The role of trust in e-government adoption: A systematic literature review

Electronic government (e-government) is a concept that has been adopted in most countries for the purposes of providing government services digitally, improving transparency between government and citizens and enabling additional communication channels with the government. Although e-government readiness in most countries is at a high level, adoption of e-government services is still considered tentative. A critical review of the literature suggests that this may be linked to citizens’ trust in government and e-government. As such, there is a need to investigate the role of trust in e-government adoption. For this purpose, a systematic literature review was conducted in order to observe research design, methodologies and approaches adopted in these studies as well as limitations identified and recommendation for future studies. The findings highlight that quantitative techniques and survey research methods appear to have been much preferred over other available alternatives such as qualitative techniques and interview methods or mixed methods in studies relating to trust in e-government adoption

Association of Physical Activity with Co-morbid Conditions in Geriatric Population

To find out association of physical activity with co-morbid conditions in geriatric population, a cross-sectional study was conducted in different cties of Pakistan in 2015. A total of 114 participants were inducted by non-probability convenience sampling technique. Data was collected after informed verbal consent by a validated questionnaire that is Rapid Assessment of Physical Activity (RAPA). Participants were categorized into two groups i.e. physically active and physically inactive. Data was entered and analyzed in SPSS version 20. There were 66 (57.9%) males and 48 (42.1%) females with mean age of 57.04±7.348 years. Among hypertensive individuals (n=43, 37.7%) there were 39 (90.7%) physically inactive, among individuals having angina (n=17, 14.9%) there were 15 (88.2%) physically inactive. Out of 37 (32.5%) diabetics, 35 (94.6%) were physically inactive. Among individuals suffering from arthritis (n=40, 35.1%), there were 38 (95%) physically inactive. A significant association was found between physical activity and diabetes and arthritis with p-value of 0.048 and 0.029 respectively. Physical activity is significantly associated with diabetes and arthritis in geriatric population. Adequate physical activity should be performed to reduce the risk of co-morbid conditions and improve the quality of life in geriatric population

Probabilistic locomotion mode recognition with wearable sensors

Recognition of locomotion mode is a crucial process for control of wearable soft robotic devices to assist humans in walking activities. We present a probabilistic Bayesian approach with a sequential analysis method for recognition of locomotion and phases of the gait cycle. Our approach uses recursive accumulation of evidence, as biological systems do, to reduce uncertainty present in the sensor measurements, and thus improving recognition accuracy. Data were collected from a wearable sensor, attached to the shank of healthy human participants, from three locomotion modes; level-ground walking, ramp ascent and ramp descent. We validated our probabilistic approach with recognition of locomotion in steady-state and gait phases in transitional states. Furthermore, we evaluated the effect, in recognition accuracy, of the accumulation of evidence controlled by increasing belief thresholds. High accuracy results achieved by our approach, demonstrate its potential for robust control of lower limb wearable soft robotic devices to provide natural and safe walking assistance to humans

Simultaneous Bayesian recognition of locomotion and gait phases with wearable sensors

Recognition of movement is a crucial process to assist humans in activities of daily living, such as walking. In this work, a high-level method for the simultaneous recognition of locomotion and gait phases using wearable sensors is presented. A Bayesian formulation is employed to iteratively accumulate evidence to reduce uncertainty, and to improve the recognition accuracy. This process uses a sequential analysis method to autonomously make decisions, whenever the recognition system perceives that there is enough evidence accumulated. We use data from three wearable sensors, attached to the thigh, shank, and foot of healthy humans. Level-ground walking, ramp ascent and descent activities are used for data collection and recognition. In addition, an approach for segmentation of the gait cycle for recognition of stance and swing phases is presented. Validation results show that the simultaneous Bayesian recognition method is capable to recognize walking activities and gait phases with mean accuracies of 99.87% and 99.20%. This process requires a mean of 25 and 13 sensor samples to make a decision for locomotion mode and gait phases, respectively. The recognition process is analyzed using different levels of confidence to show that our method is highly accurate, fast, and adaptable to specific requirements of accuracy and speed. Overall, the simultaneous Bayesian recognition method demonstrates its benefits for recognition using wearable sensors, which can be employed to provide reliable assistance to humans in their walking activities

Prediction of gait events in walking activities with a Bayesian perception system

In this paper, a robust probabilistic formulation for prediction of gait events from human walking activities using wearable sensors is presented. This approach combines the output from a Bayesian perception system with observations from actions and decisions made over time. The perception system makes decisions about the current gait events, while observations from decisions and actions allow to predict the most probable gait event during walking activities. Furthermore, our proposed method is capable to evaluate the accuracy of its predictions, which permits to obtain a better performance and trade-off between accuracy and speed. In our work, we use data from wearable inertial measurement sensors attached to the thigh, shank and foot of human participants. The proposed perception system is validated with multiple experiments for recognition and prediction of gait events using angular velocity data from three walking activities; level-ground, ramp ascent and ramp descent. The results show that our method is fast, accurate and capable to evaluate and adapt its own performance. Overall, our Bayesian perception system demonstrates to be a suitable high-level method for the development of reliable and intelligent assistive and rehabilitation robots

Viability of Split Thickness Autogenous Skin Transplantation in Canine Distal Limb Reconstruction – An Experimental Evaluation

Distal limb reconstruction is complicated by the paucity of local tissues and the frequent association of orthopedic injury with cutaneous loss. Though, second-intention healing or skin stretching techniques are used for wounds involving less than a 30% circumference of the limb, however, skin grafts are recommended for reconstruction of larger superficial wounds. The present study was designed to clinically evaluate the viability of split thickness autogenous skin transplantation (STAST) in dogs. Standardized surgical defects of variable size i.e. 3×3, 4×4 and 5×5 sq cm were made on the left middle radial area (forearm) of 15 mongrel dogs assigned to Group A, B and C, respectively having 5 dogs each. Split thickness autogenous skin grafts were harvested from mid thorax and placed in these defects through several simple interrupted sutures. Results indicated a success rate of 80% with no clinical difference in the survival rate of three different sizes of grafts used. Hence, STAST can successfully be used for canine distal limb reconstruction

Gender Dimorphism of the Cardiac Dysfunction in Murine Sepsis: Signalling Mechanisms and Age-Dependency

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.JC is supported by a jointly funded PhD-studentship of the China Scholarship Council (grant number 201206240146) and Queen Mary University of London (QMUL). SMC is supported by a Research Fellowship of the German Research Foundation (Deutsche Forschungsgemeinschaft; DFG CO 912/1-1). NSAP is, in part, supported by the Bart’s and The London Charity (753/1722). This work is supported, in part, by the William Harvey Research Foundation and by a grant from the University of Turin (Ricerca Locale ex-60% 2013). This work forms part of the research themes contributing to the translational research portfolio of Barts and the London Cardiovascular Biomedical Research Unit, which is supported and funded by the National Institute of Health Research. This work also contributes to the Organ Protection research theme of the Barts Centre for Trauma Sciences, supported by the Barts and The London Charity (Award 753/1722)

Methanol fractionations of Catha edulis frosk (Celastraceae) contracted lewis rat aorta in vitro: a comparison between crimson and green leaves

The study investigated the effect of methanol extract and its fractionations obtained from Yemeni khat on the smooth muscle isometric tension in Lewis rat aortal ring preparations and compared the effects of the crimson and green leaves. Khat leaves were sorted into green (khat Light; KL) and crimson (khat Dark; KD) leaves, extracted with methanol, followed with solvent-solvent extraction (benzene, chloroform and ethylacetate). The contractile activity of the fractions was tested using aortal ring preparations. The control (phenylepherine contraction) methanol extracts contracted aortas at concentrations 250, 125 and 67.5 μg /1 ml buffer by 80.2% , 57.3%, 26.4% and 81.5%, 65.6% , 24.6% for KL and KD, respectively. Fractions of benzene (BF) and ethylacetate (EaF) contracted the aorta with 2μgm, whereas, chloroform (ChF) with 1 μgm / 1 ml buffer was less potent. The shape of contraction curve produced by EaF differed from that of ChF and BF of both (KL and KD). The EaF induced-contraction peaked after 3.3 ± 0.94 mins, whereas those of BF and CHF peaked after 18.0 ± 2.2, 19.7 ± 0.94 mins, respectively. Pre-incubation with nifedipine (10-6 M) insignificantly reduced the contraction induced by all fractionations, but prazosin (10-6 M) reduced the contraction by 81.9%, 63.1%, 71.8% with p= 0.23, 0.09, 0.15 for BF, ChF and EaF of KL, respectively. It significantly reduced contraction of ChF, 64.1%; p= 0.02, and of EaF, 73.5%; p= 0.04 of KD, while the reduction in contraction of BF was 63.1%; p= 0.06. In conclusion, fractions of green and crimson Yemeni khat leaves contracted aortas of Lewis rats. Both leaves behave almost similarly. Contraction induced by chloroform fraction produced alphasympathetic activity.Key words: Catha edulis, aorta contraction, rat, cathinone