Journal articles: 'E-boosting'

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Sangrulkar, Mrs Surekha A. "E-Banking- ICT Plus Banking for Boosting Business." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-FIIIIPM2019 (March 20, 2019): 98–100. http://dx.doi.org/10.31142/ijtsrd23074.

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Méndez, José R., M. Reboiro-Jato, Fernando Díaz, Eduardo Díaz, and Florentino Fdez-Riverola. "Grindstone4Spam: An optimization toolkit for boosting e-mail classification." Journal of Systems and Software 85, no. 12 (December 2012): 2909–20. http://dx.doi.org/10.1016/j.jss.2012.06.027.

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Hamim, Touria, Faouzia Benabbou, and Nawal Sael. "Student Profile Modeling Using Boosting Algorithms." International Journal of Web-Based Learning and Teaching Technologies 17, no. 5 (September 2022): 1–13. http://dx.doi.org/10.4018/ijwltt.20220901.oa4.

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The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for classification, prediction or clustering purpose. In this paper, the authors present a comparative study between different boosting algorithms which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher Score, Information Gain combined with Recursive Feature Elimination to enhance the preprocessing task and models’ performances. Using multi-label dataset predict the class of the student performance in mathematics, this article results show that the Light Gradient Boosting Machine (LightGBM) algorithm achieved the best performance when using Information gain with Recursive Feature Elimination method compared to the other boosting algorithms.

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Larkin, Marilynn. "Robert E W Hancock–boosting innate immunity to combat infection." Lancet Infectious Diseases 3, no. 11 (November 2003): 736–39. http://dx.doi.org/10.1016/s1473-3099(03)00799-0.

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Monti, Luciano, Gianfrancesco Rizzuti, and Erica Pepe. "E-government and Open Data Boosting Economic Growth: A New Index." Journal of Business and Economics 6, no. 12 (December 20, 2015): 2080–88. http://dx.doi.org/10.15341/jbe(2155-7950)/12.06.2015/009.

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Al-Adwan, Ahmad Samed, and Maher Ahmad Al-Horani. "Boosting Customer E-Loyalty: An Extended Scale of Online Service Quality." Information 10, no. 12 (December 3, 2019): 380. http://dx.doi.org/10.3390/info10120380.

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The Customer trust, satisfaction and loyalty with regard to the provision of e-commerce services is expected to be critical factors for the assessment of the success of online businesses. Service quality and high-quality product settings are closely linked to these factors. However, despite the rapid advancement of e-commerce applications, especially in the context of business to consumer (B2C), prior research has confirmed that e-retailers face difficulties when it comes to maintaining customer loyalty. Several e-service quality frameworks have been employed to boost service quality by targeting customer loyalty. Among these prominent frameworks is the scale of online etail quality (eTailQ). This scale has been under criticism as it was developed before the emergence of Web 2.0 technologies. Consequently, this paper aims to fill this gap by offering empirically-tested and conceptually-derived measurement model specifications for an extended eTailQ scale. In addition, it investigates the potential effects of the extended scale on e-trust and e-satisfaction, and subsequently e-loyalty. The practical and theoretical implications are highlighted to help businesses to design effective business strategies based on quality in order to achieve enhanced customer loyalty, and to direct future research in the field of e-commerce.

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Hu, Bo, Chunlin Chen, Zhangsong Zhan, Xueying Su, Tiegang Hu, Guangyong Zheng, and Zhiyong Yang. "Progress and recent trends in 48 V hybridisation and e-boosting technology on passenger vehicles – a review." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 232, no. 11 (October 24, 2017): 1543–61. http://dx.doi.org/10.1177/0954407017729950.

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Electrification of the powertrain system will play an important role in reducing fuel consumption and engine-out emissions in the next few decades. Compared to the pure electric and full hybrid concept, 48 V mild hybridisation and the accompanying 48 V e-boosting concept, due to their superior cost–benefit performance, may become mainstream for the next generation of fuel reduction measures. The mild hybrid system can realise advanced stop–start, active and passive engine-off coasting, braking recuperation, boost assistance, e-creeping and torque vectoring functions, and is thus deemed to give approximately 10–15% fuel consumption benefits in the New European Driving Cycle (NEDC); and the e-boosting concept, due to its capability for further downsizing and down-speeding, allows the engine operating points to be shifted into a more efficient area. A strong synergy between 48 V mild hybridisation and the 48 V e-boosting concept has been found after reviewing both technologies, and the trends for developing such a combined electrical system are also discussed. Together with more engine and vehicle component electrification, fuel efficiency could be further improved, although the interactions between these technologies are highly complex and need to be optimised at a system level.

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Al-Qudah, Dana A., Ala' M. Al-Zoubi, Pedro A. Castillo-Valdivieso, and Hossam Faris. "Sentiment Analysis for e-Payment Service Providers Using Evolutionary eXtreme Gradient Boosting." IEEE Access 8 (2020): 189930–44. http://dx.doi.org/10.1109/access.2020.3032216.

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Chen, Liang, Lei Zhang, Yan Wang, and Zhiping Yu. "A Compact E-Band Power Amplifier With Gain-Boosting and Efficiency Enhancement." IEEE Transactions on Microwave Theory and Techniques 68, no. 11 (November 2020): 4620–30. http://dx.doi.org/10.1109/tmtt.2020.3017728.

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Vieira, Fábio D., Stanley R. de M. Oliveira, and Samuel R. Paiva. "Metodologia baseada em técnicas de mineração de dados para suporte à certificação de raças de ovinos." Engenharia Agrícola 35, no. 6 (December 2015): 1172–86. http://dx.doi.org/10.1590/1809-4430-eng.agric.v35n6p1172-1186/2015.

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RESUMO O objetivo deste trabalho foi desenvolver uma metodologia baseada em técnicas de mineração de dados para selecionar os principais marcadores SNP (Single Nucleotide Polymorphism) para as raças de ovinos: Crioula, Morada Nova e Santa Inês. Os dados utilizados foram obtidos do Consórcio Internacional de Ovinos e são compostos por 72 animais das raças citadas, e cada animal possui 49.034 marcadores SNP. Considerando que o número de atributos (marcadores) é muito maior que o de observações (animais), foram aplicadas as técnicas de predição LASSO (Least Absolute Shrinkage and Selection Operator), Random Forest e Boosting para a geração de modelos preditivos que incorporam métodos de seleção de atributos. Os resultados revelaram que os modelos preditivos selecionaram os principais marcadores SNP para identificação das raças estudadas. O modelo LASSO selecionou um total de 29 marcadores relevantes. A partir dos modelos Random Forest e Boosting, foram obtidos 27 e 20 marcadores importantes, respectivamente. Por meio da intersecção dos modelos gerados, identificou-se um subconjunto de 18 marcadores com maior potencial de identificação das raças.

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YU, MEILING, and LIANSHOU LIU. "AN EMPIRICAL STUDY OF BOOSTED NEURAL NETWORK FOR PARTICLE CLASSIFICATION IN HIGH ENERGY COLLISIONS." International Journal of Modern Physics A 22, no. 06 (March 10, 2007): 1201–11. http://dx.doi.org/10.1142/s0217751x07034301.

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The possible application of boosted neural network to particle classification in high energy physics is discussed. A two-dimensional toy model, where the boundary between signal and background is irregular but not overlapping, is constructed to show how boosting technique works with neural network. It is found that boosted neural network not only decreases the error rate of classification significantly but also increases the efficiency and signal–background ratio. Besides, boosted neural network can avoid the disadvantage aspects of single neural network design. The boosted neural network is also applied to the classification of quark- and gluon-jet samples from Monte Carlo e+e- collisions, where the two samples show significant overlapping. The performance of boosting technique for the two different boundary cases — with and without overlapping is discussed.

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Jbene, Mourad, Smail Tigani, Saadane Rachid, and Abdellah Chehri. "Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search." Big Data and Cognitive Computing 5, no. 3 (August 13, 2021): 35. http://dx.doi.org/10.3390/bdcc5030035.

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In the age of information overload, customers are overwhelmed with the number of products available for sale. Search engines try to overcome this issue by filtering relevant items to the users’ queries. Traditional search engines rely on the exact match of terms in the query and product meta-data. Recently, deep learning-based approaches grabbed more attention by outperforming traditional methods in many circumstances. In this work, we involve the power of embeddings to solve the challenging task of optimizing product search engines in e-commerce. This work proposes an e-commerce product search engine based on a similarity metric that works on top of query and product embeddings. Two pre-trained word embedding models were tested, the first representing a category of models that generate fixed embeddings and a second representing a newer category of models that generate context-aware embeddings. Furthermore, a re-ranking step was performed by incorporating a list of quality indicators that reflects the utility of the product to the customer as inputs to well-known ranking methods. To prove the reliability of the approach, the Amazon reviews dataset was used for experimentation. The results demonstrated the effectiveness of context-aware embeddings in retrieving relevant products and the quality indicators in ranking high-quality products.

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Singh, Ruchi. "Can Ancient Science And Wisdom Of Yagya Therapy ‘With Herbs Having Immune Boosting and Antiviral Properties’ Aid In The Fight Against COVID19?" Dev Sanskriti Interdisciplinary International Journal 16 (July 31, 2020): 61–68. http://dx.doi.org/10.36018/dsiij.v16i.166.

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In the COVID19 pandemic, there is strong need of immune boosting and mental health approaches which are easily available and traditionally used for preventing as well as managing COVID19 infection. Since past 40 years, Dev Sanskriti University (DSVV) and parent institution (All World Gayatri Pariwar) has been working on various aspects of traditional herbal utility and Yagya Therapy. Vedic texts mentioned use of herbal fumes for health benefits as well as purifying air and removing seasonal pathogens from air through Bheshaj Yajnas (Yagya / Hawan). Bheshaj Yajna (herbal fumigation) was widely used in India to combat seasonal epidemics; scriptures described them in details. Studies have shown Yagya Therapy and herbal fumigation effects in various diseases i,e, common diseases such as diabetes, thyroid, as well as life threatening diseases such as cancer, multi-drug-resistant tuberculosis and in psychological ailments such as Obsessive-Compulsive Disorder and PolyCystic Ovarian Disease, epilepsy, depression, etc., indicating potential of herbal fumes for boosting immunity and aiding psychological wellbeing; besides, the herbal fumes is made using herbs known for their immune boosting and mental health care potential in Ayurveda and traditional knowledge. Hence, the study narrated the selective herbs which are pan-available and widely used traditionally in Yagya Therapy or generating herbal fumes, which can help boosting immunity and aid psychological wellbeing.

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Rodrigues, Fabiano, Francisco Aparecido Rodrigues, and Thelma Valéria Rocha Rodrigues. "Modelos de machine learning para predição do sucesso de startups." Revista de Gestão e Projetos 12, no. 2 (June 15, 2021): 28–55. http://dx.doi.org/10.5585/gep.v12i2.18942.

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Este estudo analisa resultados obtidos com modelos de machine learning para predição do sucesso de startups. Como proxy de sucesso considera-se a perspectiva do investidor, na qual a aquisição da startup ou realização de IPO (Initial Public Offering) são formas de recuperação do investimento. A revisão da literatura aborda startups e veículos de financiamento, estudos anteriores sobre predição do sucesso de startups via modelos de machine learning, e trade-offs entre técnicas de machine learning. Na parte empírica, foi realizada uma pesquisa quantitativa baseada em dados secundários oriundos da plataforma americana Crunchbase, com startups de 171 países. O design de pesquisa estabeleceu como filtro startups fundadas entre junho/2010 e junho/2015, e uma janela de predição entre junho/2015 e junho/2020 para prever o sucesso das startups. A amostra utilizada, após etapa de pré-processamento dos dados, foi de 18.571 startups. Foram utilizados seis modelos de classificação binária para a predição: Regressão Logística, Decision Tree, Random Forest, Extreme Gradiente Boosting, Support Vector Machine e Rede Neural. Ao final, os modelos Random Forest e Extreme Gradient Boosting apresentaram os melhores desempenhos na tarefa de classificação. Este artigo, envolvendo machine learning e startups, contribui para áreas de pesquisa híbridas ao mesclar os campos da Administração e Ciência de Dados. Além disso, contribui para investidores com uma ferramenta de mapeamento inicial de startups na busca de targets com maior probabilidade de sucesso.

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Pozzi, Francesca, Manuela Delfino, Stefania Manca, Donatella Persico, and Immacolata Scancarello. "Boosting Innovation in an Italian Online University." International Journal of Online Pedagogy and Course Design 3, no. 4 (October 2013): 29–43. http://dx.doi.org/10.4018/ijopcd.2013100103.

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This paper describes the process of boosting an innovative e-learning system in an online university in Italy. The system relies on a satellite-terrestrial telecommunication infrastructure and allows for different interaction types, including synchronous, asynchronous, textual, audio and video communication modes. The adoption of this infrastructure was preceded by a training initiative proposed to the university staff to favor its intake. The paper analyses the effects of both the training initiative and the technological innovation based on qualitative data derived from the observed differences between the pre-existing courses and their re-design and quantitative data tracked by the system during a pilot test that lasted eleven months. These data show a trend reversal in the e-learning approach, from a prevalence of transmissive mode to a more interactive one, although there is still a long way to go before more radical changes can take place.

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Barahim, Aisha, Amal Alhajri, Norah Alasaibia, Nouf Altamimi, Nida Aslam, and Irfan Ullah Khan. "Enhancing the Credit Card Fraud Detection Through Ensemble Techniques." Journal of Computational and Theoretical Nanoscience 16, no. 11 (November 1, 2019): 4461–68. http://dx.doi.org/10.1166/jctn.2019.8619.

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Nowadays people prefer to use e-commerce because of easiness, timesaving, convenience, etc. By the increase in e-commerce use, credit card fraud increases. The fraudsters get the benefit of online payments and stealing the card details. Therefore, it is essential to improve the detection methods to overcome with the fraudster’s activity and secure the card transactions. The purpose of this study is to investigate the performance of several individual different classifiers and the combination of classifiers using ensemble methods for credit card fraud detection. The study is organized as initially the three well-known classifiers i.e., Decision Tree, Naïve Bayes and SVM have been applied. Afterwards the ensemble learning module have been applied using the boosting technique with the previously mentioned classification algorithms. The dataset used is open source credit card transaction dataset containing 3075 transactions. The performance of the classification techniques is evaluated based on accuracy, sensitivity, specificity, precision, ROC value and F-measure. The result shows that Boosting with Decision Tree outperforms the other techniques.

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Mu, Fanglin, Yu Gu, Jie Zhang, and Lei Zhang. "Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques." Sensors 20, no. 15 (July 30, 2020): 4238. http://dx.doi.org/10.3390/s20154238.

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In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductor sensors is developed to identify milk sources (dairy farms) and to estimate the content of milk fat and protein which are the indicators of milk quality. The developed E-nose is a low cost and non-destructive device. For milk source identification, the features based on milk odor features from E-nose, composition features (Dairy Herd Improvement, DHI analytical data) from DHI analysis and fusion features are analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) for dimension reduction and then three machine learning algorithms, logistic regression (LR), support vector machine (SVM), and random forest (RF), are used to construct the classification model of milk source (dairy farm) identification. The results show that the SVM model based on the fusion features after LDA has the best performance with the accuracy of 95%. Estimation model of the content of milk fat and protein from E-nose features using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and random forest (RF) are constructed. The results show that the RF models give the best performance (R2 = 0.9399 for milk fat; R2 = 0.9301 for milk protein) and indicate that the proposed method in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of milk.

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Luiz, Thiago Boeno Patricio, and Thomas Schroder. "MODELOS CHUVA-VAZÃO: USO DE TÉCNICAS DE APRENDIZAGEM DE MÁQUINAS PARA CALIBRAÇÃO DE MODELOS EM UMA PEQUENA BACIA HIDROGRÁFICA." Geoambiente On-line, no. 37 (July 16, 2020): 304–21. http://dx.doi.org/10.5216/revgeoamb.vi37.62872.

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Estimativas de vazões em bacias hidrográficas baseadas em dados de precipitação pluviométrica são extremamente importantes para atividades relacionadas à gestão dos recursos hídricos. A elaboração de cenários de disponibilidade hídrica com boa precisão pode contribuir com os processos de planejamento dos recursos ambientais e evitar possíveis conflitos pelo uso da água. Este trabalho utilizou estruturas baseadas em aprendizagem de máquinas (Machine Learning) para calibrar dois modelos de chuva-vazão em escala diária na Bacia Hidrográfica do Arroio Grande no leste do Rio Grande do Sul. Foram empregados métodos de Redes Neurais Artificiais (RNA) e Gradient Boosting Machine (GBM) com a técnica bootstrap de reamostragem. O objetivo deste trabalho foi avaliar a capacidade dessas técnicas para modelar a série histórica de vazão, considerando-se a influência de dois pluviômetros localizados próximos à estação fluviométrica. A performance das técnicas utilizadas foi verificada por meio do coeficiente de determinação (R²), que atingiu 0,93 para o algoritmo de redes neurais e de 0,99 para o algoritmo de boosting, bem como pelos baixos valores do desvio absoluto. Através dos gráficos de resíduos foi possível observar o bom desempenho de calibração alcançado na aplicação dessas técnicas, onde a técnica GBM apresentou-se levemente superior à de RNA.

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Lin, Xiaolin, Xuequn Wang, and Nick Hajli. "Building E-Commerce Satisfaction and Boosting Sales: The Role of Social Commerce Trust and Its Antecedents." International Journal of Electronic Commerce 23, no. 3 (July 3, 2019): 328–63. http://dx.doi.org/10.1080/10864415.2019.1619907.

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Yamaguchi, Shinichi, Hirohide Sakaguchi, and Kotaro Iyanaga. "The Boosting Effect of E-WOM on Macro-level Consumption: A Cross-Industry Empirical Analysis in Japan." Review of Socionetwork Strategies 12, no. 2 (October 27, 2018): 167–81. http://dx.doi.org/10.1007/s12626-018-0027-4.

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Moreira, Paulo Sergio da Conceição, and Denise Fukumi Tsunoda. "Mineração de dados aplicada à classificação automática de gêneros musicais." Revista Brasileira de Computação Aplicada 11, no. 3 (September 10, 2019): 47–58. http://dx.doi.org/10.5335/rbca.v11i3.9157.

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Tem por objetivo classificar gêneros musicais automaticamente por meio de algoritmos de Mineração de Dados, considerando descritores extraídos do sinal de áudio. Identifica na Last.fm as 150 músicas mais populares de sete gêneros musicais (Rock, Jazz, POP, Música Clássica, MPB, Heavy Metal e Samba). Mediante a extração de descritores relacionados ao sinal de áudio destas músicas, aplica os algoritmos Random Forest; Bayes Net; C4.5; KNN e as estratégias Bagging e Boosting para a classificação. Obtém como melhor resultado 66,67% de acerto com o algoritmo C4.5 para classificação entre Samba e MPB. Constata que a classificação de gêneros musicais se apresenta como um "problema interessante" para estudos que envolvem técnicas de Machine Learning. Estimula a continuidade de estudos semelhantes aplicando algoritmos baseados em Redes Neurais e Algoritmos Genéticos.

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Rubin de Celis, Ethel, Zoila Aurora Cruz Burga, Nelson Carlos Rosot, Ana Paula Dalla Corte, and Hideo Araki. "Cambio de uso de la tierra en la amazonía peruana mediante algoritmos de inteligencia artificial." Journal of Biotechnology and Biodiversity 9, no. 1 (March 24, 2021): 073–84. http://dx.doi.org/10.20873/jbb.uft.cemaf.v9n1.celis.

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La investigacion tiene como objetivo analizar el mejor modelo de clasificación supervisada de imágenes satelitales para determinar el cambio de uso de la tierra entre los algoritmos Support Vector Machine (SVM) y Boosting para la Amazonía peruana. El distrito de Nueva Requena y diferentes zonas de la cuenca amazónica, enfrentan en la actualidad un alarmante cambio de cobertura forestal y cambio de uso de la tierra, generándose importantes cambios en los procesos ambientales. Se utilizó imágenes satelitales de Sentinel-2A, con longitudes de onda en el rango espectral del visible y dos algoritmos robustos: Support Vector Machine (SVM) y el algoritmo Boosting o árboles de decisión. Se realizaron 25 clasificaciones supervisadas con dichos algoritmos y diferentes insumos de las imágenes satelitales. El mejor modelo de cambio de uso de la tierra resultó de la clasificación del año 2016 con el algoritmo Boosting y para el año 2018 se realizó con algoritmo Support Vector Machine (SVM), luego mediante el algebra de mapa resultó el cambio de uso de la tierra. Este modelo presentó el menor error de clasificación de 22.7%, la validación se realizó con imágenes de alta resolución PERUSAT-1 para el año 2018 e imágenes Google Earth para el año 2016 proporcionando un índice Kappa de 0.606 y el porcentaje correctamente clasificado (PCC) de 86.10% para el año 2016 y el índice Kappa de 0.560 y el porcentaje correctamente clasificado (PCC) de 82.30% para el año 2018 demostrando la fuerza de concordancia considerable y moderada respectivamente.

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Sun, Quan, Tao Tang, Hongfeng Chai, Jie Wu, and Yang Chen. "Boosting Fraud Detection in Mobile Payment with Prior Knowledge." Applied Sciences 11, no. 10 (May 11, 2021): 4347. http://dx.doi.org/10.3390/app11104347.

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With the prevalence of mobile e-commerce, fraudulent transactions conducted by robots are becoming increasingly common in mobile payments, which is severely undermining market fairness and resulting in financial losses. It has become a difficult problem for mobile applications to identify robotic automation accurately and efficiently from a massive number of transactions. The current research does not propose any effective method or engineering implementation. In this article, an extension to boost algorithms is presented that permits the incorporation of prior human knowledge as a means of compensating for a training data shortage and improving prediction results. Prior human knowledge is accumulated from historical fraud transactions or transferred from different domains in the form of expert rules and blacklists. The knowledge is applied to extract risk features from transaction data, risk features together with normal features are input into the boosting algorithm to perform training, and therefore we incorporate boosting algorithm with prior human knowledge to improve the performance of the model. For the first time we verified the effectiveness of the method via a widely deployed mobile APP with 150+ million users, and by taking experiments on a certain dataset, the extended boosting model shows an accuracy increase from 0.9825 to 0.9871 and a recall rate increase from 0.888 to 0.948. We also investigated feature differences between robots and normal users and we discovered the behavior patterns of robotic automation that include less spatial motion detected by device sensors (1/10 of normal user pattern), higher IP group-clustering ratio (60% in robots vs. 15% in normal users), higher jailbroken device rate (92.47% vs. 4.64%), more irregular device names and fewer IP address changes. The quantitative analysis result is helpful for APP developers and service providers to understand and prevent fraudulent transactions from robotic automation.This article proposed an optimized boosting model, which has better use in the field of robotic automation detection of mobile phones. By combining prior knowledge and feature importance analysis, the model is more robust when the actual dataset is unbalanced or with few-short samples. The model is also more explainable as feature analysis is available which can be used for generating disposal rules in the actual fake mobile user blocking systems.

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Liu, Shimin, David Masters, Mark Ferguson, and Andrew Thompson. "Vitamin E status and reproduction in sheep: potential implications for Australian sheep production." Animal Production Science 54, no. 6 (2014): 694. http://dx.doi.org/10.1071/an13243.

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Vitamin E concentrations in dried pastures, stubble and most grains are below the recommended requirement of 10–25 mg/kg dry matter (DM). Sheep grazing in an environment when dry pastures and cereal crop stubbles are their primary source of nutrients for a few months have a high risk of developing vitamin E deficiency. If the low vitamin E status coincides with late gestation, the neonate is likely to have a deficiency of vitamin E. Some of the consequences of this are well known, with nutritional myopathy (with high mortality) a risk in young growing sheep unless vitamin E supplements are provided. Vitamin E plays an important role in the management of oxidative stress. Sperm are subject to oxidative damage due to high metabolic rate and high concentration of polyunsaturated fatty acids in their membranes. Oxidative stress may also compromise follicular development and ovarian activity. Vitamin E is also involved with improvement in immune response. For these reasons, vitamin E status is important for reproductive efficiency in both males and females and in the survival of lambs and weaners. In addition, vitamin E deficiency is potentially exacerbated by a lack of other nutrients involved in the management of oxidative stress and immune function, such as selenium (Se) and sulfur amino acids. A Se concentration of 0.1 mg/kg DM in feedstuffs is required to maintain immune competency in sheep. In considering possible consequences for reproduction, further investigation is justified into: (i) effects of low vitamin E, in combination with low levels of other natural antioxidants, on the quality and quantity of sperm produced before and during mating; (ii) follicle development, fertilisation and embryonic mortality in Se-supplemented ewes; (iii) assessment of supplementing formulated antioxidants to rams and ewes during the mating season; (iv) managing oxidative stress in the newborn – consequences of large doses of vitamin E to ewes before parturition to boost lamb reserves; (v) potential benefits to lamb survival through boosting maternal innate immunity; (vi) choices for boosting antioxidant and immune function in ewes and lambs through ‘immune pack’ nutrient options that may target nutrients lacking in dry grass pastures; (vii) the potential role of heat stress in modifying the requirements for, and responses to, vitamin E in extensive grazing systems.

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Li, Xueling, and Zhen Li. "A Hybrid Prediction Model for E-Commerce Customer Churn Based on Logistic Regression and Extreme Gradient Boosting Algorithm." Ingénierie des systèmes d information 24, no. 5 (November 26, 2019): 525–30. http://dx.doi.org/10.18280/isi.240510.

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Xia, Yang, Lan-Fang Que, Fu-Da Yu, Liang Deng, Chang Liu, Xu-Lei Sui, Lei Zhao, and Zhen-Bo Wang. "Boosting ion/e− transfer of Ti3C2 via interlayered and interfacial co-modification for high-performance Li-ion capacitors." Chemical Engineering Journal 404 (January 2021): 127116. http://dx.doi.org/10.1016/j.cej.2020.127116.

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Li, Xing Hong, Xian Xiang, and Xiao Guang Du. "Servo Controller Application and Design in ATP System." Applied Mechanics and Materials 742 (March 2015): 540–45. http://dx.doi.org/10.4028/www.scientific.net/amm.742.540.

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With the rapid development of computer technology, it is widely used in the control field, including advanced alarm plane,cruise missile,radar,O-E theodolite,etc. The increasing complex and real-time arithmetic was applied in the ATP(Acquire Tracking Pointing) system of the control system for boosting the track、captring and pointing precision of O-E(photo-electric) theodolite, so it demanded the servo controller must complete more and more operations in shorter time. While the actual O-E theodolite accomplished the capture and tracking etc. based on PC104 system, so designed the servo control system based on embedded chip as CPU from the practicality, not only decreases the cubage of the system, but also increases the reliability and precision.

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Peng, Tu, Xiaoya Chen, Ming Wan, Lizhu Jin, Xiaofeng Wang, Xuejie Du, Hui Ge, and Xu Yang. "The Prediction of Hepatitis E through Ensemble Learning." International Journal of Environmental Research and Public Health 18, no. 1 (December 28, 2020): 159. http://dx.doi.org/10.3390/ijerph18010159.

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According to the World Health Organization, about 20 million people are infected with Hepatitis E every year. In 2015, there were 44,000 deaths due to HEV infection worldwide. Food, water and climate are key factors that affect the outbreak of Hepatitis E. This paper presents an ensemble learning model for Hepatitis E prediction by studying the correlation between historical epidemic cases of hepatitis E and environmental factors (water quality and meteorological data). Environmental factors include many features, and ones that are most relevant to HEV are selected and input into the ensemble learning model composed by Gradient Boosting Decision Tree (GBDT) and Random Forest for training and prediction. Three indicators, root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), are used to evaluate the effectiveness of the ensemble learning model against the classical time series prediction model. It is concluded that the ensemble learning model has a better prediction effect than the classical model, and the prediction effectiveness can be improved by exploiting water quality and meteorological factors (radiation, air pressure, precipitation).

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Tung Soon Theam, Puvaneswari Veloo, Nor Haliza Binti Che Hussain, and Yap Kim Luu. "Travellers’ Satisfaction on the Applications of Artificial Intelligence in Malaysia’s Tourism and Hospitality Industry." Asia Proceedings of Social Sciences 8, no. 1 (May 30, 2021): 38–42. http://dx.doi.org/10.31580/apss.v8i1.1947.

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Artificial intelligence (AI) is perceived as being able to transform tourism and hospitality industry’s operations into a greater efficiency and cost-effectiveness while offering travellers unique experiences. This study examines travellers’ satisfaction of AI applications, specifically through e-Hailing, e-Wallet, e-Gate and e-Visa in the tourism and hospitality industry in Malaysia. Quantitative research approach was adopted in the current study. Data was gathered from 200 respondents using self-administrative questionnaires. Local and foreign travellers of age 18 and above who had past travel experience were chosen as samples. The findings indicated that e-Hailing, e-Gate and e-Wallet significantly influence travellers’ satisfaction. However, the outcome shows that e-Visa application has no relationship with travellers’ satisfaction. This study helps to strengthen the tourism ministry’s current initiatives in boosting the tourism and hospitality industry in Malaysia. The outcome of the study might be of interest to the policy makers and regulators to improve on the applications of AI in tourism and hospitality in Malaysia.

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Alqahtani, Ammar Y., and Albraa A. Rajkhan. "E-Learning Critical Success Factors during the COVID-19 Pandemic: A Comprehensive Analysis of E-Learning Managerial Perspectives." Education Sciences 10, no. 9 (August 20, 2020): 216. http://dx.doi.org/10.3390/educsci10090216.

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During the COVID-19 pandemic, educational institutions were shut down all over the world, which impacted over 60% of students and caused a massive disruption of the education system. The goal of this paper was to identify the critical success factors for E-learning during COVID-19 using the multi-criteria Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) techniques to enhance the educational process. Data were generated by interviewing 69 E-learning managers in educational institutions during COVID-19 based on defined evaluation criteria and E-learning approaches through several channels. We found that technology management, support from management, increased student awareness to use E-learning systems, and demanding a high level of information technology from instructors, students, and universities were the most influential factors for E-learning during COVID-19. Among the five learning systems, blended learning was the most suitable learning system to practice. These results demonstrated that, regardless of how extraordinary the technology is in an educational institution, the readiness of E-learning execution played a large role in boosting the educational process during the COVID-19 pandemic.

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Salvetti, Fernando, and Barbara Bertagni. "An e-REAL Lab in Dubai. Immersive Experiences, Visual Communication and Augmented Reality." International Journal of Advanced Corporate Learning (iJAC) 8, no. 3 (October 8, 2015): 34. http://dx.doi.org/10.3991/ijac.v8i3.4912.

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e-REAL is an enhanced reality lab by LKN and is at the forefront by design for learners’ centricity. It is becoming a cornerstone as a suite of solutions based on immersive experiences, visual communication and augmented reality. e-REAL is both a physical and a virtual ecosystem for boosting people and fostering their competencies: it embeds augmented reality tools, mobile applications, holograms and wearable devices to be used during education and training programs, as well as being utilized for assessment and development centers. e-REAL can be delivered both face to face and remotely. By implementing e-REAL, a myriad of competencies are fostered and audited: this includes behavioral skills, as well as cognitive and metacognitive capacity. Technical know-how and job-related competencies are also honed and attested. As a result, people’s performance is expected to grow; measuring return on investment and the outputs in terms of competencies’ growth is an easy task; the most demanding traceability standards are guaranteed.

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Abidi, Syed Muhammad Raza, Wu Zhang, Saqib Ali Haidery, Sanam Shahla Rizvi, Rabia Riaz, Hu Ding, and Se Jin Kwon. "Educational Sustainability through Big Data Assimilation to Quantify Academic Procrastination Using Ensemble Classifiers." Sustainability 12, no. 15 (July 28, 2020): 6074. http://dx.doi.org/10.3390/su12156074.

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Ubiquitous online learning is continuing to expand, and the factors affecting success and educational sustainability need to be quantified. Procrastination is one of the compelling characteristics that students observe as a failure to achieve the weaker outcomes. Past studies have mainly assessed the behaviors of procrastination by describing explanatory work. Throughout this research, we concentrate on predictive measures to identify and forecast procrastinator students by using ensemble machine learning models (i.e., Logistic Regression, Decision Tree, Gradient Boosting, and Forest). Our results indicate that the Gradient Boosting autotuned is a predictive champion model of high precision compared to the other default and hyper-parameterized tuned models in the pipeline. The accuracy we enumerated for the VALIDATION partition dataset is 91.77 percent, based on the Kolmogorov–Smirnov statistics. Additionally, our model allows teachers to monitor each procrastinator student who interacts with the web-based e-learning platform and take corrective action on the next day of the class. The earlier prediction of such procrastination behaviors would assist teachers in classifying students before completing the task, homework, or mastery of a skill, which is useful and a path to developing a sustainable atmosphere for education or education for sustainable development.

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Singgih, Ivan Kristianto. "Production Flow Analysis in a Semiconductor Fab Using Machine Learning Techniques." Processes 9, no. 3 (February 24, 2021): 407. http://dx.doi.org/10.3390/pr9030407.

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In a semiconductor fab, wafer lots are processed in complex sequences with re-entrants and parallel machines. It is necessary to ensure smooth wafer lot flows by detecting potential disturbances in a real-time fashion to satisfy the wafer lots’ demands. This study aims to identify production factors that significantly affect the system’s throughput level and find the best prediction model. The contributions of this study are as follows: (1) this is the first study that applies machine learning techniques to identify important real-time factors that influence throughput in a semiconductor fab; (2) this study develops a test bed in the Anylogic software environment, based on the Intel minifab layout; and (3) this study proposes a data collection scheme for the production control mechanism. As a result, four models (adaptive boosting, gradient boosting, random forest, decision tree) with the best accuracies are selected, and a scheme to reduce the input data types considered in the models is also proposed. After the reduction, the accuracy of each selected model was more than 97.82%. It was found that data related to the machines’ total idle times, processing steps, and machine E have notable influences on the throughput prediction.

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Mellado-Sánchez, Gabriela, Julio García-Cordero, Rosendo Luria-Pérez, Lucero Lázaro-Olan, Leopoldo Santos-Argumedo, Benito Gutiérrez-Castañeda, Iris Estrada-García, and Leticia Cedillo-Barrón. "DNA Priming E and NS1 Constructs–Homologous Proteins Boosting Immunization Strategy to Improve Immune Response Against Dengue in Mice." Viral Immunology 18, no. 4 (December 2005): 709–21. http://dx.doi.org/10.1089/vim.2005.18.709.

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Muñoz, Laura Alcaide, and Raquel Garde Sánchez. "Implementation of E-Government and Reforms in Public Administrations in Crisis Periods." International Journal of Public Administration in the Digital Age 2, no. 1 (January 2015): 1–23. http://dx.doi.org/10.4018/ijpada.2015010101.

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The importance of e-Government in the reform of public administrations has made it an essential element on political agendas and an important question to be addressed in the current economic crisis that we are witnessing. The consequent drastic reduction in public revenue has made e-Government a key element in the promotion of renewed and sustainable growth with the aim of increasing efficiency and effectiveness in the management of procedures and boosting service provision. In this respect, the considerable amount of research that currently exists in academic literature requires a comprehensive review that allows for improvement in this field of knowledge, offering a broad vision of the current situation and the research possibilities for the future. The authors believe that their findings will allow public managers to be more aware of the need for a cost-benefit analysis of the new technological initiatives proposed.

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Duc, Duong Tran, Pham Bao Son, Tan Hanh, and Le Truong Thien. "A Resamping Approach for Customer Gender Prediction Based on E-Commerce Data." Journal of Science and Technology: Issue on Information and Communications Technology 3, no. 1 (March 31, 2017): 76. http://dx.doi.org/10.31130/jst.2017.40.

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Demographic attributes of customers such as gender, age, etc. provide the important information for e-commerce service providers in marketing, personalization of web applications. However, the online customers often do not provide this kind of information due to the privacy issues and other reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, the products viewed, etc. The main idea is that we extract the features from catalog viewing information and employ the classification methods to predict the gender of the viewers. The experiments were conducted on the datasets provided by the PAKDD’15 Data Mining Competition and obtained the promising results with a simple feature design, especially with the Bayesian Network method along with other supporting techniques such as resampling, cost-sensitive learning, boosting etc.

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Teja, P. Sai. "Prediction of Spam Email using Machine Learning Classification Algorithm." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 15, 2021): 1107–12. http://dx.doi.org/10.22214/ijraset.2021.35226.

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Unsolicited e-mail also known as Spam has become a huge concern for each e-mail user. In recent times, it is very difficult to filter spam emails as these emails are produced or created or written in a very special manner so that anti-spam filters cannot detect such emails. This paper compares and reviews performance metrics of certain categories of supervised machine learning techniques such as SVM (Support Vector Machine), Random Forest, Decision Tree, CNN, (Convolutional Neural Network), KNN(K Nearest Neighbor), MLP(Multi-Layer Perceptron), Adaboost (Adaptive Boosting) Naïve Bayes algorithm to predict or classify into spam emails. The objective of this study is to consider the details or content of the emails, learn a finite dataset available and to develop a classification model that will be able to predict or classify whether an e-mail is spam or not.

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Zhang, Yang, Zhun Cheng, Qing Chen, and Qingmei Li. "An Enhanced Half-Quasi-Z-Source Inverter for Wind Energy Conversion System with D-PMSG." Complexity 2021 (July 2, 2021): 1–15. http://dx.doi.org/10.1155/2021/9962115.

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To solve the problem of the traditional quasi-Z-source inverters with low voltage gain, an enhanced half-quasi-Z-source inverter (E-HQZSI) is proposed and applied to direct-drive permanent-magnet wind power generation systems in this paper. The expression of the boosting factor is deduced, which shows that E-HQZSI has higher voltage gain compared with the QZSI and HQZSI. However, the higher voltage gain of the E-HQZSI will lead to the large distortion of the generator stator current necessarily. In this paper, a periodic shoot-through duty ratio control scheme is proposed to reduce the stator current harmonics for E-HQZSI. According to the change rule of the single-phase stator current, the shoot-through duty ratio is compromised to make that the three-phase stator currents are as close as possible to the sine wave. Finally, the correctness of the theoretical analysis is verified by simulation and experiment.

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Nazari, Shima, Jason Siegel, Robert Middleton, and Anna Stefanopoulou. "Power Split Supercharging: A Mild Hybrid Approach to Boost Fuel Economy." Energies 13, no. 24 (December 14, 2020): 6580. http://dx.doi.org/10.3390/en13246580.

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This work investigates an innovative low-voltage (<60 V) hybrid device that enables engine boosting and downsizing in addition to mild hybrid functionalities such as regenerative braking, start-stop, and torque assist. A planetary gear set and a brake permit the power split supercharger (PSS) to share a 9 kW motor between supercharging the engine and direct torque supply to the crankshaft. In contrast, most e-boosting schemes use two separate motors for these two functionalities. This single motor structure restricts the PSS operation to only one of the supercharging or parallel hybrid modes; therefore, an optimized decision making strategy is necessary to select both the device mode and its power split ratio. An adaptive equivalent consumption minimization strategy (A-ECMS), which uses the battery state of charge (SoC) history to adjust the equivalence factor, is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces vehicle fuel consumption by 18.4% over standard FTP75 cycle, compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to the baseline.

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Famewo, Elizabeth Bosede, Anna Maria Clarke, and Anthony Jide Afolayan. "Evaluation of important mineral nutrients and vitamins in polyherbal medicines used for the treatment of tuberculosis in the Eastern Cape Province, South Africa." International Journal of Phytomedicine 10, no. 1 (April 30, 2018): 16. http://dx.doi.org/10.5138/09750185.2139.

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<p>Polyherbal medicines are widely used for the treatment of various diseases in the developing countries. In order to validate their ability in boosting the immune system of tuberculosis patients, the mineral nutrients and vitamins present were determined.<strong> </strong>Their nutritive properties were analysed using an inductively coupled plasma optical emission spectrometer, while the vitamins were determined using standardized methods. The polyherbal preparations were found to be rich in mineral nutrients and vitamins. Calcium was the highest mineral nutrient detected, while the lowest nutrient was phosphorus. Quantitatively, calcium and magnesium contents in the remedies ranged from 973.30 to 6503.30 mg/100g and 80.00 to 406.00 mg/100g respectively. The amount of phosphorus and potassium was between 20.00 and 263.30 mg/100g; 160.00 and 2050.00 mg/100g respectively. Micro nutrients such as iron, manganese, zinc, aluminium and copper were also detected. Iron was the highest nutrient in the majority of the polyherbal preparations while the lowest value was recorded for copper. However, vitamin C was absent in the herbal preparations while vitamin A and E were detected. These findings indicate that these polyherbal formulations contain the essential mineral nutrients and vitamins that could probably be boosting the immune system of tuberculosis patients. </p>

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Saxena, Sanyam, and Akhil Muralidharan. "Novel Design of Solar Cooker with Bottom Feed." Applied Mechanics and Materials 592-594 (July 2014): 2391–95. http://dx.doi.org/10.4028/www.scientific.net/amm.592-594.2391.

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Efforts for harnessing solar energy were made many decades ago. Solar cooking was opted worldwide as a convenient and economical method to cook food. Since then, several investigators have studied various aspects of solar cooking. The studies on solar cookers can be broadly classified into the following categories: (a) design, fabrication and testing of new types of solar cookers, (b) methods of boosting the solar energy on the cooker aperture using booster mirrors,(c) energy storage types of cookers, for use indoors and also during off sunshine periods, (d) tests on different types of cooking vessels and (e) modeling and simulation techniques.

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Dos Santos, Isabelly Raiane Silva. "Tecnologias educacionais na educação: impulsionando a autonomia e a motivação discente / Educational technologies in education: boosting student autonomy and motivation." Brazilian Journal of Development 7, no. 8 (August 12, 2021): 80427–43. http://dx.doi.org/10.34117/bjdv7n8-315.

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Musiał-Karg, Magdalena, and Izabela Kapsa. "Citizen e-Participation as an Important Factor for Sustainable Development." European Journal of Sustainable Development 8, no. 3 (October 1, 2019): 210. http://dx.doi.org/10.14207/ejsd.2019.v8n3p210.

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Citizen e-participation – in times of rapid ICT advancement – is an important factor contributing to the development of contemporary democracies. The use of electronic citizen involvement tools provides numerous changes in the relationship between the citizen and public institution, especially by boosting citizen engagement in decision making. Civic participation is a key factor in determining effective and inclusive governance at the local and national levels. As many organizations have recognized, contemporary democracy needs to ensure a responsive, inclusive, participatory and representative decision-making process, emphasizing the importance of those sustainable development factors. Moreover, National Councils for Sustainable Development were once considered critical to achieving integration in decision-making and participation, two dimensions that were at the heart of the sustainable development concept. The article presents citizen e-participation as an important factor for sustainable development. The purpose is to explain theoretical and empirical meaning of citizen e-participation for sustainable development. To achieve this goal, the authors used the following research methods: critical review of the literature and quantitative data analysis (data examined come from author’s own research (2018) into use and willingness to use e-government tools by Poles). Research findings may be an important contribution to the development of civic participation and the implicit value of the process. Keywords: citizen participation, e-democracy, e-government, e-participation, Poland, sustainable development.

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Liu, Huixiang, Qing Li, Bin Yan, Lei Zhang, and Yu Gu. "Bionic Electronic Nose Based on MOS Sensors Array and Machine Learning Algorithms Used for Wine Properties Detection." Sensors 19, no. 1 (December 22, 2018): 45. http://dx.doi.org/10.3390/s19010045.

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In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction of wines with different properties, including areas of production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms—extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and backpropagation neural network (BPNN)—were used to build identification models for different classification tasks. Experimental results show that BPNN achieved the best performance, with accuracies of 94% and 92.5% in identifying production areas and varietals, respectively; and SVM achieved the best performance in identifying vintages and fermentation processes, with accuracies of 67.3% and 60.5%, respectively. Results demonstrate the effectiveness of the developed E-nose, which could be used to distinguish different wines based on their properties following selection of an optimal algorithm.

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Kerviel, Arthur, Apostolos Pesyridis, Ahmed Mohammed, and David Chalet. "An Evaluation of Turbocharging and Supercharging Options for High-Efficiency Fuel Cell Electric Vehicles." Applied Sciences 8, no. 12 (December 3, 2018): 2474. http://dx.doi.org/10.3390/app8122474.

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Mass-produced, off-the-shelf automotive air compressors cannot be directly used for boosting a fuel cell vehicle (FCV) application in the same way that they are used in internal combustion engines, since the requirements are different. These include a high pressure ratio, a low mass flow rate, a high efficiency requirement, and a compact size. From the established fuel cell types, the most promising for application in passenger cars or light commercial vehicle applications is the proton exchange membrane fuel cell (PEMFC), operating at around 80 °C. In this case, an electric-assisted turbocharger (E-turbocharger) and electric supercharger (single or two-stage) are more suitable than screw and scroll compressors. In order to determine which type of these boosting options is the most suitable for FCV application and assess their individual merits, a co-simulation of FCV powertrains between GT-SUITE and MATLAB/SIMULINK is realised to compare vehicle performance on the Worldwide Harmonised Light Vehicle Test Procedure (WLTP) driving cycle. The results showed that the vehicle equipped with an E-turbocharger had higher performance than the vehicle equipped with a two-stage compressor in the aspects of electric system efficiency (+1.6%) and driving range (+3.7%); however, for the same maximal output power, the vehicle’s stack was 12.5% heavier and larger. Then, due to the existence of the turbine, the E-turbocharger led to higher performance than the single-stage compressor for the same stack size. The solid oxide fuel cell is also promising for transportation application, especially for a use as range extender. The results show that a 24-kWh electric vehicle can increase its driving range by 252% due to a 5 kW solid oxide fuel cell (SOFC) stack and a gas turbine recovery system. The WLTP driving range depends on the charge cycle, but with a pure hydrogen tank of 6.2 kg, the vehicle can reach more than 600 km.

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Shakoor, Hira, Jack Feehan, Ayesha S. Al Dhaheri, Habiba I. Ali, Carine Platat, Leila Cheikh Ismail, Vasso Apostolopoulos, and Lily Stojanovska. "Immune-boosting role of vitamins D, C, E, zinc, selenium and omega-3 fatty acids: Could they help against COVID-19?" Maturitas 143 (January 2021): 1–9. http://dx.doi.org/10.1016/j.maturitas.2020.08.003.

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Zhang, Wei, Wenchao Li, Jianming Zhang, and Ning Wang. "Data Integration of Hybrid Microarray and Single Cell Expression Data to Enhance Gene Network Inference." Current Bioinformatics 14, no. 3 (March 7, 2019): 255–68. http://dx.doi.org/10.2174/1574893614666190104142228.

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Background: Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference. Objective: To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution. Method: In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list. Results: Two groups of in silico gene networks are applied to illustrate the effectiveness of the proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene networks suggest that the proposed E-alpha rule significantly improves performance metrics compared with single information source. Conclusion: In GRN inference, the integration of hybrid expression data using E-alpha rule provides a feasible and efficient way to enhance performance metrics than solely increasing sample sizes.

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Rahmawati, Miftah Sigit, and Rendra Soekarta. "Social Media-Based E-learning and Online Assignments on Algebraic Materials." Jurnal Pendidikan Matematika 15, no. 2 (June 30, 2021): 175–90. http://dx.doi.org/10.22342/jpm.15.2.13714.175-190.

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This study aims at evaluating the application of social media-based e-learning and online assignments during Covid-19 pandemic based on: (1) the availability of facilities and infrastructure in implementing social media-based e-learning and online assignments during the Covid-19 pandemic, (2) comprehension and management of e-learning and online assignments by lecturers and students, (3) social media-based e-learning and online assignments. This study is a qualitative descriptive study using the CIPP evaluation by evaluating each component, including context, input, process and product/outcome. The sources of study data involved lecturers and students of Informatics Engineering at Muhammadiyah University Sorong in Matrix Algebra course. The instruments of primary data collection was online assignments and Google Form questionnaires, while secondary data was obtained through observation, literature study, documentation and interviews. The results show that students obtained an overall average score (mean) of 76.4 from the maximum score of 100, and a percentage of assignment collection of 65.78%. This results were categorized as adequate, in meaning it is rather effective for theory comprehension, and was categorized as moderate in terms of boosting students’ motivation in doing social media-based online assignments, depending on the type of assignment. This signifies that the evaluation of CIPP in social media-based e-learning and online assignments in algebra has positive outcome in terms of infrastructure, management, and use.

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Branch, Jessie, Badri S. Rajagopal, Alessandro Paradisi, Nick Yates, Peter J. Lindley, Jake Smith, Kristian Hollingsworth, et al. "C-type cytochrome-initiated reduction of bacterial lytic polysaccharide monooxygenases." Biochemical Journal 478, no. 14 (July 28, 2021): 2927–44. http://dx.doi.org/10.1042/bcj20210376.

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The release of glucose from lignocellulosic waste for subsequent fermentation into biofuels holds promise for securing humankind's future energy needs. The discovery of a set of copper-dependent enzymes known as lytic polysaccharide monooxygenases (LPMOs) has galvanised new research in this area. LPMOs act by oxidatively introducing chain breaks into cellulose and other polysaccharides, boosting the ability of cellulases to act on the substrate. Although several proteins have been implicated as electron sources in fungal LPMO biochemistry, no equivalent bacterial LPMO electron donors have been previously identified, although the proteins Cbp2D and E from Cellvibrio japonicus have been implicated as potential candidates. Here we analyse a small c-type cytochrome (CjX183) present in Cellvibrio japonicus Cbp2D, and show that it can initiate bacterial CuII/I LPMO reduction and also activate LPMO-catalyzed cellulose-degradation. In the absence of cellulose, CjX183-driven reduction of the LPMO results in less H2O2 production from O2, and correspondingly less oxidative damage to the enzyme than when ascorbate is used as the reducing agent. Significantly, using CjX183 as the activator maintained similar cellulase boosting levels relative to the use of an equivalent amount of ascorbate. Our results therefore add further evidence to the impact that the choice of electron source can have on LPMO action. Furthermore, the study of Cbp2D and other similar proteins may yet reveal new insight into the redox processes governing polysaccharide degradation in bacteria.

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YU, FU LAI TONY. "Private Enterprise Development in a One-Party Autocratic State: The Case of Alibaba Group in China’s E-Commerce." Issues & Studies 54, no. 01 (March 2018): 1850001. http://dx.doi.org/10.1142/s1013251118500017.

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This study attempts to explain China’s industrial development with special reference to e-commerce. It argues that in a one-party autocratic regime such as China, the collaboration between government officials and private entrepreneurs in strategic industries can promote industrial growth. Since Internet can jeopardize communist party’s goal of maintaining cohesiveness and absolute political power, the Chinese government has imposed surveillance on private operation in all IT operations. Specifically, in e-commerce industry, through collaborations with private enterprises, the communist party can “kill two birds in one arrow.” On the one hand, party members are able to preserve national security and maintain social and financial stability by closely monitoring the private enterprise operation. Moreover, party members can seize tangible and non-tangible benefits from the growth in e-commerce firms. On the other hand, private e-commerce enterprises, by building close connection with public officials and senior party members, can obtain strong support from the government, and thus boosting its business growth. This argument is applied to explain the miraculous growth of Alibaba Group, a private e-commerce enterprise in China. In particular, the paper attempts to show the relationship between the Chinese government and the private entrepreneur in the e-business development and how their collaboration enhances growth in the Internet market.

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Journal articles on the topic 'E-boosting'

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