A Novel Top‐Down Synthesis of Ultrathin 2D Boron Nanosheets for Multimodal Imaging‐Guided Cancer Therapy

Single atom nonmetal 2D nanomaterials have shown considerable potential in cancer nanomedicines, owing to their intriguing properties and biocompatibility. Herein, ultrathin boron nanosheets (B NSs) are prepared through a novel top‐down approach by coupling thermal oxidation etching and liquid exfoliation technologies, with controlled nanoscale thickness. Based on the PEGylated B NSs, a new photonic drug delivery platform is developed, which exhibits multiple promising features for cancer therapy and imaging, including: i) efficient NIR‐light‐to‐heat conversion with a high photothermal conversion efficiency of 42.5%, ii) high drug‐loading capacity and triggered drug release by NIR light and moderate acidic pH, iii) strong accumulation at tumor sites, iv) multimodal imaging properties (photoacoustic, photothermal, and fluorescence imaging), and v) complete tumor ablation and excellent biocompatibility. As far as it is known, this is the first report on the top‐down fabrication of ultrathin 2D B NSs by the combined thermal oxidation etching and liquid exfoliation, as well as their application as a multimodal imaging‐guided drug delivery platform. The newly prepared B NSs are also expected to provide a robust and useful 2D nanoplatform for various biomedical applications.     

Delayed renal dysfunction and flash pulmonary edema post endovascular abdominal aneurysm repair

After endovascular aortic repair (EVAR), the deterioration in long‐term renal function is probably multifactorial. Preoperative renal failure is an independent risk factor. Postoperative renal dysfunction can be associated with inadvertent renal artery occlusion, renal artery complications as stenosis, plaque dislodgement, or dissection. Ischemic nephropathy can accelerate hypertension and circulatory congestion. We report a case of coverage of the renal arteries symptomatic with flash pulmonary edema and renal failure 15 months after EVAR, suggesting a delayed endograft migration. The patient had complete resolution of symptoms and renal function after renal artery stenting with placement between endograft and aneurysm to the left renal artery.     

Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. Genetic algorithms are applied to this purpose and three different popular algorithms capable to deal with multi-objective optimization are compared. The three algorithms, namely the Niched Pareto Genetic Algorithm, the Non-dominated Sorting Genetic Algorithm 2 and the Strength Pareto Genetic Algorithm 2, are described in details and the achieved results are widely discussed; moreover several statistical tests have been applied in order to evaluate the statistical significance of the obtained results.     

Learners Reliability Estimated Through Neural Networks Applied to Build a Novel Hybrid Ensemble Method

Dimensionality reduction (DR) has been one central research topic in information theory, pattern recognition, and machine learning. Apparently, the performance of many learning models significantly rely on dimensionality reduction: successful DR can largely improve various approaches in clustering and classification, while inappropriate DR may deteriorate the systems. When applied on high-dimensional data, some existing research approaches often try to reduce the dimensionality first, and then input the reduced features to other available models, e.g., Gaussian mixture model (GMM). Such independent learning could however significantly limit the performance, since the optimal subspace given by a particular DR approach may not be appropriate for the following model. In this paper, we focus on investigating how unsupervised dimensionality reduction could be performed together with GMM and if such joint learning could lead to improvement in comparison with the traditional unsupervised method. In particular, we engage the mixture of factor analyzers with the assumption that a common factor loading exists for all the components. Based on that, we then present EM-algorithm that converges to a local optimal solution. Such setting exactly optimizes a dimensionality reduction together with the parameters of GMM. We describe the framework, detail the algorithm, and conduct a series of experiments to validate the effectiveness of our proposed approach. Specifically, we compare the proposed joint learning approach with two competitive algorithms on one synthetic and six real data sets. Experimental results show that the joint learning significantly outperforms the comparison methods in terms of three criteria.     

Reengineering legacy applications into software product lines: a systematic mapping

Software Product Lines (SPLs) are families of systems that share common assets allowing a disciplined reuse. Rarely SPLs start from scratch, instead they usually start from a set of existing systems that undergo a reengineering process. Many approaches to conduct the reengineering process have been proposed and documented in research literature. This scenario is a clear testament to the interest in this research area. We conducted a systematic mapping study to provide an overview of the current research on reengineering of existing systems to SPLs, identify the community activity in regarding of venues and frequency of publications in this field, and point out trends and open issues that could serve as references for future research. This study identified 119 relevant publications. These primary sources were classified in six different dimensions related to reengineering phases, strategies applied, types of systems used in the evaluation, input artefacts, output artefacts, and tool support. The analysis of the results points out the existence of a consolidate community on this topic and a wide range of strategies to deal with different phases and tasks of the reengineering process, besides the availability of some tools. We identify some open issues and areas for future research such as the implementation of automation and tool support, the use of different sources of information, need for improvements in the feature management, the definition of ways to combine different strategies and methods, lack of sophisticated refactoring, need for new metrics and measures and more robust empirical evaluation. Reengineering of existing systems into SPLs is an active research topic with real benefits in practice. This mapping study motivates new research in this field as well as the adoption of systematic reuse in software companies.     

On web user tracking of browsing patterns for personalised advertising

On today’s Web, users trade access to their private data for content and services. App and service providers want to know everything they can about their users, in order to improve their product experience. Also, advertising sustains the business model of many websites and applications. Efficient and successful advertising relies on predicting users’ actions and tastes to suggest a range of products to buy. Both service providers and advertisers try to track users’ behaviour across their product network. For application providers this means tracking users’ actions within their platform. For third-party services following users, means being able to track them across different websites and applications. It is well known how, while surfing the Web, users leave traces regarding their identity in the form of activity patterns and unstructured data. These data constitute what is called the user’s online footprint. We analyse how advertising networks build and collect users footprints and how the suggested advertising reacts to changes in the user behaviour.     

A Survey of Cardiac 4D PC‐MRI Data Processing

Cardiac four‐dimensional phase‐contrast magnetic resonance imaging (4D PC‐MRI) acquisitions have gained increasing clinical interest in recent years. They allow to non‐invasively obtain extensive information about patient‐specific hemodynamics, and thus have a great potential to improve the diagnosis, prognosis and therapy planning of cardiovascular diseases. A dataset contains time‐resolved, three‐dimensional blood flow directions and strengths, making comprehensive qualitative and quantitative data analysis possible. Quantitative measures, such as stroke volumes, help to assess the cardiac function and to monitor disease progression. Qualitative analysis allows to investigate abnormal flow characteristics, such as vortices, which are correlated to different pathologies. Processing the data comprises complex image processing methods, as well as flow analysis and visualization. In this work, we mainly focus on the aorta. We provide an overview of data measurement and pre‐processing, as well as current visualization and quantification methods. This allows other researchers to quickly catch up with the topic and take on new challenges to further investigate the potential of 4D PC‐MRI data.     

Dynamic role authorization in multiparty conversations

Protocols in distributed settings usually rely on the interaction of several parties and often identify the roles involved in communications. Roles may have a behavioral interpretation, as they do not necessarily correspond to sites or physical devices. Notions of role authorization thus become necessary to consider settings in which, e.g., different sites may be authorized to act on behalf of a single role, or in which one site may be authorized to act on behalf of different roles. This flexibility must be equipped with ways of controlling the roles that the different parties are authorized to represent, including the challenging case in which role authorizations are determined only at runtime. We present a typed framework for the analysis of multiparty interaction with dynamic role authorization and delegation. Building on previous work on conversation types with role assignment, our formal model is based on an extension of the \({\pi}\)-calculus in which the basic resources are pairs channel-role, which denote the access right of interacting along a given channel representing the given role. To specify dynamic authorization control, our process model includes (1) a novel scoping construct for authorization domains, and (2) communication primitives for authorizations, which allow to pass around authorizations to act on a given channel. An authorization error then corresponds to an action involving a channel and a role not enclosed by an appropriate authorization scope. We introduce a typing discipline that ensures that processes never reduce to authorization errors, including when parties dynamically acquire authorizations.