LNG vapour cloud dispersion modelling and simulations with OpenFOAM

Growth in demand for Liquefied Natural Gas (LNG) has increased calls for further research and development on LNG production and safer methods for its transportation. This paper presents the implementation of numerical models for dispersion of evaporated LNG in the open atmosphere. The developed model incorporates in its formulation LNG spill and pool formation into a source model. It is then coupled with a Computational Fluid Dynamics (CFD) approach in OpenFOAM for dispersion calculations. Atmospheric conditions such as average wind speed and direction were used to resolve wind boundary layers. The model also accounts for the humidity effect and its influence on air-density and buoyancy change. Verifications have been conducted using the experimental results from Maplin Sands series of tests by comparing the maximum evaporated gas concentration in every arc in relation to the release point. The results show good agreements between the model’s predictions and experiments

Multi-Objective Optimization of Demand Side Management and Multi DG in the Distribution System with Demand Response

The optimal management of distributed generation (DG) enhances the efficiency of the distribution system; On the other hand, increasing the interest of customers in optimizing their consumption improves the performance of DG. This act is called demand side management. In this study, a new method based on the intelligent algorithm is proposed to optimal operate the demand side management in the presence of DG units and demand response. Firstly, the best location and capacity of different technologies of DG are selected by optimizing the technical index including the active and reactive loss and the voltage profile. Secondly, the daily performance of multi-DG and grid is optimized with and without considering the demand response. The economic and environmental indices are optimized in this step. In both steps, the non-dominated sorting firefly algorithm is utilized to multi-objective optimize the objective functions and then the fuzzy decision-making method is used to select the best result from the Pareto optimal solutions. Finally, the proposed method is implemented on the IEEE 33-bus distribution system and actual 101-bus distribution systems in Khoy-Iran. The obtained numerical results indicate the impact of the proposed method on improving the technical, economic and environmental indices of the distribution system

Co-creation facilitates translational research on upper limb prosthetics

People who either use an upper limb prosthesis and/or have used services provided by a prosthetic rehabilitation centre, hereafter called users, are yet to benefit from the fast-paced growth in academic knowledge within the field of upper limb prosthetics. Crucially over the past decade, research has acknowledged the limitations of conducting laboratory-based studies for clinical translation. This has led to an increase, albeit rather small, in trials that gather real-world user data. Multi-stakeholder collaboration is critical within such trials, especially between researchers, users, and clinicians, as well as policy makers, charity representatives, and industry specialists. This paper presents a co-creation model that enables researchers to collaborate with multiple stakeholders, including users, throughout the duration of a study. This approach can lead to a transition in defining the roles of stakeholders, such as users, from participants to co-researchers. This presents a scenario whereby the boundaries between research and participation become blurred and ethical considerations may become complex. However, the time and resources that are required to conduct co-creation within academia can lead to greater impact and benefit the people that the research aims to serve

Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements

Abstract Background Myoelectric pattern recognition systems can decode movement intention to drive upper-limb prostheses. Despite recent advances in academic research, the commercial adoption of such systems remains low. This limitation is mainly due to the lack of classification robustness and a simultaneous requirement for a large number of electromyogram (EMG) electrodes. We propose to address these two issues by using a multi-modal approach which combines surface electromyography (sEMG) with inertial measurements (IMs) and an appropriate training data collection paradigm. We demonstrate that this can significantly improve classification performance as compared to conventional techniques exclusively based on sEMG signals. Methods We collected and analyzed a large dataset comprising recordings with 20 able-bodied and two amputee participants executing 40 movements. Additionally, we conducted a novel real-time prosthetic hand control experiment with 11 able-bodied subjects and an amputee by using a state-of-the-art commercial prosthetic hand. A systematic performance comparison was carried out to investigate the potential benefit of incorporating IMs in prosthetic hand control. Results The inclusion of IM data improved performance significantly, by increasing classification accuracy (CA) in the offline analysis and improving completion rates (CRs) in the real-time experiment. Our findings were consistent across able-bodied and amputee subjects. Integrating the sEMG electrodes and IM sensors within a single sensor package enabled us to achieve high-level performance by using on average 4-6 sensors. Conclusions The results from our experiments suggest that IMs can form an excellent complimentary source signal for upper-limb myoelectric prostheses. We trust that multi-modal control solutions have the potential of improving the usability of upper-extremity prostheses in real-life applications

Antidepressants during and after Menopausal Transition: A Systematic Review and Meta-Analysis

To assess the therapeutic benefits of antidepressants in depressive women during and after menopausal transition, PubMed, Cochrane Library, EMBASE and Science Direct were systematically searched from inception to February 1, 2020 for randomized controlled trials examining antidepressants compared to placebo. Primary outcome was change in depressive symptom severity, while secondary outcomes were rates of response/remission rates and dropout/discontinuation due to adverse events. Seven trials involving 1,676 participants (mean age = 52.6 years) showed significant improvement in depressive symptoms (k = 7, Hedges’ g = 0.44, 95% confidence interval (CI) = 0.32 to 0.57, p < 0.001) relative to that in controls. Furthermore, response (k = 3, odds ratio (OR) = 2.53, 95% CI = 1.24 to 5.15, p = 0.01) and remission (k = 3, OR = 1.84, 95% CI = 1.32 to 2.57, p < 0.001) rates were significantly higher in antidepressant-treated groups compared to those with controls. Although dropout rates did not differ between antidepressant and control groups (k = 6, OR = 0.93, 95% CI = 0.70 to 1.26, p = 0.68), the rate of discontinuation due to adverse events was significantly higher in antidepressant-treated groups (k = 6, OR = 0.55, 95% CI = 0.35 to 0.86, p = 0.01). Subgroup analysis indicated that antidepressants were also efficacious for depressive symptoms in those without diagnosis of MDD. The results demonstrated that antidepressants were efficacious for women with depressive syndromes during and after menopausal transition but associated with a higher risk of discontinuation due to adverse events

Associations between maternal urinary iodine assessment, dietary iodine intakes and neurodevelopmental outcomes in the child: A Systematic Review

Abstract Objective Mild to moderate iodine deficiency during pregnancy has been associated with adverse neurodevelopmental outcomes in offspring. Few research studies to date combine assessment of urinary iodine (UIC and/or ICr), biomarkers that best reflect dietary intake, with reported dietary intake of iodine rich foods in their assessment of iodine deficiency. Thus, a systematic review was conducted to incorporate both these important measures. Design Using PRISMA guidelines, a comprehensive search was conducted in three electronic databases (EMBASE®, MedLine® and Web of Science®) from January 1970–March 2021. Quality assessment was undertaken using the Newcastle Ottawa Scale. Eligible studies included reported assessment of iodine status through urinary iodine (UIC and/or ICr) and/or dietary intake measures in pregnancy alongside neurodevelopmental outcomes measured in the children. Data extracted included study author, design, sample size, country, gestational age, child age at testing, cognitive tests, urinary iodine assessment (UIC in μg/L and/or ICr in μg/g), dietary iodine intake assessment and results of associations for the assessed cognitive outcomes. Results Twelve studies were included with nine reporting women as mild-moderately iodine deficient based on World Health Organization (WHO) cut-offs for urinary iodine measurements < 150 μg/l, as the median UIC value in pregnant women. Only four of the nine studies reported a negative association with child cognitive outcomes based on deficient urinary iodine measurements. Five studies reported urinary iodine measurements and dietary intakes with four of these studies reporting a negative association of lower urinary iodine measurements and dietary iodine intakes with adverse offspring neurodevelopment. Milk was identified as the main dietary source of iodine in these studies. Conclusion The majority of studies classified pregnant women to be mild-moderately iodine deficient based on urinary iodine assessment (UIC and/or ICr) and/or dietary intakes, with subsequent offspring neurodevelopment implications identified. Although a considerable number of studies did not report an adverse association with neurodevelopmental outcomes, these findings are still supportive of ensuring adequate dietary iodine intakes and urinary iodine monitoring throughout pregnancy due to the important role iodine plays within foetal neurodevelopment. This review suggests that dietary intake data may indicate a stronger association with cognitive outcomes than urinary iodine measurements alone. The strength of this review distinguishes results based on cognitive outcome per urinary iodine assessment strategy (UIC and/or ICr) with dietary data. Future work is needed respecting the usefulness of urinary iodine assessment (UIC and/or ICr) as an indicator of deficiency whilst also taking account of dietary intakes

Multiview classification and dimensionality reduction of scalp and intracranial EEG data through tensor factorisation

Electroencephalography (EEG) signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In classification paradigms, algorithms are developed that can distinguish between these processes. In this work, we apply tensor factorisation to a set of EEG data from a group of epileptic patients and factorise the data into three modes; space, time and frequency with each mode containing a number of components or signatures. We train separate classifiers on various feature sets corresponding to complementary combinations of those modes and components and test the classification accuracy of each set. The relative influence on the classification accuracy of the respective spatial, temporal or frequency signatures can then be analysed and useful interpretations can be made. Additionaly, we show that through tensor factorisation we can perform dimensionality reduction by evaluating the classification performance with regards to the number mode components and by rejecting components with insignificant contribution to the classification accuracy

LNG vapour cloud dispersion modelling and simulations with OpenFOAM

Growth in demand for Liquefied Natural Gas (LNG) has increased calls for further research and development on LNG production and safer methods for its transportation. This paper presents the implementation of numerical models for dispersion of evaporated LNG in the open atmosphere. The developed model incorporates in its formulation LNG spill and pool formation into a source model. It is then coupled with a Computational Fluid Dynamics (CFD) approach in OpenFOAM for dispersion calculations. Atmospheric conditions such as average wind speed and direction were used to resolve wind boundary layers. The model also accounts for the humidity effect and its influence on air-density and buoyancy change. Verifications have been conducted using the experimental results from Maplin Sands series of tests by comparing the maximum evaporated gas concentration in every arc in relation to the release point. The results show good agreements between the model’s predictions and experiments