Dual interfacial modifications of hierarchically structured iodine-doped ZnO photoanodes for high-efficiency dye-sensitized solar cells

A nanocomposite porous electrode structure consisting of hierarchical iodine-doped zinc oxide (I-ZnO) aggregates combined with the two simple solution-processed interfacial modifications i.e. a ZnO compact layer (CL) and a TiO2 protective layer (PL) has been developed in order to understand electron transport and recombination in the photoanode matrix, together with boosting the conversion efficiency of I-ZnO based dye-sensitized solar cells (DSCs). Electrochemical impedance spectra demonstrate that ZnO CL pre-treatment and TiO2 PL post-treatment synergistically reduce charge-transfer resistance and suppress electron recombination. Furthermore, the electron lifetime in two combined modifications of IZnO + CL + PL photoelectrode is the longest in comparison with the other three photoelectrodes. As a consequence, the overall conversion efficiency of I-ZnO + CL + PL DSC is significantly enhanced to 6.79%, with a 36% enhancement compared with unmodified I-ZnO DSC. Moreover, the stability of I-ZnO + CL + PL cell is improved as compared to I-ZnO one. The mechanism of electron transfer and recombination upon the introduction of ZnO CL and TiO2 PL is also proposed in this work.

Common Themes in Multi-block Structured Quad/Hex Mesh Generation

The automatic generation of structured multi-block quadrilateral (quad) and hexahedral (hex) meshes has been researched for many years without definitive success. The core problem in quad / hex mesh generation is the placement of mesh singularities to give the desired mesh orientation and distribution [1]. It is argued herein that existing approaches (medial axis, paving / plastering, cross / frame fields) are actually alternative views of the same concept. Using the information provided by the different approaches provides additional insight into the problem.

Gene-associated markers can assign origin in a weakly structured fish, Atlantic herring

Regulations on the exploitation of populations of commercially important fish species and the ensuing consumer interest in sustainable products have increased the need to accurately identify the population of origin of fish and fish products. Although genomics-based tools have proven highly useful, there are relatively few examples in marine fish displaying accurate origin assignment. We synthesize data for 156 single-nucleotide polymorphisms typed in 1039 herring, Clupea harengus L., spanning the Northeast Atlantic to develop a tool that allows assignment of individual herring to their regional origin. We show the method's suitability to address specific biological questions, as well as management applications. We analyse temporally replicated collections from two areas, the Skagerrak (n = 81, 84, 66) and the western Baltic (n = 52, 52). Both areas harbour heavily fished mixed-origin stocks, complicating management issues. We report novel genetic evidence that herring from the Baltic Sea contribute to catches in the North Sea, and find support that western Baltic feeding aggregations mainly constitute herring from the western Baltic with contributions from the Eastern Baltic. Our study describes a general approach and outlines a database allowing individual assignment and traceability of herring across a large part of its East Atlantic distribution.

Exploiting evidence from unstructured data to enhance master data management

Master data management (MDM) integrates data from multiple
structured data sources and builds a consolidated 360-
degree view of business entities such as customers and products.
Today’s MDM systems are not prepared to integrate
information from unstructured data sources, such as news
reports, emails, call-center transcripts, and chat logs. However,
those unstructured data sources may contain valuable
information about the same entities known to MDM from
the structured data sources. Integrating information from
unstructured data into MDM is challenging as textual references
to existing MDM entities are often incomplete and
imprecise and the additional entity information extracted
from text should not impact the trustworthiness of MDM
data.
In this paper, we present an architecture for making MDM
text-aware and showcase its implementation as IBM InfoSphere
MDM Extension for Unstructured Text Correlation,
an add-on to IBM InfoSphere Master Data Management
Standard Edition. We highlight how MDM benefits from
additional evidence found in documents when doing entity
resolution and relationship discovery. We experimentally
demonstrate the feasibility of integrating information from
unstructured data sources into MDM.

Neurology assessment by objective structured video examination (OSVE)

Summary
Background
The ability to carry out a neurological examination and make an appropriate differential diagnosis is one of the mainstays of our final Bachelor of Medicine (MB) exam; however, with the introduction of objective structured clinical examinations (OSCEs) it has become impossible to arrange for adequate numbers of suitable real patients to participate in the exam.

Context
It is vital that newly qualified doctors can perform a basic neurological examination, interpret the physical signs and formulate a differential diagnosis.

It is vital that newly qualified doctors can perform a basic neurological examination

Innovation
Since 2010 we have introduced an objective structured video examination (OSVE) of a neurological examination of a real patient as part of our final MB OSCE exam. The students view clips of parts of the examination process. They answer questions on the signs that are demonstrated and formulate a differential diagnosis.

Implications
This type of station is logistically a lot easier to organise than a large number of real patients at different examination sites. The featured patients have clearly demonstrated signs and, as every student sees the same patient, are perfectly standardised. It is highly acceptable to examiners and performed well as an assessment tool. There are, however, certain drawbacks in that we are not examining the student's examination technique or their interaction with the patient. Also, certain signs, in particular the assessment of muscle tone and power, are more difficult for a student to estimate in this situation

Two-part Segmentation of Text Documents

We consider the problem of segmenting text documents that have a
two-part structure such as a problem part and a solution part. Documents
of this genre include incident reports that typically involve
description of events relating to a problem followed by those pertaining
to the solution that was tried. Segmenting such documents
into the component two parts would render them usable in knowledge
reuse frameworks such as Case-Based Reasoning. This segmentation
problem presents a hard case for traditional text segmentation
due to the lexical inter-relatedness of the segments. We develop
a two-part segmentation technique that can harness a corpus
of similar documents to model the behavior of the two segments
and their inter-relatedness using language models and translation
models respectively. In particular, we use separate language models
for the problem and solution segment types, whereas the interrelatedness
between segment types is modeled using an IBM Model
1 translation model. We model documents as being generated starting
from the problem part that comprises of words sampled from
the problem language model, followed by the solution part whose
words are sampled either from the solution language model or from
a translation model conditioned on the words already chosen in the
problem part. We show, through an extensive set of experiments on
real-world data, that our approach outperforms the state-of-the-art
text segmentation algorithms in the accuracy of segmentation, and
that such improved accuracy translates well to improved usability
in Case-based Reasoning systems. We also analyze the robustness
of our technique to varying amounts and types of noise and empirically
illustrate that our technique is quite noise tolerant, and
degrades gracefully with increasing amounts of noise

A Structured Approach to Simulate Manufacturing System In Virtual Environment

Nowadays, the realization of the Virtual Factory (VF) is the strategic goal of many manufacturing enterprises for the coming years. The industrial scenario is characterized by the dynamics of innovations increment and the product life cycle became shorter. Furthermore products and the corresponding manufacturing processes get more and more complex. Therefore, companies need new methods for the planning of manufacturing systems.
To date, the efforts have focused on the creation of an integrated environment to design and manage the manufacturing process of a new product. The future goal is to integrate Virtual Reality (VR) tools into the Product Lifecycle Management of the manufacturing industries.
In order to realize this goal the authors have conducted a study to perform VF simulation steps for a supplier of Industrial Automation Systems and have provided a structured approach focusing on interaction between simulation software and VR hardware tools in order to simulate both robotic and
manual work cells.
The first results of the study in progress have been carried out in the VR Laboratory of the Competence Regional Centre for the qualification of the Transportation Systems that has been founded by Campania Region.