A fast method for fuzzy neural modeling and refinement

In the identification of complex dynamic systems using fuzzy neural networks, one of the main issues is the curse of dimensionality, which makes it difficult to retain a large number of system inputs or to consider a large number of fuzzy sets. Moreover, due to the correlations, not all possible network inputs or regression vectors in the network are necessary and adding them simply increases the model complexity and deteriorates the network generalisation performance. In this paper, the problem is solved by first proposing a fast algorithm for selection of network terms, and then introducing a refinement procedure to tackle the correlation issue. Simulation results show the efficacy of the method.

Energy levels, wavefunction compositions and electric dipole transitions in neutral Ca

A configuration-interaction approach, based on the use of B-spline basis sets combined with a model potential including monoelectronic and dielectronic core polarization effects, is employed to calculate term energies and wavefunctions for neutral Ca. Results are reported for singlet and triplet bound states, and some quasi-bound states above the lowest ionization limit, with angular momentum up to L = 4. Comparison with experiment and with other theoretical results shows that this method yields the most accurate energy values for neutral Ca obtained to date. Wavefunction compositions, necessary for labelling the levels, and the effects of semi-empirical polarization potentials on the wavefunctions are discussed, as are some recent identifications of doubly-excited states. It is shown that taking into account dielectronic core polarization changes the energies of the lowest terms in Ca significantly, in general by a few hundred cm(-1), the effect decreasing rapidly for the higher bound states. For Rydberg states with n approximate to 7 the accuracy of the results is often better than a few cm(-1). For series members (or perturbers) with a pronounced 3d character the error can reach 150 cm(-1). The wavefunctions are used to calculate oscillator strengths and lifetimes for a number of terms and these are compared with existing measurements. The agreement is good but points to a need for improved measurements.

A systematic review of systematic reviews to improve maternal mental health and well-being

Objective: to identify non-invasive interventions in the perinatal period that could enable midwives to offer effective support to women within the area of maternal mental health and well-being.

Methods: a total of 9 databases were searched: MEDLINE, PubMed, EBSCO (CINAHL/British Nursing Index), MIDIRS Online Database, Web of Science, The Cochrane library, CRD (NHS EED/DARE/HTA), Joanne Briggs Institute and EconLit. A systematic search strategy was formulated using key MeSH terms and related text words for midwifery, study aim, study design and mental health. Inclusion criteria were articles published from 1999 onwards, English language publications and articles originating from economically developed countries, indicated by membership of the Organisation for Economic Co-operation and Development (OECD). Data were independently extracted using a data collection form, which recorded data on the number of papers reviewed, time frame of the review, objectives, key findings and recommendations. Summary data tables were set up outlining key data for each study and findings were organised into related groups. The methodological quality of the reviews was assessed based on predefined quality assessment criteria for reviews.

Findings: 32 reviews were identified as examining interventions that could be used or co-ordinated by midwives in relation to some aspect of maternal mental health and well-being from the antenatal to the postnatal period and met the inclusion criteria. The review highlighted that based on current systematic review evidence it would be premature to consider introducing any of the identified interventions into midwifery training or practice. However there were a number of examples of possible interventions worthy of further research including midwifery led models of care in the prevention of postpartum depression, psychological and psychosocial interventions for treating postpartum depression and facilitation/co-ordination of parent-training programmes. No reviews were identified that supported a specific midwifery role in maternal mental health and well-being in pregnancy, and yet, this is the point of most intensive contact.

Key conclusions and implications for practice: This systematic review of systematic reviews provides a valuable overview of the current strengths and gaps in relation to maternal mental health interventions in the perinatal period. While there was little evidence identified to inform the current role of midwives in maternal mental health, the review provides the opportunity to reflect on what is achievable by midwives now and in the future and the need for high quality randomised controlled trials to inform a strategic approach to promoting maternal mental health in midwifery.

PROSTATIC INTRAEPITHELIAL NEOPLASIA (PIN) - PERFORMANCE OF BAYESIAN BELIEF NETWORK FOR DIAGNOSIS AND GRADING

Prostatic intraepithelial neoplasia (PIN) diagnosis and grading are affected by uncertainties which arise from the fact that almost all knowledge of PIN histopathology is expressed in concepts, descriptive linguistic terms, and words. A Bayesian belief network (BBN) was therefore used to reduce the problem of uncertainty in diagnostic clue assessment, while still considering the dependences between elements in the reasoning sequence. A shallow network was used with an open-tree topology, with eight first-level descendant nodes for the diagnostic clues (evidence nodes), each independently linked by a conditional probability matrix to a root node containing the diagnostic alternatives (decision node). One of the evidence nodes was based on the tissue architecture and the others were based on cell features. The system was designed to be interactive, in that the histopathologist entered evidence into the network in the form of likelihood ratios for outcomes at each evidence node. The efficiency of the network was tested on a series of 110 prostate specimens, subdivided as follows: 22 cases of non-neoplastic prostate or benign prostatic tissue (NP), 22 PINs of low grade (PINlow), 22 PINs of high grade (PINhigh), 22 prostatic adenocarcinomas with cribriform pattern (PACcri), and 22 prostatic adenocarcinomas with large acinar pattern (PAClgac). The results obtained in the benign and malignant categories showed that the belief for the diagnostic alternatives is very high, the values being in general more than 0.8 and often close to 1.0. When considering the PIN lesions, the network classified and graded most of the cases with high certainty. However, there were some cases which showed values less than 0.8 (13 cases out of 44), thus indicating that there are situations in which the feature changes are intermediate between contiguous categories or grades. Discrepancy between morphological grading and the BBN results was observed in four out of 44 PIN cases: one PINlow was classified as PINhigh and three PINhigh were classified as PINlow. In conclusion, the network can grade PlN lesions and differentiate them from other prostate lesions with certainty. In particular, it offers a descriptive classifier which is readily implemented and which allows the use of linguistic, fuzzy variables.

On the nudging terms at open boundaries in regional ocean models

For open boundary conditions (OBCs) in regional models, a nudging term added to radiative and/or advective conditions during the wave or flow propagation outward from the model domain of interest is widely used, to prevent the predicted boundary values from evolving to become quite different from the external data, especially for a long-term integration. However, nudging time scales are basically unknown, leading to many empirical selections. In this paper, a method for objectively estimating nudging time scales during outward propagation is proposed, by using internal model dynamics near the boundary. We tested this method and other several commonly used OBCs for cases of both an idealized model domain and a realistic configuration, and model results demonstrated that the proposed method improves the model solutions. Many similarities are found between the nudging and mixing time scales, in magnitude, spatial and temporal variations, since the nudging mainly replaces the effect of the mixing terms in this study. However, the mixing time scale is not an intrinsic property of the nudging term because in other studies the nudging term might replace terms other than the mixing terms and, thus, should reflect other characteristic time scales.

Oxygen vacancy formation in CeO2 and Ce1-xZrxO2 solid solutions:electron localization, electrostatic potential and structural relaxation

Ceria (CeO2) and ceria-based composite materials, especially Ce1-xZrxO2 solid solutions, possess a wide range of applications in many important catalytic processes, such as three-way catalysts, owing to their excellent oxygen storage capacity (OSC) through the oxygen vacancy formation and refilling. Much of this activity has focused on the understanding of the electronic and structural properties of defective CeO2 with and without doping, and comprehending the determining factor for oxygen vacancy formation and the rule to tune the formation energy by doping has constituted a central issue in material chemistry related to ceria. However, the calculation on electronic structures and the corresponding relaxation patterns in defective CeO2-x oxides remains at present a challenge in the DFT framework. A pragmatic approach based on density functional theory with the inclusion of on-site Coulomb correction, i.e. the so-called DFT + U technique, has been extensively applied in the majority of recent theoretical investigations. Firstly, we review briefly the latest electronic structure calculations of defective CeO2(111), focusing on the phenomenon of multiple configurations of the localized 4f electrons, as well as the discussions of its formation mechanism and the catalytic role in activating the O-2 molecule. Secondly, aiming at shedding light on the doping effect on tuning the oxygen vacancy formation in ceria-based solid solutions, we summarize the recent theoretical results of Ce1-xZrxO2 solid solutions in terms of the effect of dopant concentrations and crystal phases. A general model on O vacancy formation is also discussed; it consists of electrostatic and structural relaxation terms, and the vital role of the later is emphasized. Particularly, we discuss the crucial role of the localized structural relaxation patterns in determining the superb oxygen storage capacity in kappa-phase Ce1-xZr1-xO2. Thirdly, we briefly discuss some interesting findings for the oxygen vacancy formation in pure ceria nanoparticles (NPs) uncovered by DFT calculations and compare those with the bulk or extended surfaces of ceria as well as different particle sizes, emphasizing the role of the electrostatic field in determining the O vacancy formation.

A Model to Understand the Oxygen Vacancy Formation in Zr-Doped CeO2:Electrostatic Interaction and Structural Relaxation

Using density functional theory with the inclusion of on-site Coulomb Correction, the O vacancy formation energies of CexZr1-xO2 solid solutions with a series of Ce/Zr ratios are calculated, and a model to understand the results is proposed. It consists of electrostatic and structural relaxation terms, and the latter is found to play a vital role in affecting the O vacancy formation energies. Using this model, several long-standing questions in the field, such as why ceria with 50% ZrO2 usually exhibit the best oxygen storage capacity, can be explained. Some implications of the new interpretation are also discussed.

Will your article be found? Authors choose a confusing variety of words to describe dental survival analyses

OBJECTIVES: Identify the words and phrases that authors used to describe time-to-event outcomes of dental treatments in patients.

MATERIALS AND METHODS: A systematic handsearch of 50 dental journals with the highest Citation Index for 2008 identified articles reporting dental treatment with time-to-event statistics (included "case" articles, n = 95), without time-to-event statistics (active "control" articles, n = 91), and all other articles (passive "control" articles n = 6796). The included and active controls were read, identifying 43 English words across the title, aim and abstract, indicating that outcomes were studied over time. Once identified, these words were sought within the 6796 passive controls. Words were divided into six groups. Differences in use of words were analyzed with Pearson's chi-square across these six groups, and the three locations (title, aim, and abstract).

RESULTS: In the abstracts, included articles used group 1 (statistical technique) and group 2 (statistical terms) more frequently than the active and passive controls (group 1: 35%, 2%, 0.37%, P < 0.001 and group 2: 31%, 1%, 0.06%, P < 0.001). The included and active controls used group 3 (quasi-statistical) equally, but significantly more often than the passive controls (82%, 78%, 3.21%, P < 0.001). In the aims, use of target words was similar for included and active controls, but less frequent for groups 1-4 in the passive controls (P < 0.001). In the title, group 2 (statistical techniques) and groups 3-5 (outcomes) were similar for included and active controls, but groups 2 and 3 were less frequent in the passive controls (P < 0.001). Significantly more included articles used group 6 words (stating the study duration) (54%, 30%, P = 0.001).

CONCLUSION: All included articles used time-to-event analyses, but two-thirds did not include words to highlight this in the abstract. There is great variation in the words authors used to describe dental time-to-event outcomes. Electronic identification of such articles would be inconsistent, with low sensitivity and specificity. Authors should improve the reporting quality. Journals should allow sufficient space in abstracts to summarize research, and not impose unrealistic word limits. Readers should be mindful of these problems when searching for relevant articles. Additional research is required in this field.

Search filters: mapping the literature using a novel form of content analysis.

Background: Search filters are combinations of words and phrases designed to retrieve an optimal set of records on a particular topic (subject filters) or study design (methodological filters). Information specialists are increasingly turning to reusable filters to focus their searches. However, the extent of the academic literature on search filters is unknown. We provide a broad overview to the academic literature on search filters.
Objectives: To map the academic literature on search filters from 2004 to 2015 using a novel form of content analysis.
Methods: We conducted a comprehensive search for literature between 2004 and 2015 across eight databases using a subjectively derived search strategy. We identified key words from titles, grouped them into categories, and examined their frequency and co-occurrences.
Results: The majority of records were housed in Embase (n = 178) and MEDLINE (n = 154). Over the last decade, both databases appeared to exhibit a bimodal distribution with the number of publications on search filters rising until 2006, before dipping in 2007, and steadily increasing until 2012. Few articles appeared in social science databases over the same time frame (e.g. Social Services Abstracts, n = 3).
Unsurprisingly, the term ‘search’ appeared in most titles, and quite often, was used as a noun adjunct for the word 'filter' and ‘strategy’. Across the papers, the purpose of searches as a means of 'identifying' information and gathering ‘evidence’ from 'databases' emerged quite strongly. Other terms relating to the methodological assessment of search filters, such as precision and validation, also appeared albeit less frequently.
Conclusions: Our findings show surprising commonality across the papers with regard to the literature on search filters. Much of the literature seems to be focused on developing search filters to identify and retrieve information, as opposed to testing or validating such filters. Furthermore, the literature is mostly housed in health-related databases, namely MEDLINE, CINAHL, and Embase, implying that it is medically driven. Relatively few papers focus on the use of search filters in the social sciences.

Copyright, Licences, and Statutory Fraud

In this article we explore the interplay between the law of copyright, contract, and statutory fraud within the digital environment, and in particular with respect to the business of commercial image licensing within the UK.

Select, Link and Rank: Diversified Query Expansion and Entity Ranking using Wikipedia

A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. We propose a method, Select-Link-Rank, that exploits semantic information from Wikipedia to generate diversified query expansions. SLR does collective processing of terms and Wikipedia entities in an integrated framework, simultaneously diversifying query expansions and entity recommendations. SLR starts with selecting informative terms from search results of the initial query, links them to Wikipedia entities, performs a diversity-conscious entity scoring and transfers such scoring to the term space to arrive at query expansion suggestions. Through an extensive empirical analysis and user study, we show that our method outperforms the state-of-the-art diversified query expansion and diversified entity recommendation techniques.

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules

Background
It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.
Results
In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks.
Conclusions
Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system