1,722,384 research outputs found
Data for: Image Clustering: An Unsupervised Approach to Categorize Visual Data in Social Science Research
No full textData for Zhang, Han, and Peng, Yilang. (2021). Image Clustering: An Unsupervised Approach to Categorize Visual Data in Social Science Research
A time-efficient multi-objective optimisation method for solar- and geothermal-LNG combined with organic Rankine cycles
No full textAs global energy demands continue to grow, the need for efficient utilisation of renewable energy is also increasing. Renewable energy sources frequently exhibit low-temperature characteristics, and the Organic Rankine Cycle (ORC) power generation technology is able to utilise this low-grade renewable thermal energy. Given a relatively low-temperature heat source, the Carnot efficiency of the ORC can be improved by choosing a low temperature cold source such as liquid natural gas (LNG). Hence, the combined ORC system utilising both low-grade renewable thermal energy and LNG cryogenic energy is worth studying. Optimisation is a commonly employed method to identify optimal operational parameters, working fluids, and system configurations. Traditionally, optimisations have predominantly been focused on the operational parameters alone. Therefore, optimisation procedures must initially be conducted separately for each working fluid-system configuration combination and then compared after the acquisition of all combination results, entailing an extended computational load for optimisation, especially when dealing with multi-objective optimisation.
This thesis focuses on the utilisation of a LNG cold source and two typical renewable heat sources: solar energy and geothermal energy. The Matlab models of the solar-LNG combined ORC system and the geothermal-LNG combined ORC system have been constructed. To address the time-consuming problem of optimisation, this thesis proposes an efficient multi-objective, multi-fluid, multi-configuration optimisation method termed the One-Shot Optimisation (OSO), where different working fluids and system configurations in addition to operational parameters, are included and compared within one optimisation process. The OSO method can significantly reduce the number of simulations required and thereby the total optimisation time needed. The OSO method is applied to both the solar-LNG combined ORC system and the geothermal-LNG combined ORC system, respectively, under three typical natural gas distribution pressures. For two combined ORC systems, as many as 9/8 operational parameters, 9/12 commonly used working fluids, and both up to 16 different cycle configurations are optimised during each simulation. The two-objective optimisation results are further analysed based on the thermodynamic weight (W 1 ), and two typical cases are analysed and compared: the balanced case (W 1 = 0.5), where the thermodynamic and economic metrics are treated equally, and the maximum case (W 1 = 1) where only the thermodynamic performance is considered.
The optimisation results show that the OSO method is capable of significantly reducing the number of required simulations and diminishing the total optimisation time. The computational time for each combined ORC system at each distribution pressure is approximately 5 hours. The comparison results show that in both combined ORC systems, although the maximum case can achieve the optimal thermodynamic performance, it is concurrently associated with the worst economic performance. The balanced case is capable of achieving considerable economic benefits while incurring minimal thermodynamic losses compared to the maximum case. In the solar-LNG combined ORC system, the balanced case optimal solution can yield as high as 84.53% system efficiency with only 14.04% UA compared with those of the maximum case at the distribution pressure of 3 MPa. In the geothermal-LNG combined ORC system, the balanced case is able to achieve 82.94% energy efficiency with only 44.17% EPC compared with those of the maximum case at the distribution pressure of 7 MPa
Replication Data for: The Impact of Health Worker Absenteeism on Patient Health Care Seeking Behavior, Testing and Treatment: A Longitudinal Analysis in Uganda
No full textThis package contains replication data for the paper "The Impact of Health Worker Absenteeism on Patient Health Care Seeking Behavior, Testing and Treatment: A Longitudinal Analysis in Uganda". The de-identified dataset contains information on household treatment seeking behavior and health work absenteeism for the full sample, collected in six districts over a 10-month period in Uganda. The code, produced in Stata, contains analysis code that generate the main results for the full sample and the sample of children under-five, respectively
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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