İçerik Yönetim Sisteminde Kullanılabilirlik Yapılarının İncelenmesi

The internet provides a media-rich navigational environment where people interact with computer systems. This interactive relationship between humans and computers can be explored from a socio-technical philosophy. Thus, investigating individual behaviors toward new information technologies based on their experiences with the internet technology in general, and content management in particular emerged as an alternative stream of research. Since users' behaviors are heavily influenced by web sites usability, this study is aimed at exploring multidimensionalty in usability constructs of a content management system. The findings indicate that multidimensional model - at least- with two upper constructs exist in usability. This finding supports the socio-technical perspective in usability in that content presentation and architectural design were perceived as separate constructs by participants.İnternet, bireylerin bilgisayar sistemleri ile etkileşim sağladığı medya açısından zengin bir ortam sunmaktadır. Bilgisayarlar ve bireylar arasındaki bu etkileşimli ortam, sosyoteknik bir açıdan incelenebilir. Bu nedenle, bireylerin yeni teknolojiye yönelik davranışlarını, internet teknolojileri ile olan deneyimlerine ve içerik yönetimine dayalı olarak inceleme, araştırmalarda alternatif bir yaklaşım olarak ortaya çıkmıştır. Bireylerin davranışları, web sitelerinin kullanışlılığından çok fazla etkilenmektedir. Bu görüşten hareketle bu çalışmanın amacı, bir içerik yönetim sisteminin kullanışlılık yapılarını çok boyutluluk açısından araştırmaktır. Bulgular, kullanışlılık açısından en az iki olmak üzere model olarak çok boyutlu bir yapının varlığını göstermektedir. Bu bulgu, kullanışlılık açısından sosyoteknik bakış açısı ile içerik sunumunun ve mimari tasarımın farklı yapılar olarak ele alınması gerektiği görüşünü desteklemektedir

The Impact of Information Sharing on Different Performance Indicators in a Multi-Level Supply Chain

Enterprises can use different methods/principles to obtain competitive advantages. Information sharing (IS) among supply chain (SC) partners is also one of these methods used in enterprises and it has positive effects on overall system performance like reduced inventory level, decreased cost, bullwhip effects and increased profit. In this paper, our aim is to present the impacts of IS on different costs like ordering, holding and penalty costs of each SC member and total system costs in multi SC. We want to show the effects of sharing different types of information simultaneously or separately on SC partners as cost change. Besides, this paper presents the situation of order quantity estimation according to the proximity of actual order quantity in decentralized or centralized demand sharing. A model is developed to determine IS influence on the cost of SC partners. Various IS scenarios are studied in this paper. The customer demand, warehouse order quantity and warehouse-manufacturer lead time are the shared information of scenarios. Results are tested and analysed by using analysis of variance (ANOVA).The findings of this study show that IS especially simultaneously sharing reduces system costs. Lead time sharing provides the lowest cost between other types of sharing. For every system member, holding cost reduces the most during IS. The more accurate demand forecasting is performed in centralized demand sharing compared to decentralized sharing

Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

<p>Abstract</p> <p>Background</p> <p>The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published <it>q</it>-Norm MKL algorithm.</p> <p>Results</p> <p>We demonstrate the performance of our method on two problems from Computational Biology. First, we show that our method is able to improve performance on a splice site dataset with given hierarchical task structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no knowledge about the degree of task relatedness. Here, we are able to learn the task similarities<it> ab initio</it> along with the Multitask classifiers. In both cases, we outperform baseline methods that we compare against.</p> <p>Conclusions</p> <p>We present a novel approach to Multitask Learning that is capable of learning task similarity along with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks relationships if available, but is also able to identify task similarities in absence of such prior information. Both variants show promising results in applications from Computational Biology.</p

Diagnosis of comorbid migraine without aura in patients with idiopathic/genetic epilepsy based on the gray zone approach to the International Classification of Headache Disorders 3 criteria

BackgroundMigraine without aura (MwoA) is a very frequent and remarkable comorbidity in patients with idiopathic/genetic epilepsy (I/GE). Frequently in clinical practice, diagnosis of MwoA may be challenging despite the guidance of current diagnostic criteria of the International Classification of Headache Disorders 3 (ICHD-3). In this study, we aimed to disclose the diagnostic gaps in the diagnosis of comorbid MwoA, using a zone concept, in patients with I/GEs with headaches who were diagnosed by an experienced headache expert.MethodsIn this multicenter study including 809 consecutive patients with a diagnosis of I/GE with or without headache, 163 patients who were diagnosed by an experienced headache expert as having a comorbid MwoA were reevaluated. Eligible patients were divided into three subgroups, namely, full diagnosis, zone I, and zone II according to their status of fulfilling the ICHD-3 criteria. A Classification and Regression Tree (CART) analysis was performed to bring out the meaningful predictors when evaluating patients with I/GEs for MwoA comorbidity, using the variables that were significant in the univariate analysis.ResultsLonger headache duration (&lt;4 h) followed by throbbing pain, higher visual analog scale (VAS) scores, increase of pain by physical activity, nausea/vomiting, and photophobia and/or phonophobia are the main distinguishing clinical characteristics of comorbid MwoA in patients with I/GE, for being classified in the full diagnosis group. Despite being not a part of the main ICHD-3 criteria, the presence of associated symptoms mainly osmophobia and also vertigo/dizziness had the distinguishing capability of being classified into zone subgroups. The most common epilepsy syndromes fulfilling full diagnosis criteria (n = 62) in the CART analysis were 48.39% Juvenile myoclonic epilepsy followed by 25.81% epilepsy with generalized tonic-clonic seizures alone.ConclusionLonger headache duration, throbbing pain, increase of pain by physical activity, photophobia and/or phonophobia, presence of vertigo/dizziness, osmophobia, and higher VAS scores are the main supportive associated factors when applying the ICHD-3 criteria for the comorbid MwoA diagnosis in patients with I/GEs. Evaluating these characteristics could be helpful to close the diagnostic gaps in everyday clinical practice and fasten the diagnostic process of comorbid MwoA in patients with I/GEs