4653 research outputs found
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Control in healthcare interiors: the staff's perspective
No full textAs two of the most stressful professions, doctors and nurses work intensively in direct contact with patients. However, there has been little research into their perception of and satisfaction with healthcare interiors. To fill this gap in the literature, this study evaluated the working, resting, and dining spaces of healthcare staff in terms of control. Specifically, privacy, boundary and territory, and environmental control were analyzed in four state hospitals as case studies. Following a literature review, observations, semi-structured interviews, and surveys were conducted with doctors and nurses from the four hospitals in 2017. The findings showed that controlling privacy, boundaries, and environmental control elements like natural and artificial lighting, noise and odour were important for the participants. This indicates a need to ensure privacy and boundaries more effectively through separate spaces and furniture while noise and odour should also be given more attention.Architectur
VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments
No full textMeteorological drought is a common hydrological hazard that affects human life. It is one of the significant factors leading to water and food scarcity. Early detection of drought events is necessary for sustainable agricultural and water resources management. For the catchments with scarce meteorological observatory stations, the lack of observed data is the main leading cause of unfeasible sustainable watershed management plans. However, various earth science and environmental databases are available that can be used for hydrological studies, even at a catchment scale. In this study, the Global Drought Monitoring (GDM) data repository that provides real-time monthly Standardized Precipitation and Evapotranspiration Index (SPEI) across the globe was used to develop a new explicit evolutionary model for SPEI prediction at ungauged catchments. The proposed model, called VMD-GP, uses an inverse distance weighting technique to transfer the GDM data to the desired area. Then, the variational mode decomposition (VMD), in conjunction with state-of-the-art genetic programming, is implemented to map the intrinsic mode functions of the GMD series to the subsequent SPEI values in the study area. The suggested model was applied for the month-ahead prediction of the SPEI series at Erbil, Iraq. The results showed a significant improvement in the prediction accuracy over the classic GP and gene expression programming models developed as the benchmarks.Environmental Sciences || Water Resource
Machine Learning Enabled Sleep Time Estimation (MLE-STE) Architecture for Indoor Positioning in Energy-Efficient Mobile Internet of Things
No full textIndoor positioning and tracking systems require not only accurate position estimates of mobile IoT devices but also energy efficiency in order to maximize the battery life of the mobile IoT device. The contribution of this paper is the design of a machine learning enabled indoor positioning and tracking system in which artificial intelligence is utilized for the estimation of the duration for which a mobile IoT device needs to sleep in order to conserve energy. Our Machine Learning Enabled Sleep Time Estimation (MLE-STE) architecture is comprised of the following stages: First, it forms the forecast of the near-future trajectory of the mobile IoT device. Second, based on these forecasts, it determines the optimal sleep duration subject to the constraint of a maximum tolerable forecasting error. We demonstrate that our MLE-STE architecture outperforms both of the following state-of-the-art algorithms in this area: Positioning Interval based on Displacement (PID) and Dynamic Positioning Interval Based on Reciprocal Forecasting Error (DPI-RFE). This work represents a significant advance in the development of accurate indoor positioning and tracking algorithms that target the energy efficiency of mobile IoT devices.Computer Science, Artificial Intelligence || Computer Science, Theory & Methods || Engineering, Electrical & Electronic || Telecommunication
Voltage Regulation of Permanent Magnet Synchronous Generator in Variable Load by Using Buck-Converter in Aircraft Applications
No full textGenerator drive applications have been used in many different systems for a long time. There are plenty of different kinds of generators and generator control topologies. One of the generator control topologies is the DC-DC buck converter. So, implementing a buck converter topology to generator control makes the design simple, efficient and easy to apply. This paper presents a control application of a 15kW permanent magnet synchronous generator by using a buck converter in aircraft applications. This application involves new technologies such as Silicon Carbide and environmentally reliable components. According to the theoretical calculations, the selected components for practical implementation are given. The losses of selected semiconductors and passive components are calculated individually. At the end of the paper, the theoretical calculation and simulation results are compared with the experimental results.Engineering, Industrial || Engineering, Electrical & Electroni
Hybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport
No full textSediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective, the development of accurate models based on effective variables involved for flow velocity computation could provide a reliable solution in channel design. Furthermore, validity of sediment transport models is linked to the range of data used for the model development. Existing design models were established on the limited data ranges. Thus, the present study aimed to utilize all experimental data available in the literature, including recently published datasets that covered an extensive range of hydraulic properties. Extreme learning machine (ELM) algorithm and generalized regularized extreme learning machine (GRELM) were implemented for the modeling, and then, particle swarm optimization (PSO) and gradient-based optimizer (GBO) were utilized for the hybridization of ELM and GRELM. GRELM-PSO and GRELM-GBO findings were compared to the standalone ELM, GRELM, and existing regression models to determine their accurate computations. The analysis of the models demonstrated the robustness of the models that incorporate channel parameter. The poor results of some existing regression models seem to be linked to the disregarding of the channel parameter. Statistical analysis of the model outcomes illustrated the outperformance of GRELM-GBO in contrast to the ELM, GRELM, GRELM-PSO, and regression models, although GRELM-GBO performed slightly better when compared to the GRELM-PSO counterpart. It was found that the mean accuracy of GRELM-GBO was 18.5% better when compared to the best regression model. The promising findings of the current study not only may encourage the use of recommended algorithms for channel design in practice but also may further the application of novel ELM-based methods in alternative environmental problems.Computer Science, Interdisciplinary Applications || Computer Science, Theory & Method
In the nexus of sustainability, circular economy and food industry: Circular food package design
No full textIn these days, when the concepts of sustainability and circular economy are extremely prevalent, the most critical issue to be addressed is food. Since food is a basic need, circular economy practices have become extremely important, especially in food packaging. Not only the robustness or minimization of the package, but also the fact that the material used can be evaluated within the scope of circular economy ensures that food packaging is sustainable. Therefore, the first aim is to investigate the impact of food packaging design on sustainability. Moreover, to determine the relationship between circularity and food packaging design, and to minimize the conflicting food packaging design characteristics against sustainability by circular design are other aims of this study. With this aim, Quality Function Deployment method is applied for 29 consumer expectations about circular food packaging design (What's) and 34 technical requirements for these expectations for circular food packaging design (How's). The novelty of this study, since food packaging design is a newly discussed topic, it is important to consider the customer and technical expectations together in terms of circularity and sustainability. As a result, the most important consumer expectations are determined and relationship is presented with technical requirements each other. It is expected that this study will be beneficial for managers and practitioners.Green & Sustainable Science & Technology || Engineering, Environmental || Environmental Science
Transaction Processing Policies in a Flexible Shuttle-based Storage and Retrieval System by Real-time Data Tracking under Agent-based Modelling
No full textThis study investigates priority assignment rules (PARs) for transaction processing in automated warehouses featuring a shuttlebased storage and retrieval system (SBSRS). By incorporating real-time data tracking through agent-based modeling, the research explores the unique aspect of the SBSRS design, which involves flexible travel of robotic order picker shuttles between tiers. The paper proposes PARs under agent-based modeling to enhance multi-objective performance metrics, including average flow time (AFT), maximum flow time (MFT), outlier transaction AFT, and standard deviations of flow times (SD) within the system. Experimental evaluations are conducted with various warehouse designs, comparing the results against commonly used static scheduling rules. The findings demonstrate that real-time tracking policies significantly improve system performance. Specifically, prioritizing the processing of outliers based on transaction waiting time enhances MFT, SD, and other performance metrics, while minimizing adverse effects on AFT. Certain rules exhibit notable improvements in MFT and SD, while others achieve the lowest AFT values among all experiments. This paper contributes to the existing literature by presenting a multi-objective performance improvement procedure and highlighting the advantages of real-time data tracking-based scheduling policies in automated warehousing systems.Engineering, Multidisciplinary || Operations Research & Management Science || Mathematics, Applie
A REVIEW ON COMPUTATIONAL FLUID DYNAMICS SIMULATION METHODS FOR DIFFERENT CONVECTIVE DRYING APPLICATIONS
No full textThis paper focuses on the CFD studies on one of the commonly used drying pro-cesses for different applications. First, a brief information about drying is given with determining important properties that effect drying characteristics. Next, ba-sic principles of CFD modelling are explained while capabilities of computational processing are presented. A detailed literature survey about CFD studies in con-vective drying process is then conducted. Finally, some sound concluding remarks are listed. It may be concluded that the CFD is a powerful and flexible tool that can be adopted to many different physical situations including complex scenarios, results of CFD simulations represent good predictions for fluid-flow, heat and mass transfer of various drying methods and those numerical studies can be used for validation and controlling of applicability of new drying systems.Thermodynamic
Ensemble and optimized hybrid algorithms through Runge Kutta optimizer for sewer sediment transport modeling using a data pre-processing approach
No full textUncontrolled sediment deposition in drainage and sewer systems raises unexpected maintenance expenditures. To this end, implementation of an accurate model relying on effective parameters involved is a reliable benchmark. In this study, three machine learning techniques, namely extreme learning machine (ELM), multilayer perceptron neural network (MLPNN), and M5P model tree (M5PMT) || and three optimization approaches of Runge Kutta (RUN), genetic algorithm (GA), and particle swarm optimization (PSO) are applied for modeling. The optimization and ensemble hybridization approaches are applied in the modeling procedure. For the case of hybrid optimized models, the ELM and MLPNN models are hybridized with RUN, GA, and PSO algorithms to develop six hybrid models of ELM-RUN, ELM-GA, ELMPSO, MLPNN-RUN, MLPNN-GA, and MLPNN-PSO. Ensemble hybrid models are developed through coupling the ELM and MLPNN models with the M5PMT algorithm. The data pre-processing approach is applied to find the best randomness characteristic of the utilized data. Results illustrate that the RUNbased hybrid models outperform the GA- and PSO-based counterparts. Although the MLPNN-RUN and MLPNN-M5PMT hybrid models generate better results than their alternatives, MLPNN-M5PMT slightly outperforms MLPNN-RUN model with a coefficient of determination of 0.84 and a root mean square error of 0.88. The current study shows the superiority of the ensemble-based approach to the optimization techniques. Further investigation is needed by considering alternative optimization techniques to enhance sediment transport modeling. (c) 2023 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.Environmental Sciences || Water Resource
Energy loss and contraction coefficients-based vertical sluice gate's discharge coefficient under submerged flow using symbolic regression
No full textAccurate calculation of discharge is a critical task in terms of environmental and operational regulations. In the current study, a new approach for determining vertical sluice gates' flow discharge with a minor bias is proposed. Energy-momentum equations are used to characterize the physical expression of the phenomena intended for generation of the coefficient of discharge. The coefficient of discharge is then expressed according to coefficients of energy loss and contraction. Following that, the coefficient of discharge, coefficient of contraction, and coefficient of energy loss are calculated using an optimization approach. Then, dimensional analysis is conducted and regression equations for quantifying the coefficient of energy loss is produced using symbolic regression method. The derived contraction coefficient and energy loss coefficient formulas are accordingly utilized to compute the coefficient of discharge in the vertical sluice gate and also to determine flow discharge. For computing discharge, five different scenarios are considered. The developed approaches' performance is examined against selected benchmarks from the literature. The results show that the symbolic regression method can compute discharge more accurate than its alternatives.Environmental Science