Urte biko gelako gatazkak: izaera eta esku-hartzea

[EUS] Ikerketa lan honen helburua urte biko gela batean ematen diren egoera gatazkatsuak eta horietan burutzen diren esku-hartze mota desberdinak aztertzea izan da. Horretarako, bi alditan banandu da ikerketa honen zeregina: fase esploratzailean eta fase zientifikoan, hain zuzen. Lehenengoan, gaiari buruzko hausnarketa teoriko sakon bat gauzatzearekin batera, ikerketaren behaketa esparruak zehaztu dira, hala nola, zer, noiz, non eta nola behatuko den. Orduan, goizeko une gatazkatsuenak behatzea erabaki da; saio kolektiboa, txokoak eta batzeko unea, hain zuzen. Ondoren, fase zientifikoan, hiru esparru horietan behatutako datu guztiak bildu eta hainbat taula eta grafiko eratu dira. Lortutako emaitzen arabera, argi gelditu delarik eskolan biolentzia erabiltzea nahiko ohikoa dela; autoritarismoa, diziplina inposaketa eta boterearen erabilera bortitza. Hori dela eta, batetik garatutako lan enpirikoan eta bestetik Pikler-Lóczy eskolatze goiztiarraren hezkuntza-eredu arrakastatsuan oinarritutako hausnarketa egin da, gatazka egoeretan erabili daitezkeen baliabide nahiz esku-hartze mota kalitatezko eta egokiagoak eskaintzeko asmotan.

Multiple SNP Markers Reveal Fine-Scale Population and Deep Phylogeographic Structure in European Anchovy (Engraulis encrasicolus L.)

10 p.

A reflection on the future of the quality management paradigm in the EU

In recent years, the Quality Management Paradigm has successfully taken root in the European Union’s business environment. Quality management besides being a multivariate issue including matters from management and economics till engineering may be called a global knowledge in permanent bubbling. This theoretical article is an eclectic effort to analyse the evolution of the Quality Management Paradigm. More specifically, the article deals with this management Paradigm evolution and change according to the present and future expected business environments.

Deep learning review and its applications

Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learning algorithms in diverse areas such as speech recognition, computer vision and bioinformatics. Convolutional networks especially have shown prowess in visual recognition tasks such as object recognition and detection in which this work is focused on. Mod- ern award-winning architectures have systematically surpassed previous attempts at tackling computer vision problems and keep winning most current competitions. After a brief study of deep learning architectures and readily available frameworks and libraries, the LeNet handwriting digit recognition network study case is developed, and lastly a deep learn- ing network for playing simple videogames is reviewed.