A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Electrocatalytic oxidation of ascorbic acid using a single layer of gold nanoparticles immobilized on 1,6-hexanedithiol modified gold electrode

This paper describes the electrocatalytic oxidation of ascorbic acid (AA) in phosphate buffer solution by the immobilized citrate capped gold nanoparticles (AuNPs) on 1,6-hexanedithiol (HDT) modified Au electrode. X-ray photoelectron spectrum (XPS) of HDT suggests that it forms a monolayer on Au surface through one of the two single bondSH groups and the other single bondSH group is pointing away from the electrode surface. The free single bondSH groups of HDT were used to covalently attach colloidal AuNPs. The covalent attachment of AuNPs on HDT monolayer was confirmed from the observed characteristic carboxylate ion stretching modes of citrate attached with AuNPs in the infra-red reflection absorption spectrum (IRRAS) in addition to a higher reductive desorption charges obtained for AuNPs immobilized on HDT modified Au (Au/HDT/AuNPs) electrode in 0.1 M KOH when compared to HDT modified Au (Au/HDT) electrode. The electron transfer reaction of [Fe(CN)6]4−/3− was markedly hindered at the HDT modified Au (Au/HDT) electrode while it was restored with a peak separation of 74 mV after the immobilization of AuNPs on Au/HDT (Au/HDT/AuNPs) electrode indicating a good electronic communication between the immobilized AuNPs and the underlying bulk Au electrode through a HDT monolayer. The Cottrell slope obtained from the potential-step chronoamperometric measurements for the reduction of ferricyanide at Au/HDT/AuNPs was higher than that of bare Au electrode indicating the increased effective surface area of AuNPs modified electrode. The Au/HDT/AuNPs electrode exhibits excellent electrocatalytic activity towards the oxidation of ascorbic acid (AA) by enhancing the oxidation peak current to more than two times with a 210 mV negative shift in the oxidation potential when compared to a bare Au electrode. The standard heterogeneous electron transfer rate constant (ks) calculated for AA oxidation at Au/HDT/AuNPs electrode was 5.4 × 10−3 cm s−1. The oxidation peak of AA at Au/HDT/AuNPs electrode was highly stable upon repeated potential cycling. Linear calibration plot was obtained for AA over the concentration range of 1–110 μM with a correlation coefficient of 0.9950. The detection limit of AA was found to be 1 μM. The common physiological interferents such as glucose, oxalate ions and urea do not show any interference within the detection limit of AA. The selectivity of the AuNPs modified electrode was illustrated by the determination of AA in the presence of uric acid.

A switching control strategy for single and dual inductor push-pull converters

A switching control strategy is proposed for single and dual inductor current-fed push-pull converters. The proposed switching control strategy can be used with both current-fed push-pull converters with an active voltage doubler rectifier, or active rectifier, in the secondary side of the isolation transformer. The proposed switching control strategy makes turn-on and turn-off processes of the primary side power switches zero-voltage-switching and zero-current-switching respectively. The soft-switching operation of the single and dual inductor push-pull converters, with both types of active rectifier, is explained. Simulation and experimental results are provided to validate soft switching operation of the current-fed push-pull converters with the proposed switching control strategy.

Item reputation-aware recommender systems

Recommender systems provide personalized advice for customers online based on their own preferences, while reputation systems generate a community advice on the quality of items on the Web. Both systems use users’ ratings to generate their output. In this paper, we propose to combine reputation models with recommender systems to enhance the accuracy of recommendations. The main contributions include two methods for merging two ranked item lists which are generated based on recommendation scores and reputation scores, respectively, and a personalized reputation method to generate item reputations based on users’ interests. The proposed merging methods can be applicable to any recommendation methods and reputation methods, i.e., they are independent from generating recommendation scores and reputation scores. The experiments we conducted showed that the proposed methods could enhance the accuracy of existing recommender systems.

An efficient tagging data interpretation and representation scheme for item recommendation

A tag-based item recommendation method generates an ordered list of items, likely interesting to a particular user, using the users past tagging behaviour. However, the users tagging behaviour varies in different tagging systems. A potential problem in generating quality recommendation is how to build user profiles, that interprets user behaviour to be effectively used, in recommendation models. Generally, the recommendation methods are made to work with specific types of user profiles, and may not work well with different datasets. In this paper, we investigate several tagging data interpretation and representation schemes that can lead to building an effective user profile. We discuss the various benefits a scheme brings to a recommendation method by highlighting the representative features of user tagging behaviours on a specific dataset. Empirical analysis shows that each interpretation scheme forms a distinct data representation which eventually affects the recommendation result. Results on various datasets show that an interpretation scheme should be selected based on the dominant usage in the tagging data (i.e. either higher amount of tags or higher amount of items present). The usage represents the characteristic of user tagging behaviour in the system. The results also demonstrate how the scheme is able to address the cold-start user problem.

The Craving Experience Questionnaire : a brief, theory-based measure of consummatory desire and craving

Background and Aims Research into craving is hampered by lack of theoretical specification and a plethora of substance-specific measures. This study aimed to develop a generic measure of craving based on elaborated intrusion (EI) theory. Confirmatory factor analysis (CFA) examined whether a generic measure replicated the three-factor structure of the Alcohol Craving Experience (ACE) scale over different consummatory targets and time-frames. Design Twelve studies were pooled for CFA. Targets included alcohol, cigarettes, chocolate and food. Focal periods varied from the present moment to the previous week. Separate analyses were conducted for strength and frequency forms. Setting Nine studies included university students, with single studies drawn from an internet survey, a community sample of smokers and alcohol-dependent out-patients. Participants A heterogeneous sample of 1230 participants. Measurements Adaptations of the ACE questionnaire. Findings Both craving strength [comparative fit indices (CFI = 0.974; root mean square error of approximation (RMSEA) = 0.039, 95% confidence interval (CI) = 0.035–0.044] and frequency (CFI = 0.971, RMSEA = 0.049, 95% CI = 0.044–0.055) gave an acceptable three-factor solution across desired targets that mapped onto the structure of the original ACE (intensity, imagery, intrusiveness), after removing an item, re-allocating another and taking intercorrelated error terms into account. Similar structures were obtained across time-frames and targets. Preliminary validity data on the resulting 10-item Craving Experience Questionnaire (CEQ) for cigarettes and alcohol were strong. Conclusions The Craving Experience Questionnaire (CEQ) is a brief, conceptually grounded and psychometrically sound measure of desires. It demonstrates a consistent factor structure across a range of consummatory targets in both laboratory and clinical contexts.

A poroviscohyperelastic model for numerical analysis of mechanical behavior of single chondrocyte

The aim of this paper is to utilize a poroviscohyperelastic (PVHE) model which is developed based on the porohyperelastic (PHE) model to explore the mechanical deformation properties of single chondrocytes. Both creep and relaxation responses are investigated by using FEM models of micropipette aspiration and AFM experiments, respectively. The newly developed PVHE model is compared thoroughly with the SnHS and PHE models. It has been found that the PVHE can accurately capture both creep and stress relaxation behaviors of chondrocytes better than other two models. Hence, the PVHE is a promising model to investigate mechanical properties of single chondrocytes.

Efficient web browsing with a single-switch

This paper presents a new approach to web browsing in situ- ations where the user can only provide the device with a sin- gle input command device (switch). Switches have been de- veloped for example for people with locked-in syndrome and are used in combination with scanning to navigate virtual keyboards and desktop interfaces. Our proposed approach leverages the hierarchical structure of webpages to operate a multi-level scan of actionable elements of webpages (links or form elements). As there are a few methods already exist- ing to facilitate browsing under these conditions, we present a theoretical usability evaluation of our approach in com- parison to the existing ones, which takes into account the average time taken to reach any part of a web page (such as a link or a form) but also the number of clicks necessary to reach the goal. We argue that these factors contribute together to usability. In addition, we propose that our ap- proach presents additional usability benefits.

Comparison of eight published lumbar spine finite element models

Due to its ability to represent intricate systems with material nonlinearities as well as irregular loading, boundary, geometrical and material domains, the finite element (FE) method has been recognized as an important computational tool in spinal biomechanics. Current FE models generally account for a single distinct spinal geometry with one set of material properties despite inherently large inter-subject variability. The uncertainty and high variability in tissue material properties, geometry, loading and boundary conditions has cast doubt on the reliability of their predictions and comparability with reported in vitro and in vivo values. A multicenter study was undertaken to compare the results of eight well-established models of the lumbar spine that have been developed, validated and applied for many years. Models were subjected to pure and combined loading modes and their predictions were compared to in vitro and in vivo measurements for intervertebral rotations, disc pressures and facet joint forces. Under pure moment loading, the predicted L1-5 rotations of almost all models fell within the reported in vitro ranges; their median values differed on average by only 2° for flexion-extension, 1° for lateral bending and 5° for axial rotation. Predicted median facet joint forces and disc pressures were also in good agreement with previously published median in vitro values. However, the ranges of predictions were larger and exceeded the in vitro ranges, especially for facet joint forces. For all combined loading modes, except for flexion, predicted median segmental intervertebral rotations and disc pressures were in good agreement with in vivo values. The simulations yielded median facet joint forces of 0 N in flexion, 38 N in extension, 14 N in lateral bending and 60 N in axial rotation that could not be validated due to the paucity of in vivo facet joint forces. In light of high inter-subject variability, one must be cautious when generalizing predictions obtained from one deterministic model. This study demonstrates however that the predictive power increases when FE models are combined together. The median of individual numerical results can hence be used as an improved tool in order to estimate the response of the lumbar spine.

Do twins differ from single-born children on rates of behavioral difficulty in early childhood? A study of sibling relationship risk factors

The hypothesis that twinning raises risk for behavioral difficulties in childhood is persistent, yet there is limited and inconsistent empirical evidence. Simple mean comparison without control for confounders provides data on prevalence rates but cannot provide knowledge about risk or etiology. To assess the effect of twin relationship on behavior, comparison of patterns of association with single-born siblings may be informative. Analyses of data from an Australian sample of twins and single-born children (N = 305, mean age 4 years 9 months, and a follow-up 12 months later) were undertaken. The outcome measure was the Strengths and Difficulties Questionnaire. Predictor and control measures were obtained from parent report on the sibling/co-twin relationship behavior, family demographics, and obstetric history. We assessed difference between twins and single-born children in two respects: (a) mean behavioral difficulties, and (b) patterns of association between sibling relationship and behavioral difficulties, controlling for confounders. Results showed no differences in mean levels of behavioral difficulties between twins and single-born siblings identifying the importance of statistical control for family and obstetric adversity. Differences in patterns of association were found; for twin children, conflict in their co-twin relationship predicted externalizing behaviors, while for single-born children conflict predicted internalizing behaviors. The findings of mean differences between twin and single-born children in social background, but not in behavioral difficulties, underscore the necessity of statistical control to identify risk associated with twinning compared with risk associated with family and obstetric background factors.

Deformation of a single red blood cell in a microvessel

Red blood cells (RBCs) are the most common type of cells in human blood and they exhibit different types of motions and deformed shapes in capillary flows. The behaviour of the RBCs should be studied in order to explain the RBC motion and deformation mechanism. This article presents a numerical simulation method for RBC deformation in microvessels. A two dimensional spring network model is used to represent the RBC membrane, where the elastic stretch/compression energy and the bending energy are considered with the constraint of constant RBC surface area. The forces acting on the RBC membrane are obtained from the principle of virtual work. The whole fluid domain is discretized into a finite number of particles using smoothed particle hydrodynamics concepts and the motions of all the particles are solved using Navier--Stokes equations. Minimum energy concepts are used to simulate the deformed shape of the RBC model. To verify the model, the motion of a single RBC is simulated in a Poiseuille flow and the characteristic parachute shape of the RBC is observed. Further simulations reveal that the RBC shows a tank treading motion when it flows in a linear shear flow.

Single-cell force spectroscopy, an emerging tool to quantify cell adhesion to biomaterials

Cell adhesion receptors play a central role in sensing and integrating signals provided by the cellular environment. Thus, understanding adhesive interactions at the cell-biomaterial interface is essential to improve the design of implants that should emulate certain characteristics of the cell's natural environment. Numerous cell adhesion assays have been developed; among these, atomic force microscopy-based single-cell force spectroscopy (AFM-SCFS) provides a versatile tool to quantify cell adhesion at physiological conditions. Here we discuss how AFM-SCFS can be used to quantify the adhesion of living cells to biomaterials and give examples of using AFM-SCFS in tissue engineering and regenerative medicine. We anticipate that in the near future, AFM-SCFS will be established in the biomaterial field as an important technique to quantify cell-biomaterial interactions and thereby will contribute to the optimization of implants, scaffolds, and medical devices.