Commercialisation of eHealth Innovations in the Market of UK Healthcare Sector: A Framework for Sustainable Business Model.

This is the peer reviewed version of the following article: Festus Oluseyi Oderanti, and Feng Li, ‘Commercialization of eHealth innovations in the market of the UK healthcare sector: A framework for a sustainable business model’, Psychology & Marketing, Vol. 35 (2): 120-137, February 2018, which has been published in final form at https://doi.org/10.1002/mar.21074. Under embargo until 10 January 2020. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Demographic trends with extended life expectancy are placing increasing pressures on the UK state-funded healthcare budgets. eHealth innovations are expected to facilitate new avenues for cost-effective and safe methods of care, for enabling elderly people to live independently at their own homes and for assisting governments to cope with the demographic challenges. However, despite heavy investment in these innovations, large-scale deployment of eHealth continues to face significant obstacles, and lack of sustainable business models (BMs) is widely regarded as part of the greatest barriers. Through various empirical methods that include facilitated workshops, case studies of relevant organizations, and user groups, this paper investigates the reasons the private market of eHealth innovations has proved difficult to establish, and therefore it develops a framework for sustainable BMs that could elimiesnate barriers of eHealth innovation commercialization. Results of the study suggest that to achieve sustainable commercialization, BM frameworks and innovation diffusion characteristics should be considered complements but not substitutes.Peer reviewe

Diffusion of e-health innovations in 'post-conflict' settings: a qualitative study on the personal experiences of health workers.

BACKGROUND: Technological innovations have the potential to strengthen human resources for health and improve access and quality of care in challenging 'post-conflict' contexts. However, analyses on the adoption of technology for health (that is, 'e-health') and whether and how e-health can strengthen a health workforce in these settings have been limited so far. This study explores the personal experiences of health workers using e-health innovations in selected post-conflict situations. METHODS: This study had a cross-sectional qualitative design. Telephone interviews were conducted with 12 health workers, from a variety of cadres and stages in their careers, from four post-conflict settings (Liberia, West Bank and Gaza, Sierra Leone and Somaliland) in 2012. Everett Roger's diffusion of innovation-decision model (that is, knowledge, persuasion, decision, implementation, contemplation) guided the thematic analysis. RESULTS: All health workers interviewed held positive perceptions of e-health, related to their beliefs that e-health can help them to access information and communicate with other health workers. However, understanding of the scope of e-health was generally limited, and often based on innovations that health workers have been introduced through by their international partners. Health workers reported a range of engagement with e-health innovations, mostly for communication (for example, email) and educational purposes (for example, online learning platforms). Poor, unreliable and unaffordable Internet was a commonly mentioned barrier to e-health use. Scaling-up existing e-health partnerships and innovations were suggested starting points to increase e-health innovation dissemination. CONCLUSIONS: Results from this study showed ICT based e-health innovations can relieve information and communication needs of health workers in post-conflict settings. However, more efforts and investments, preferably driven by healthcare workers within the post-conflict context, are needed to make e-health more widespread and sustainable. Increased awareness is necessary among health professionals, even among current e-health users, and physical and financial access barriers need to be addressed. Future e-health initiatives are likely to increase their impact if based on perceived health information needs of intended users

Unacylated ghrelin promotes adipogenesis in rodent bone marrow via ghrelin O-acyl transferase and GHS-R1a activity: evidence for target cell-induced acylation

Despite being unable to activate the cognate ghrelin receptor (GHS-R), unacylated ghrelin (UAG) possesses a unique activity spectrum that includes promoting bone marrow adipogenesis. Since a receptor mediating this action has not been identified, we re-appraised the potential interaction of UAG with GHS-R in the regulation of bone marrow adiposity. Surprisingly, the adipogenic effects of intra-bone marrow (ibm)-infused acylated ghrelin (AG) and UAG were abolished in male GHS-R-null mice. Gas chromatography showed that isolated tibial marrow adipocytes contain the medium-chain fatty acids utilised in the acylation of UAG, including octanoic acid. Additionally, immunohistochemistry and immunogold electron microscopy revealed that tibial marrow adipocytes show prominent expression of the UAG-activating enzyme ghrelin O-acyl transferase (GOAT), which is located in the membranes of lipid trafficking vesicles and in the plasma membrane. Finally, the adipogenic effect of ibm-infused UAG was completely abolished in GOAT-KO mice. Thus, the adipogenic action of exogenous UAG in tibial marrow is dependent upon acylation by GOAT and activation of GHS-R. This suggests that UAG is subject to target cell-mediated activation – a novel mechanism for manipulating hormone activity

Severe traumatic injury during long duration spaceflight: Light years beyond ATLS

Traumatic injury strikes unexpectedly among the healthiest members of the human population, and has been an inevitable companion of exploration throughout history. In space flight beyond the Earth's orbit, NASA considers trauma to be the highest level of concern regarding the probable incidence versus impact on mission and health. Because of limited resources, medical care will have to focus on the conditions most likely to occur, as well as those with the most significant impact on the crew and mission. Although the relative risk of disabling injuries is significantly higher than traumatic deaths on earth, either issue would have catastrophic implications during space flight. As a result this review focuses on serious life-threatening injuries during space flight as determined by a NASA consensus conference attended by experts in all aspects of injury and space flight

MalSketch - A machine learning-based malware behaviour analysis framework

Malware samples has increased exponentially over the years, and there is a need to improve the efficiency of analysing large number of malware samples. Additionally, the diversity of malware types and the methods it employs to defeat analysis techniques has also increased steadily. Static analysis methods of malware are just not enough to combat modern malware attack as it has inherent limitation in that it is easily defeated by obfuscation and polymorphism. On the other hand, dynamic analysis methods of malware behaviour do not suffer from such limitations due to the fact that the samples are executed, therefore revealing its true behaviours. To address this problem, a framework for the automatic analysis of malware behaviour is proposed. The framework analyses malware behaviour, then convert the behaviour reports into a metalanguage format suitable for machine learning. To speed up computation, Minhash is used to represent samples, and Locality Sensitive Hashing is applied for nearest neighbour search in sublinear time. Disjoint-set Forest clustering algorithm is then applied to the results to cluster malware into family clusters. The framework achieves 97.4% true positive rate and 99.4% true negative rate, using a dataset of 65,000 from VirusShare. This shows that the framework works very well even for random dataset, and it is capable of daily malware samples clustering and to identify unknown malware. Keywords: Malware behaviour, malware analysis, clustering, automated analysi

Position tracking of underwater vehicle using extended Kalman Filter

Position tracking is essential for mobile robots for autonomous functionalities and navigation especially for robots that are deployed in underwater conditions. Hence, this thesis proposes the usage of the Extended Kalman Filter (EKF) for position tracking of an underwater vehicle. Underwater vehicles cannot use conventional GPS for position tracking due to radio signals being damped by the body of water surrounding it. Underwater GPS(UGPS) is used for predicting the position of underwater vehicles, but it suffers from latency issues. Therefore, estimation algorithms like Kalman Filter (KF) and EKF are applied to provide a consistent position value from the UGPS. The main advantage of the EKF estimation algorithm is it can estimate the state of a non-linear system without an observable model. It is a nonlinear extension of KF, and it is a popular method used in estimating robot position due to its simplicity and consistency. The main objective of this research is to implement EKF in underwater conditions using UGPS relative position and Inertial Measurement Unit (IMU) orientation. The secondary objective of this research is improving EKF positioning estimation by implementing of outlier filters. Overall, the proposed system allows accurate position tracking of underwater vehicles. Before EKF is applied, the dead reckoning model of the ROV was developed as the vehicle odometry. In addition, an experiment is conducted by evaluating the odometry of the robot where the transmitter of the UGPS is attached to the Remotely Operated Vehicle (ROV) and need to travel a pre-measured distance and compare the odometry output of the ROV with the measured distance. To test the effectiveness of the proposed method, the EKF was implemented offline with recorded data consisting of Underwater GPS (UGPS) and Inertial Measurement Unit (IMU). The filtered EKF output is evaluated by using MSE and RMSE to ensure the distinct features of the output signals are retained. The MSE and RMSE of median mean filter are less than 0.1 meter which signifies the filtered output of EKF retains the distinct features of the raw output of EKF. The proposed method can overcome the UGPS latency issues and accurately estimate the underwater vehicle’s pose