms. The more complex ML algorithms such as the Support Vector Machines (SVM), Random Forest (RF), Stochastic Gradient Boosting (GBM), Extreme Gradient Boosting (XGBoost), and Catboost that provided the best performance on the training set exhibited weaker generalization capabilities. Therefore, a simpler and more robust Elastic Net (ENET) algorithm was chosen for the final map creation.
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Grippi, Jamie. "Reviewing the relationship between thermal reservoir parameters and geothermal energy output." PAM Review Energy Science & Technology 5 (May 31, 2018): 2–21. http://dx.doi.org/10.5130/pamr.v5i0.1494.
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This meta-study draws upon contemporary literature to examine parameters of thermal reservoirs and their relationships to geothermal power station output metrics. The objectives of the meta-study are to identify trends and quantify the influence of each parameter on the system as a whole. This study provides a framework for industry and researchers exploring new potential geothermal fields. Six reservoir parameters – well depth, temperature, enthalpy, mass flow rate, thermal gradient and crust thickness – were plotted against the net electrical output per production well (Enet/well) and exergy efficiency (ηB) of 64 geothermal facilities. The meta-study identified that reservoir temperature has the greatest proportionality to power output, with yields above 10MWe exhibited only for high enthalpy reservoirs exceeding 500K. Well depth has the greatest inverse proportionality to exergy efficiency, with upper limit values declining below 80% for wells deeper than 3000m. Well depth has a similar trend line, though lesser correlation, as reservoir temperature to power output. Crust thickness has an inverse correlation to exergy efficiency, with upper limit values dropping from 100% to 65% as thickness increased from 30 to 45km. There was significant clustering of data points in most trendless plots, suggesting a considerable degree of homogeneity between currently tapped reservoirs and turbine efficiencies. The low number of well-defined data trends implies a high degree of complexity arising from the relationships between reservoir parameters that make quantification problematic. Despite this difficulty, examination of the aforementioned parameters suggests that although hotter reservoirs are usually found at greater depths, the hottest and shallowest reservoirs should be prioritized for use in order to return maximal power outputs and reduce exergy losses that occur along large lengths of piping.
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Michel, Simon, Didier Swingedouw, Marie Chavent, Pablo Ortega, Juliette Mignot, and Myriam Khodri. "Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0." Geoscientific Model Development 13, no. 2 (March 3, 2020): 841–58. http://dx.doi.org/10.5194/gmd-13-841-2020.
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Abstract. Modes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statistical learning methods applied to proxy records is useful for improving our understanding of their behaviour. For doing so, several statistical methods exist, among which principal component regression is one of the most widely used in paleoclimatology. Here, we provide the software ClimIndRec to the climate community; it is based on four regression methods (principal component regression, PCR; partial least squares, PLS; elastic net, Enet; random forest, RF) and cross-validation (CV) algorithms, and enables the systematic reconstruction of a given climate index. A prerequisite is that there are proxy records in the database that overlap in time with its observed variations. The relative efficiency of the methods can vary, according to the statistical properties of the mode and the proxy records used. Here, we assess the sensitivity to the reconstruction technique. ClimIndRec is modular as it allows different inputs like the proxy database or the regression method. As an example, it is here applied to the reconstruction of the North Atlantic Oscillation by using the PAGES 2k database. In order to identify the most reliable reconstruction among those given by the different methods, we use the modularity of ClimIndRec to investigate the sensitivity of the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the targeted reconstruction period. We obtain the best reconstruction of the North Atlantic Oscillation (NAO) using the random forest approach. It shows significant correlation with former reconstructions, but exhibits higher validation scores.
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Wunderlich, Kevin, and Emmanuel Thompson. "Estimating Health Care Costs among Fragile and Conflict Affected States: An Elastic Net-Risk Measures Approach." International Journal of Public Health Science (IJPHS) 7, no. 3 (September 3, 2018): 175. http://dx.doi.org/10.11591/ijphs.v7i3.14844.
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<span>Fragile and conflict affected states (FCAS) are those in which the government lacks the political will and/or capacity to provide the basic functions necessary for poverty reduction, economic development, and the security of human rights of their populations.</span><span>Until recent history, unfortunately, the majority of research conducted and universal health care debates have been centered around middle income and emerging economies. As a result, FCAS have been neglected from many global discussions and decisions. Due to this neglect, many FCAS do not have proper vaccinations and antibiotics. Seemingly, well estimated health care costs are a necessary stepping stone in improving the health of citizens among FCAS. Fortunately, developments in statistical learning theory combined with data obtained by the WBG and Transparency International make it possible to accurately model health care cost among FCAS. The data used in this paper consisted of 35 countries and 89 variables. Of these 89 variables, health care expenditure (HCE) was the only response variable. With 88 predictor variables, there was expected to be multicollinearity, which occurs when multiple variables share relatively large absolute correlation. Since multicollinearity is expected and the number of variables is far greater than the number of observations, this paper adopts Zou and Hastie’</span><span lang="IN">s </span><span>method of regularization via elastic net (ENET). In order to accurately estimate the maximum and expected maximum HCE among FCAS, well-known risk measures, such as Value at Risk and Conditional Value at Risk, and related quantities were obtained via Monte Carlo simulations. This paper obtained risk measures at 95 security level.</span>
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Camiolo, Massimo. "Vol-ente o nol-ente." MINORIGIUSTIZIA, no. 1 (May 2009): 334–37. http://dx.doi.org/10.3280/mg2009-001036.
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Quinlan, Marsha B., Robert J. Quinlan, and Samuel Dira. "Sidama Agro-Pastoralism and Ethnobiological Classification of its Primary Plant, Enset (Ensete ventricosum)." Ethnobiology Letters 5 (October 2, 2014): 116–25. http://dx.doi.org/10.14237/ebl.5.2014.222.
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Enset is an essential plant for the Ethiopian Sidama system of agropastoralism. Sidama agropastoralism and the folk taxonomy of enset is presented here in ethnographic context. One of several societies of Ethiopia’s enset complex, the highland Sidama are among the most wholly reliant on enset and maintain more enset varieties in their gardens than other groups. Sidama agro-pastoral systems revolve around human-enset-cattle interaction: Sidama eat low-protein parts of enset; cattle eat high-protein parts of enset; Sidama get protein from dairy; Sidama fertilize enset with cattle manure. In the Sidama language, enset offers an example of Hunn’s generic elevation within the framework of Berlinian perceptual-taxonomic theory. Weesho (enset) may serve both as a folk generic taxon and a life-form taxon depending on the frame of reference. Such expansion allows for an intermediate taxa translating to “male” or “female” ensets, followed by generic and specific taxa for kinds or “breeds” of enset. Generic elevation offers descriptive magnification of nomenclature for enset, a most salient species among Sidama people.
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Fanta, Solomon Workneh, and Satheesh Neela. "A review on nutritional profile of the food from enset." Nutrition & Food Science 49, no. 5 (September 9, 2019): 824–43. http://dx.doi.org/10.1108/nfs-11-2018-0306.
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Purpose This paper is a systemic review on enset plant’s role in Ethiopian people’s life as the source of food. This paper aims to summarize the traditional processing and preparation methods of enset-based foods and their nutritional composition. Design/methodology/approach Available scientific articles were collected and reviewed for enset plant evaluation, description, enset plant’s role in Ethiopian people’s food security, post harvesting and traditional processing of enset plants, microbiology of the fermented enset foods, different foods reported from enset, nutritional profile of the three food from enset base (kocho, bulla and amicho) and other non-food applications of enset plant. Findings Enset plant has a predominant role in people living in the southern part of Ethiopia. This plant is drought-tolerant and provides many non-food applications. Harvesting of the enset plant, preparing for fermentation and food preparations follow the traditional route by using the indigenous knowledge and practices. Limited studies have been reported on the microbiology of the enset fermentation, but various types of microbes have been reported. In case of nutritional composition, foods from enset are reported to contain high carbohydrate and minerals content, such as calcium, potassium and zinc, but limited protein content; they are also the best source of the essential amino acids such as lysine and leucine. Limited data are available on vitamins, anti-oxidant and fatty acids profiles of enset-based foods. The existing data indicate variations, and the reasons for variability are discussed in this paper. Originality/value Scientific reviews on enset food nutrition profile and related issues are scarce; this paper will compile information about enset plant-based foods for researchers for their future research.
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Siegl, Florian. "More on possible Forest Enets – Ket contacts." Eesti ja soome-ugri keeleteaduse ajakiri. Journal of Estonian and Finno-Ugric Linguistics 3, no. 1 (June 18, 2012): 327–42. http://dx.doi.org/10.12697/jeful.2012.3.1.16.
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In this paper linguistic traces of the Enets’ migration to the Taimyr Peninsula are addressed. Special attention is paid to Forest Enets–Ket contacts and a tentative etymology for the (Forest) Enets’ ethnonym for Kets and Selkups is offered. Of special importance is a likely Enets place name in Northern Evenkija, an area from which no Enets place names have been reported earlier. As the same area is inhabited by speakers of the northern dialect of Ket, this area should be seen as a possible contact area for the unusual case of pronoun borrowing in Forest Enets as discussed in Siegl (2008)
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Tsegaye, A., and P. C. Struik. "ANALYSIS OF ENSET (ENSETE VENTRICOSUM) INDIGENOUS PRODUCTION METHODS AND FARM-BASED BIODIVERSITY IN MAJOR ENSET-GROWING REGIONS OF SOUTHERN ETHIOPIA." Experimental Agriculture 38, no. 3 (June 18, 2002): 291–315. http://dx.doi.org/10.1017/s0014479702003046.
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Enset (Ensete ventricosum) production is declining, and it faces genetic erosion due to drought, diseases and population pressure. Participatory Rural Appraisal (PRA) and additional formal survey studies on 315 households were conducted over three consecutive years (1998–2000) in the Sidama, Wolaita and Hadiya ethnic regions of southern Ethiopia to assess traditional cultivation methods, analyse the production systems, and evaluate farm-based enset biodiversity. The regions differ in terms of cultural background, resources, farming systems, population density, and agro-ecology. Furthermore, the methods for initiating suckers and the frequency of transplanting vary among the three regions.Diverse enset landraces were identified in the Sidama (52), Wolaita (55) and Hadiya (59) regions. Sidama farmers had the highest number of landraces per farm, 57% and 21% more than found on Wolaita and Hadiya farms respectively. In all three regions, landrace diversity was influenced by household resources, cultural background, population pressure, and agro-ecology. There were significant differences in the average number of enset landraces and livestock between rich and poor households in the three regions. Rich farmers had more land and manure-producing livestock, and they planted more enset landraces than did poor farmers. In all three regions, women proved to be more experienced than men in identifying enset landraces.The number of enset landraces per farm was significantly correlated with other household characteristics for resource-rich Sidama farmers and with the number of livestock and area of farmland for resource-rich Hadiya farmers. This suggests that middle-income or poor farmers concentrate on annual crops, rather than on growing the perennial enset plant. More research is needed to identify, characterize and conserve genetic diversity, and to improve the cultivation practices for enset. The cultural, socio-economic, and gender-associated aspects of enset cultivation need to be assessed to understand the dynamics of enset biodiversity.
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Zirkzee, E. J. M., M. E. Ndosi, T. P. M. Vliet Vlieland, and J. J. L. Meesters. "Measuring educational needs among patients with systemic lupus erythematosus (SLE) using the Dutch version of the Educational Needs Assessment Tool (D-ENAT)." Lupus 23, no. 13 (July 24, 2014): 1370–76. http://dx.doi.org/10.1177/0961203314544188.
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Objective The Educational Needs Assessment Tool (ENAT) was developed in the United Kingdom (UK) to systematically assess the educational needs of patients with rheumatic diseases. The aim of the present study was to describe the educational needs of Dutch patients with systemic lupus erythematosus (SLE) by means of a Dutch version of the ENAT (D-ENAT). Methods The D-ENAT was sent to a random sample of 244 SLE patients registered at the outpatient clinic of a university hospital. D-ENAT consists of 39 items in seven domains. The D-ENAT domain scores range from 0–16 to 0–28 (higher scoring equals higher educational needs) depending of the number of items in the domain. A total D-ENAT score (0–156) is calculated by summing all 39 items. In addition, age, disease duration, gender, educational level, present information need (yes/no) and the extent of information need (1–4: nothing–everything) were recorded. Univariate regression analysis was used to examine the D-ENAT’s potential determinants. Results The response rate was 122 out of 244 (50%). The mean (% of maximum score) educational needs scores were 56% for ‘D-ENAT total score’, 62% for ‘Self-help measures’, 60% for ‘Disease process’, 58% for ‘Feelings’, 56% for ‘Treatments’, 50% for ‘Movement’, 49% for ‘Support systems’ and 46% for ‘Managing pain’. Being female was significantly associated with higher scoring on the D-ENAT total score (β 23.0; 95% CI 5.9, 40.3). Conclusion SLE patients demonstrated substantial educational needs, especially in the domains: ‘Self-help measures’, ‘Disease process’ and ‘Feelings’. The validity and practical applicability of the D-ENAT to make an inventory of SLE patients’ educational needs requires further investigation.
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Noltemeier, Martina. "WC-Ente." Lebensmittel Zeitung 73, no. 26 (2021): 128. http://dx.doi.org/10.51202/0947-7527-2021-26-128.
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