A cross layer protocol based on mac and routing protocols for healthcare applications using Wireless Sensor Networks

Using Wireless Sensor Networks (WSNs) in healthcare systems has had a lot of attention in recent years. In much of this research tasks like sensor data processing, health states decision making and emergency message sending are done by a remote server. Many patients with lots of sensor data consume a great deal of communication resources, bring a burden to the remote server and delay the decision time and notification time. A healthcare application for elderly people using WSN has been simulated in this paper. A WSN designed for the proposed healthcare application needs efficient MAC and routing protocols to provide a guarantee for the reliability of the data delivered from the patients to the medical centre. Based on these requirements, A cross layer based on the modified versions of APTEEN and GinMAC has been designed and implemented, with new features, such as a mobility module and routes discovery algorithms have been added. Simulation results show that the proposed cross layer based protocol can conserve energy for nodes and provide the required performance such as life time of the network, delay and reliability for the proposed healthcare application.

ReadingAct RGB-D action dataset and human action recognition from local features

For general home monitoring, a system should automatically interpret people’s actions. The system should be non-intrusive, and able to deal with a cluttered background, and loose clothes. An approach based on spatio-temporal local features and a Bag-of-Words (BoW) model is proposed for single-person action recognition from combined intensity and depth images. To restore the temporal structure lost in the traditional BoW method, a dynamic time alignment technique with temporal binning is applied in this work, which has not been previously implemented in the literature for human action recognition on depth imagery. A novel human action dataset with depth data has been created using two Microsoft Kinect sensors. The ReadingAct dataset contains 20 subjects and 19 actions for a total of 2340 videos. To investigate the effect of using depth images and the proposed method, testing was conducted on three depth datasets, and the proposed method was compared to traditional Bag-of-Words methods. Results showed that the proposed method improves recognition accuracy when adding depth to the conventional intensity data, and has advantages when dealing with long actions.

Time-dependent gel to gel transformation of a peptide based supramolecular gelator

A dipeptide with a long fatty acid chain at its N-terminus gives hydrogels in phosphate buffer in the pH range 7.0–8.5. The hydrogel with a gelator concentration of 0.45% (w/v) at pH 7.46 (physiological pH) provides a very good platform to study dynamic changes within a supramolecular framework as it exhibits remarkable change in its appearance with time. Interestingly, the first formed transparent hydrogel gradually transforms into a turbid gel within 2 days. These two forms of the hydrogel have been thoroughly investigated by using small angle X-ray scattering (SAXS), powder X-ray diffraction (PXRD), field emission scanning electron microscopic (FE-SEM) and high-resolution transmission electron microscopic (HR-TEM) imaging, FT-IR and rheometric analyses. The SAXS and low angle PXRD studies substantiate different packing arrangements for the gelator molecules for these two different gel states (the freshly prepared and the aged hydrogel). Moreover, rheological studies of these two gels reveal that the aged gel is stiffer than the freshly prepared gel.

Anthelmintic activities against Haemonchus contortus or Trichostrongylus colubriformis from small ruminants are influenced by structural features of condensed tannins

Plants containing condensed tannins (CTs) may hold promise as alternatives to synthetic anthelmintic (AH) drugs for controlling gastrointestinal nematodes (GINs). However, the structural features that contribute to the AH activities of CTs remain elusive. This study probed the relationships between CT structures and their AH activities. Eighteen plant resources were selected based on their diverse CT structures. From each plant resource, two CT fractions were isolated and their in vitro AH activities were measured with the Larval Exsheathment Inhibition Assay, which was applied to Haemonchus contortus and Trichostrongylus colubriformis. Calculation of mean EC50 values indicated that H. contortus was more susceptible than T colubriformis to the different fractions and that the F1 fractions were less efficient than the F2 ones, as indicated by the respective mean values for H.contortus F1 = 136.9 ± 74.1 µg/ml; and for H.contortus F2 = 108.1 ± 53.2 µg/ml and for T colubriformis F1 = 233 ± 54.3 µg/ml and F2=166 ± 39.9 µg/ml. The results showed that the AH activity against H. contortus was associated with the monomeric subunits that give rise to prodelphinidins (P < 0.05) and with CT polymer size (P < 0.10). However, for T. colubriformis AH activity was correlated only with prodelphinidins (P < 0.05). These results suggest that CTs have different modes of action against different parasite species.

The application of a domains based analysis to family processes: implications for assessment and therapy

Social domains are classes of interpersonal processes each with distinct procedural rules underpinning mutual understanding, emotion regulation and action. We describe the features of three domains of family life – safety, attachment and discipline/expectation – and contrast them with exploratory processes in terms of the emotions expressed, the role of certainty versus uncertainty, and the degree of hierarchy in an interaction. We argue that everything that people say and do in family life carries information about the type of interaction they are engaged in – that is, the domain. However, sometimes what they say or how they behave does not make the domain clear, or participants in the social interactions are not in the same domain (there is a domain mismatch). This may result in misunderstandings, irresolvable arguments or distress. We describe how it is possible to identify domains and judge whether they are clear and unclear, and matched and mismatched, in observed family interactions and in accounts of family processes. This then provides a focus for treatment and helps to define criteria for evaluating outcomes.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

Environmental factors and features that influence visual search in a 3D WIMP interface

The challenge of moving past the classic Window Icons Menus Pointer (WIMP) interface, i.e. by turning it ‘3D’, has resulted in much research and development. To evaluate the impact of 3D on the ‘finding a target picture in a folder’ task, we built a 3D WIMP interface that allowed the systematic manipulation of visual depth, visual aides, semantic category distribution of targets versus non-targets; and the detailed measurement of lower-level stimuli features. Across two separate experiments, one large sample web-based experiment, to understand associations, and one controlled lab environment, using eye tracking to understand user focus, we investigated how visual depth, use of visual aides, use of semantic categories, and lower-level stimuli features (i.e. contrast, colour and luminance) impact how successfully participants are able to search for, and detect, the target image. Moreover in the lab-based experiment, we captured pupillometry measurements to allow consideration of the influence of increasing cognitive load as a result of either an increasing number of items on the screen, or due to the inclusion of visual depth. Our findings showed that increasing the visible layers of depth, and inclusion of converging lines, did not impact target detection times, errors, or failure rates. Low-level features, including colour, luminance, and number of edges, did correlate with differences in target detection times, errors, and failure rates. Our results also revealed that semantic sorting algorithms significantly decreased target detection times. Increased semantic contrasts between a target and its neighbours correlated with an increase in detection errors. Finally, pupillometric data did not provide evidence of any correlation between the number of visible layers of depth and pupil size, however, using structural equation modelling, we demonstrated that cognitive load does influence detection failure rates when there is luminance contrasts between the target and its surrounding neighbours. Results suggest that WIMP interaction designers should consider stimulus-driven factors, which were shown to influence the efficiency with which a target icon can be found in a 3D WIMP interface.