Myths and misperceptions in pacemaker patients: a quasi experimental study to assess effect of an educational intervention

Background Misconceptions regarding activities after pacemaker implantation can result in restrictions in daily life. This study aims at measuring the correction of misconceptions following an educational intervention utilizing a picture based brochure and personal counseling. Methods One hundred and twenty-eight patients were enrolled in a quasi experimental study on the day after pacemaker implantation. Patients’ perceptions about safety of various daily activities, medical procedures, and usage of appliances, in the presence of pacemaker was assessed using a questionnaire before and ten days after an educational intervention using a pictorial brochure. The number of correct responses before and after the intervention was compared to assess the change in patients’ perception. Results A total of 128 patients were interviewed at baseline, of which 115 followed-up at 10 days. Mean age of patients was 60.31 ± 12.81 years. In total, 76 (59.4%) were male and 61(47.7%) were illiterate. Mean correct responses increased from 64.47 ± 29.48% to 92.29 ± 14.42% (p < 0.001). Percent of questions answered correctly improved in all three categories after the intervention. Correct answers increased from 74.57 ± 24.94% to 95.65 ± 11.48% (p < 0.001) for routine daily activities, 51.09 ± 33.9% to 84.78 ± 20.86% (p < 0.001) for medical procedures and 64.03 ± 37.36% to 92.57 ± 18.79% (p < 0.001) for domestic appliance usage. Conclusion Misconception that can adversely affect pacemaker patient’s quality of life can be corrected by counseling with pictorial based brochures regardless of the patient’s baseline knowledge or education.

Temporal Patternization of Power Signatures for Appliance Classification in NILM

Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregated power signals. In this paper, we propose a novel approach called a temporal bar graph, which patternizes the operational status of the appliances and time in order to extract the inherent features from the aggregated power signals for efficient load identification. To verify the effectiveness of the proposed method, a temporal bar graph was applied to the total power and tested on three state-of-the-art deep learning techniques that previously exhibited superior performance in image classification tasks—namely, Extreme Inception (Xception), Very Deep One Dimensional CNN (VDOCNN), and Concatenate-DenseNet121. The UK Domestic Appliance-Level Electricity (UK-DALE) and Tracebase datasets were used for our experiments. The results of the five-appliance case demonstrated that the accuracy and F1-score increased by 19.55% and 21.43%, respectively, on VDOCNN, and by 33.22% and 35.71%, respectively, on Xception. A performance comparison with the state-of-the-art deep learning methods and image-based spectrogram approach was conducted.

Nanofabrication route to achieve sustainable production of next generation defect-free graphene: analysis and characterisation

In the past two decades, graphene has been one of the most studied materials due to its exceptional properties. The scalable route to cost-effective manufacture defect-free graphene has continued to remain a technical challenge. Intrinsically defect-free graphene changes its properties dramatically, and it is a challenging task to control the defects in graphene production using scaled-down subtractive manufacturing techniques. In this work, the exfoliation of graphite was investigated as a sustainable low-cost graphene manufacturing technique. The study made use of a simple domestic appliance e.g., a kitchen blender to churn graphene in wet conditions by mixing with N-Methyl-2-pyrrolidone (NMP). It was found that the centrifugal force-induced turbulent flow caused by the rotating blades exfoliates graphite flakes to form graphene. The technique is endowed with a high yield of defect-free graphene (0.3 g/h) and was deemed suitable to remove 10% fluoride content from the water and color absorption from fizzy drinks.

Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques

Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors, in order to make predictions and optimizations to anticipate the use of electrical appliances at home. However, the problem with this kind of analysis rests in how to isolate individual appliances from an aggregated consumption signal. In this work, we propose an unsupervised disaggregation method based on a controlled dataset obtained using smart meters in a standard household. By using soft computing techniques, the proposed methodology can identify the behavior of each of the devices from aggregated consumption records. In the approach developed in this work, it is possible to detect changes in power levels and to build a box model, consisting of a sequence of rectangles of different heights (power) and widths (time), which is highly adaptable to the real-life working conditions of household appliances. The system was developed and tested using data collected at households in France and the UK (UK-domestic appliance-level electricity (DALE) dataset). The proposed analysis method serves as a basis to be applied to large amounts of data collected by distribution companies with smart meters.

Performance Analysis of Brushless DC Motor Using Modified Queen Bee Evolution Based Genetic Algorithm Tuned PI Controller under Different Speed Conditions

The modeling of BLDC motor and performance analysis under diverse operating speed settings has been presented in this paper. BLDC motors gaining more & more attention from different Industrial and domestic appliance manufacturers due to its compact size, high efficiency and robust structure. Voluminous research and developments in the domains of material science and power electronics led to substantial increase in applications of BLDC motor to electric drives. This paper deals with the modeling of BLDC motor drive system along with a comparative study of modified queens bee evolution based GA tuned & manually tuned control schemes using MATLAB /SIMULINK. In order to evaluate the performance of proposed drive, simulation is carried out at different Mechanical load & speed conditions. Test outcomes thus achieved show that the model performance is satisfactory.