Methyl 2-amino-5-iso­propyl-1,3-thia­zole-4-carboxyl­ate

The title compound, C8H12N2O2S, forms a supramolecular network based on N-HN hydrogen-bonded centrosymmetric dimers that are linked in turn by N-HO contacts

A new method for venom extraction from venomous fish, green scat

Scatophagus argus argus (Green Scat) is a pretty aquarium fish. Its hard spines are venomous and can cause painful injury. In this study 60 specimens of Green Scat were collected periodically from coastal waters of Boushehr (south of Iran) from May 2011 to April 2012. Anatomical features of venomous spines were investigated. Scat venom was extracted from the spines in a new manner for keeping the specimens alive. The nature of venom was tested by SDS-PAGE. Ethical issues and animal welfare principles such as rapid and instantaneous anesthetizing, post operation disinfection and fast recovery of the specimens was practiced in order to minimize the complications. This method enhanced the purity and quantity of venom as demonstrated by 12 separated proteins in electrophoresis. New ethical issues were developed to surviving the specimens and prolong viability as well

The use of serious gaming for Open Learning environments

The extensive growth of Open Learning has been facilitated through technological innovation and continuous examination of the global Open Education development. With the introduction of compulsory computing subjects being incorporated into the UK school system in September 2014, the challenge of harnessing and integrating technological advances to aid children's learning is becoming increasingly important, referring to £1.1 million being invested to offer training programs for teachers to become knowledgeable and experienced in computing. From the age of 5, children will be taught detailed computing knowledge and skills such as; algorithms, how to store digital content, to write and test simple programs. Simultaneously, as the Internet and technology are improving, parents and teachers are looking at the incorporation of game based learning to aid children’s learning processes in more exciting and engaging ways. The purpose of game-based learning is to provide a better engagement, and in turn, an anticipated improvement in learning ability. This paper presents a research based on the investigation of properly combining the advantages of serious games and Open Learning to enhance the learning abilities of primary school children. The case study and the adequate evaluation address a learning environment in support of a history subject matter

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for predicting flood severity and classifying outcomes into three classes, normal, abnormal, and high-risk floods. Extensive research indicates that artificial intelligence algorithms could produce enhancement when utilised for the pre-processing of flood data. These approaches helped in acquiring better accuracy in the classification techniques. Neural network architectures generally produce good outcomes in many applications, however, our experiments results illustrated that random forest classifier yields the optimal results in comparison with the benchmarked models

Single-carrier frequency-domain equalization with hybrid decision feedback equalizer for Hammerstein channels containing nonlinear transmit amplifier

We propose a nonlinear hybrid decision feedback equalizer (NHDFE) for single-carrier (SC) block transmission systems with nonlinear transmit high power amplifier (HPA), which significantly outperforms our previous nonlinear SC frequency-domain equalization (NFDE) design. To obtain the coefficients of the channel impulse response (CIR) as well as to estimate the nonlinear mapping and the inverse nonlinear mapping of the HPA, we adopt a complex-valued (CV) B-spline neural network approach. Specifically, we use a CV B-spline neural network to model the nonlinear HPA, and we develop an efficient alternating least squares scheme for estimating the parameters of the Hammerstein channel, including both the CIR coefficients and the parameters of the CV B-spline model. We also adopt another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can be estimated using the least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. The effectiveness of our NHDFE design is demonstrated in a simulation study, which shows that the NHDFE achieves a signal-to-noise ratio gain of 4dB over the NFDE at the bit error rate level of 10−4

OnabotulinumtoxinA in the treatment of overactive bladder: a cost-effectiveness analysis versus best supportive care in England and Wales

The cost-effectiveness of onabotulinumtoxinA (BOTOX®) 100 U + best supportive care (BSC) was compared with BSC alone in the management of idiopathic overactive bladder in adult patients who are not adequately managed with anticholinergics. BSC included incontinence pads and, for a proportion of patients, anticholinergics and/or occasional clean intermittent catheterisation. A five-state Markov model was used to estimate total costs and outcomes over a 10-year period. The cohort was based on data from two placebo-controlled trials and a long-term extension study of onabotulinumtoxinA. After discontinuation of initial treatment, a proportion of patients progressed to downstream sacral nerve stimulation (SNS). Cost and resource use was estimated from a National Health Service perspective in England and Wales using relevant reference sources for 2012 or 2013. Results showed that onabotulinumtoxinA was associated with lower costs and greater health benefits than BSC in the base case, with probabilistic sensitivity analysis indicating an 89 % probability that the incremental cost-effectiveness ratio would fall below £20,000. OnabotulinumtoxinA remained dominant over BSC in all but two scenarios tested; it was also economically dominant when compared directly with SNS therapy. In conclusion, onabotulinumtoxinA appears to be a cost-effective treatment for overactive bladder compared with BSC alone

A Framework to Support Ubiquitous Healthcare Monitoring and Diagnostic for Sickle Cell Disease

Recent technology advances based on smart devices have improved the medical facilities and become increasingly popular in association with real-time health monitoring and remote/personals health-care. Healthcare organisations are still required to pay more attention for some improvements in terms of cost-effectiveness and maintaining efficiency, and avoid patients to take admission at hospital. Sickle cell disease (SCD) is one of the most challenges chronic obtrusive disease that facing healthcare, affects a large numbers of people from early childhood. Currently, the vast majority of hospitals and healthcare sectors are using manual approach that depends completely on patient input, which can be slowly analysed, time consuming and stressful. This work proposes an alert system that could send instant information to the doctors once detects serious condition from the collected data of the patient. In addition, this work offers a system that can analyse datasets automatically in order to reduce error rate. A machine-learning algorithm was applied to perform the classification process. Two experiments were conducted to classify SCD patients from normal patients using machine learning algorithm in which 99 % classification accuracy was achieved using the Instance-based learning algorithm

Training Neural networks for Experimental models: Classifying Biomedical Datasets for Sickle Cell Disease

This paper presents the use of various type of neural network architectures for the classification of medical data. Extensive research has indicated that neural networks generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. Up to date, most of hospitals and healthcare sectors in the United Kingdom are using manual approach for analysing patient input for sickle cell disease, which depends on clinician’s experience that can lead to time consuming and stress to patents. The results obtained from a range of models during our experiments have shown that the proposed Back-propagation trained feed-forward neural network classifier generated significantly better outcomes over the other range of classifiers. Using the ROC curve, experiments results showed the following outcomes for our models, in order of best to worst: Back-propagation trained feed-forward neural net classifier: 0.989, Functional Link neural Network: 0.972, in comparison to the Radial basis neural Network Classifiers with areas of 0.875, and the Voted Perception classifier: 0.766. A Linear Neural Network was used as baseline classifier to illustrate the importance of the previous models, producing an area of 0.849, followed by a random guessing model with an area of 0.524