Improved results on stability analysis of time-varying delay systems via delay partitioning method and Finsler’s lemma

This paper proposes novel conditions based on linear matrix inequalities (LMI) for stability analysis of arbitrarily-fast time-varying delays systems. The time-varying delay interval is divided into smaller pieces in order to obtain an equivalent switched model with multiple time-varying delays of smaller interval, which differently from other existing approaches, the maximum switching frequency is not required for stability analysis. Thus, by the use of augmented Lyapunov-Krasovskii functionals and the Finsler’s lemma, together with some relationships among state variables intentionally defined, the inherent conservatism can be progressively reduced by refining more and more the delay partition. The superiority of the proposed method is illustrated through two benchmark examples.     

Early diagnosis of Alzheimer’s via PET probe

Alzheimer's disease (AD), a neurodegenerative disorder that impairs memory, cognition and behavior, has become the most frequent senile dementia, currently crippling more than 20 million people across the world. Scientists have found that the accumulation of amyloid plaques in the brain features the pathological change of the disease. Unfortunately, they haven't found an effective method to make in-vivo detection of the plagues in brain tissue.     

Amplifying scientific paper’s abstract by leveraging data-weighted reconstruction

In this paper, we focus on the problem of automatically generating amplified scientific paper’s abstract which represents the most influential aspects of scientific paper. The influential aspects can be illustrated by the target scientific paper’s abstract and citation sentences discussing the target paper, which are provided in papers citing the target paper. In this paper, we extract representative sentences through data-weighted reconstruction approach(DWR) by jointly leveraging target scientific paper’s abstract and citation sentences’ content and structure. In our study, we make two-folded contributions.Firstly, sentence’s weight was learned by exploiting regularization for ranking on heterogeneous bibliographic network. Specially, Sentences-similar-Sentences relationship was identified by language modeling-based approach and added to the bibliographic network. Secondly, a data-weighted reconstruction objective function is optimized to select the most representative sentences which reconstructs the original sentence set with minimum error. In this process, sentences’ weight plays a critical role. Experimental evaluation over real dataset confirms the effectiveness of our approach.     

Modification of Jaffe’s kinetic method decreases bilirubin interference: A preliminary report

The negative interference of bilirubin on serum creatinine determined by the kinetic alkaline picrate (Jaffe) reaction is the unresolved problem. Though high performance liquid chromatography and gas chromatography with mass spectroscopy have been proposed to be gold standards for creatinine estimation but they are not readily available in most of the clinical chemistry laboratories due to economic and technical constraints. Most of the present day analyzers use Jaffe’s kinetic method without deproteinization. Though enzymatic methods are now routinely used as most accurate method but they are not acceptable due to cost constraints. Hence this study was planned to find out a possible solution to the problem of bilirubin interference by a minor modification in the commonly used Jaffe method so that it is amenable for use on the currently used analyzers.     

Early diagnosis of Alzheimer＇s via PET probe

Alzheimer＇s disease （AD）, a neurodegenerative disorder that impairs memory, cognition and behavior, has become the most frequent senile dementia, currently crippling more than 20 million people across the world. Scientists have found that the accumulation of amyloid plaques in the brain features the pathological change of the disease.     

Development of a holistic model for the management of an enterprise’s information assets

Organisations increasingly realise that they must transform into true digital enterprises to create competitive advantage and ensure corporate survival. However, many organisations do not realise that successful digital transformation (DT) requires much more than technology; it can only succeed if they manage their data, information and knowledge as true business assets. This paper describes collaborative research conducted by academic and industry partners, a mutually beneficial journey spanning the past ten years. The aim was to develop a Holistic Information Asset Management (HIAM) model indicating the important areas of information asset management (IAM) that support the DT journey. Interviews were conducted with C-level executives in organisations from all industries on three continents to investigate their IAM practices, the barriers to good IAM and the benefits of managing information assets (IAs) well. This paper proposes that organisations should focus on ten domains in their quest for effective IAM: i) business benefits, ii) business environment, iii) executive awareness, iv) leadership and management, v) information environment, vi) information systems, vii) information behaviour, viii) information attributes/quality, ix) information performance and x) justification.