Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct...
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Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.
Accurate detection of natural or intentional contamination events in water distribution pipes is critical to drinking water safety. Efficient early warning systems that can detect contamination events require detectio...
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ISBN:
(纸本)9781450357647
Accurate detection of natural or intentional contamination events in water distribution pipes is critical to drinking water safety. Efficient early warning systems that can detect contamination events require detection algorithms that can accurately predict the occurrence of such events. This paper presents the development of adaptive neuro-fuzzy inference system (ANFIS) models for detecting the safety condition of water in pipe networks when concentrations of water quality variables in the pipes exceed their maximum thresholds. The event detection is based on time series data composed of pH, turbidity, color and bacteria count measured at the effluent of a drinking water utility and nine different locations of sensors in the distribution network in the city of Ålesund, Norway. The proposed ANFIS models correctly detected between 92% and 96% of the safety condition of the water in the pipe network, with approximately 1% false alarm rate during the testing stage. The models also achieved high rates of specificity and precision, with very high correlations between the predictions and actual conditions of the water in the pipes. The accuracy of the models achieved in this study suggests that the models can be useful in real time contamination event detection in the pipe networks.
IntroductionThis project explored the feasibility of implementing an innovative cross-curricular framework using an adaptive learning (AL) platform and telehealth simulations. ObjectiveTo determine the feasibility of ...
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IntroductionThis project explored the feasibility of implementing an innovative cross-curricular framework using an adaptive learning (AL) platform and telehealth simulations. ObjectiveTo determine the feasibility of implementing an innovative cross-curricular framework using an AL platform and telehealth simulations. MethodsA mixed-method pilot study was conducted using novel AL modules, adaptive case studies, and telehealth simulation. ResultsQuantitative data analysis demonstrated significant correlations within and across demographics using the Technology Acceptance Model (TAM) and Simulation Effectiveness Tool-Modified (SET-M). Specifically, significant correlations are evident between TAM ease of use items 1-6, 8, and 10 and TAM usefulness 1, 3, and 9, with SET-M items 3 and 5-15. Thematic analysis revealed that participants felt that the overall project was worthwhile and increased confidence in telehealth. ConclusionParticipants found the technology used in this study was easy and useful, and they indicated a positive experience with telehealth simulation. Overall, this study demonstrated that implementation of AL using our paradigm is feasible and supports further investigation into implementing a cross-curricular framework using an AL platform and telehealth simulations.
China, India, and the United States consume the most energy and emit the most CO2. According to ***, India's CO2 emission is 1.80 tnes per capita, which is harmful to living beings: hence, this study exhibits Indi...
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China, India, and the United States consume the most energy and emit the most CO2. According to ***, India's CO2 emission is 1.80 tnes per capita, which is harmful to living beings: hence, this study exhibits India's harmful CO2 emission effect and forecasts CO2 emission for the next ten years using univariate time-series data from 1980 to 2019. A multilayer perceptron is used in this study to analyse 2099 experimental data of binary systems made up of CO2 and ionic liquids and predict solubility. 33 different types of ionic liquids are represented in the dataset, which spans a wide variety of solubilities, pressures, and temperatures. In recent decades, greenhouse gas (GHG) emissions have caused air pollution and environmental problems in several countries. A precise prediction is essential for managing and planning for the decrease of greenhouse gas emissions. Furthermore, the Modified Coyote Optimization Algorithm was used to extract required properties. The empirical data revealed that predictions obtained from Multi-Layer Perceptron's Neural Network (MPNN) were more accurate than those derived from other models. The MPNN-MCOA identified a link between CO2 emissions, economic growth, and entrepreneurship. Government, personal liberty, education, and pollution all have a negative correlation. We conclude by emphasising the critical role of machinelearning in achieving carbon neutrality, from global-scale energy management to the revolutionary potential of atomic-scale MPNN-MCOA simulations for application development. As a result, one of the most reliable approaches for estimating greenhouse gas (GHG) emissions from agricultural regions and companies was recommended: the MPNN-MCOA model. The Artificial neural network is trained with three input combinations with three combinations of thermodynamic variables such as temperature (T), pressure (P), critical temperature (Tc), critical pressure, the critical compressibility factor (Zc), and the acentric f
Use of fossil fuel in industries causes Carbon emission, which is mostly responsible for global warming. Another aspect is that environment friendly energy production and sustainable development goal is highly depende...
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