Motivasi Guru Honorer Dalam Mendidik Siswa Berkebutuhan Khusus

Education is rights and obligations for each individual. The right means anyone have a right to receive education, even children with specifically need (disabilities). Obligation means individual in general has an obligation to monitor the implementation of education. Teachers, students, facilities and infrastructure, and educational environment is a factor that influences sustainability the implementation of education. Teachers have an important role as spearheads education. Judging from the stature, not all teachers are civil servants, including non permanent teachers do not have the standart of the salary. To improve the quality of educational students with specifically need (disabilities) for motivation of teachers, including honorary teacher. Imdividuals can have different motivation as a proper of his life. The purpose of this research to konows all factor that influences motivation of teachers paid by honorarium in educate students with specifically needs. The research was done with the methods interview. Informants research as many as 6 (six) a honorary teacher who works educate students with specifically needs at remarkable school, selescted purposively of sampling consisting of 3 for female teachers who han been married, and 3 for female teachers who has not been married. The research result show virtues and social support a factor that affects motivation of honorary teacher in educating students with specifically needs. Keywords: motivation, virtues, social support, children with specifically need

Polygyny or Misogyny? Reexamining the “First Law of Intergroup Conflict”

Kanazawa (2009) proposes a "first law of intergroup conflict," suggesting that polygyny and its impact on access to reproductive women provides "the ultimate cause" for civil war. This controversial claim is supported by an empirical analysis at odds with most existing studies of civil wars. We reconsider the influence of polygyny in a more conventional statistical model. We fail to find evidence that ethnic groups with polygyny engage more frequently in civil wars, although it is possible to find results indicating that civil wars may be more common in states with legal polygamy. We detail how these findings seem at odds with Kanazawa's theory and argue that misogyny seems a more plausible source of insights into the context for civil war and peace. We then show that civil wars are less common when women's rights are better established and that legal polygamy has no discernable residual effect once women's rights are considered. © 2011 Southern Political Science Association

Hydrogeological impact assessment by tunnelling at sites of high sensitivity

A tunnel for the High Speed Train (HST) was constructed in Barcelona with an Earth Pressure Balance (EPB) Tunnel Boring Machine (TBM). The tunnel crosses Barcelona and passes under some famous landmarks such as the Sagrada Familia and the Casa Mill Both monuments are UNESCO world heritage sites and a committee appointed by the UNESCO acted as external observers during the construction. Concerns about soil settlements and the hydrogeological impacts of the construction were raised. These concerns were addressed during the design stage to forestall any unexpected events. The methodology consisted of 1) characterising the geology in detail, 2) predicting the impacts caused in the aquifer, 3) predicting the soil displacements due to water table oscillations produced by the construction, and 4) monitoring the evolution of groundwater and soil settlements. The main estimated impact on groundwater was a moderate barrier effect. The barrier effect, the magnitude of which matched the predictions, was detected during construction. The monitoring of soil settlements revealed short and long term movements. The latter movements matched the analytical predictions of soil displacements caused by the groundwater oscillations. This paper proposes a realistic procedure to estimate impacts on groundwater during tunnel construction with an EPB. Our methodology will considerably improve the construction of tunnels in urban areas. (C) 2015 Elsevier B.V. All rights reserved.Peer ReviewedPostprint (author's final draft

Estimating carrion biomass originating from Red deer Cervus elaphus and its ecological effect on vertebrate scavengers in Vestland county, Norway.

Ungulates make up the majority of carrion in numerous terrestrial ecosystems. Humans have removed large carnivores from many ecosystems, and carcass waste from human hunting has taken over as the most important source of carrion. Understanding the availability of carrion is therefore crucial for comprehending its ecological impact, and my goal was to assess carrion availability in Vestland county in western Norway. I used literature to calculate the amount of red deer carrion biomass from several different causes, harvest, traffic accidents and other causes of death. I conducted a questionnaire to investigate how hunters use offal and how they treat waste from the harvest. Additionally, I investigated which scavengers that utilized carrion from red deer in an area of Vestland county that had low numbers of apex predators using eleven camera traps. I found that across Norway, a significant amount of biomass from red deer is left available for the scavengers from both the annual hunting season and from other causes of death. I found that the hunters often leave waste from harvest available for the scavengers. Based on camera traps, I found that the three corvid species; crow, raven and magpie are the most abundant scavengers with 95% of all visits. This study showed that hunters in Norway and particularly Vestland county contributed with large amount of carrion through harvest of red deer, in addition to traffic accidents and death by other causes. Carrion is often left available for scavengers and that the most observed scavengers in the study site were corvids. Based on the findings, I expect increased survival of corvids due to large amount of carrion available

Feature Selection for Text Categorisation

Text categorization is the task of discovering the category or class text documents belongs to, or in other words spotting the correct topic for text documents. While there today exists many machine learning schemes for building automatic classifiers, these are typically resource demanding and do not always achieve the best results when given the whole contents of the documents. A popular solution to these problems is called feature selection. The features (e.g. terms) in a document collection are given weights based on a simple scheme, and then ranked by these weights. Next, each document is represented using only the top ranked features, typically only a few percent of the features. The classifier is then built in considerably less time, and might even improve accuracy. In situations where the documents can belong to one of a series of categories, one can either build a multi-class classifier and use one feature set for all categories, or one can split the problem into a series of binary categorization tasks (deciding if documents belong to a category or not) and create one ranked feature subset for each category/classifier. Many feature selection metrics have been suggested over the last decades, including supervised methods that make use of a manually pre-categorized set of training documents, and unsupervised methods that need only training documents of the same type or collection that is to be categorized. While many of these look promising, there has been a lack of large-scale comparison experiments. Also, several methods have been proposed the last two years. Moreover, most evaluations are conducted on a set of binary tasks instead of a multi-class task as this often gives better results, although multi-class categorization with a joint feature set often is used in operational environments. In this report, we present results from the comparison of 16 feature selection methods (in addition to random selection) using various feature set sizes. Of these, 5 were unsupervised , and 11 were supervised. All methods are tested on both a Naive Bayes (NB) classifier and a Support Vector Machine (SVM) classifier. We conducted multi-class experiments using a collection with 20 non-overlapping categories, and each feature selection method produced feature sets common for all the categories. We also combined feature selection methods and evaluated their joint efforts. We found that the classical supervised methods had the best performance, including Chi Square, Information Gain and Mutual Information. The Chi Square variant GSS coefficient was also among the top performers. Odds Ratio showed excellent performance for NB, but not for SVM. The three unsupervised methods Collection Frequency, Collection Frequency Inverse Document Frequency and Term Frequency Document Frequency all showed performances close to the best group. The Bi-Normal Separation metric produced excellent results for the smallest feature subsets. The weirdness factor performed several times better than random selection, but was not among the top performing group. Some combination experiments achieved better results than each method alone, but the majority did not. The top performers Chi square and GSS coefficient classified more documents when used together than alone.Four of the five combinations that showed increase in performance included the BNS metric

Access Control in Multi-Thousand-Machine Datacenters

Large data centers are used for large-scale high-performance tasks that often includes processing and handling sensitive information. It is therefore important to have access control systems that are able to function in large-scale data centers. This thesis looks into existing solutions for the authentication step of access control in large data centers, and analyses how two authentication systems, Kerberos and PKI, will perform when employed on a larger scale, beyond what is normal in a large data center today. The emphasis in the analysis is on possible bottlenecks in the system, computational power spent on access control routines, procedures for administration and key distribution and availability of extension features needed in large scale data center scenarios. Our administration analysis will propose and present possible methods for initial key distribution to new machines in the data center, as well as methods for enrolling new users. We will also propose a method for automatic service instantiation in Kerberos and present a method for service instantiation in PKI. We will look at how the systems handle failed machines in the network, and look at how the systems handle breaches of trusted components. Our performance analysis will show that under given assumptions, both Kerberos and PKI will handle the average load in a hypothetical data center consisting of 100000 machines and 1000 users. We will also see that under an assumed peak load, Kerberos will be able to handle 10000 service requests in under 1 second, whereas the PKI solution would need at least 15 seconds to handle the same number of requests using recommended public key sizes. This means that some programs may need special configurations to work in a PKI system under high load

Structures based on semi-degradable biomaterials for neural regeneration in the central nervous system

Se pretende obtener un material semibiodegradable basado en ácido hialurónico químicamente enlazado a cadenas de polímeros acrílicos. Los hidrogeles de ácido hialurónico presentan en general buenas características para su utilización en regeneración del sistema nervioso central: es biodegradable, es un componente importante del tejido neural, sus propiedades mecánicas son semejantes a las del tejido cerebral, promueve la formación de nuevos capilares (angiogénesis), y limita la inflamación. Con este nuevo material se pretende mejorar el excesivo grado de hinchado en medio fisiológico, su rápida degradación, mejorar la adhesión celular, además la matriz permanente de las cadenas acrílicas pueden actuar como un soporte permanente durante el proceso regenerativo sin que se produzca una pérdida brusca de propiedades mecánicas y estructurales. El trabajo consiste en caracterizar este nuevo material así como los productos intermedios necesarios para su obtención final, comparándolo con las propiedades de un hidrogel de ácido hialurónico sin incorporar cadenas acrílicas. Los estudios celulares se llevaran a cabo in vitro, como fase preliminar para futuros implantes en el cortex cerebral, estudiando la capacidad de diferenciación de precursores neurales y de generación de nuevos capilares con el fenotipo típico de la barrera hematoencefálica, mediante el estudio de cocultivos de precursores neurales y células endoteliales.Perez Garnes, M. (2015). Structures based on semi-degradable biomaterials for neural regeneration in the central nervous system [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/4879