| 51. |
- Areskoug, Linn, 1977-
(författare)
-
Den svenske mannens gränsland : Manlighet, nation och modernitet i Sven Lidmans Silfverstååhlsvit
- 2011
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis deals with the nationalistic imaginary in the novels of Sven Lidman, published 1910–1913. The novels hold forth a conservative point-of-view that embraces the bourgeois ideal of masculinity and the idea of the healthy, Swedish rural way of life as opposed to the destructive metropolis. This dualism is part of a dichotomy that structures the novels. It also entails continuity/fragmentation, the Swedish/the foreign, men/women, activity/ passivity as well as masculinity/femininity and unmanliness. In the Silfverstååhl-cycle the protagonists are young men of a noble, Swedish family. They progress from lost and introspective youths to grown men who are deeply concerned with and engaged in society. They are different representatives of the Swedish man – the farmer, the business man, the explorer and the clergy man. What unites them is how their “coming of age” develops, how through trial and struggle they become stronger and prove worthy of the manly role they finally take on. This is a major principle of the bourgeois masculinity that is also closely connected to the national identity of the men. There is also an ambiguity concerning modernity. Throughout the novels a critique of modern society is formulated, that acknowledges the modern age but simultaneously takes on a prudent attitude towards modern society. There is no going back for the Swedish nation; the modern times have to be confronted. The present is very important since it is the time for scrutiny. The handling of the modern era takes place in the developing processes of the young men, who have to be careful not to get trapped in the modern whirlpool that threatens to shatter the human being. The past, the familiar and the rural anchorage that the family relation entitles, is a defense against the destructive forces of modernity. But the past is not completely beneficial. Even though the past is of major importance to the national identity of the protagonists, they have to be very careful not to delve too much into the past because of the risk of paralysis and effeminization. In the nationalistic narrative the present encapsulates the past and the future. The Swedish man has to navigate in the borderland of modernity.
|
|
| 52. |
- Arvidsson, Lisa, et al.
(författare)
-
Healthcare personnel's working conditions in relation to risk behaviours for organism transmission : A mixed-methods study
- 2022
-
Ingår i: Journal of Clinical Nursing. - : Wiley. - 0962-1067 .- 1365-2702. ; 31:7-8, s. 878-894
-
Tidskriftsartikel (refereegranskat)abstract
- AIMS AND OBJECTIVES: To investigate healthcare personnel's working conditions in relation to risk behaviours for organism transmission.BACKGROUND: Healthcare personnel's behaviour is often influenced by working conditions that in turn can impact the development of healthcare-associated infections. Observational studies are scarce, and further understanding of working conditions in relation to behaviour is essential for the benefit of the healthcare personnel and the safety of the patients.DESIGN: A mixed-methods convergent design.METHODS: Data were collected during 104 h of observation at eight hospital units. All 79 observed healthcare personnel were interviewed. Structured interviews covering aspects of working conditions were performed with the respective first-line manager. The qualitative and quantitative data were collected concurrently and given equal priority. Data were analysed separately and then merged. The study follows the GRAMMS guidelines for reporting mixed-methods research.RESULTS: Regardless of measurable and perceived working conditions, risk behaviours frequently occurred especially missed hand disinfection. Healthcare personnel described staffing levels, patient-level workload, physical factors and interruptions as important conditions that influence infection prevention behaviours. The statistical analyses confirmed that interruptions increase the frequency of risk behaviours. Significantly higher frequencies of risk behaviours also occurred in activities where healthcare personnel worked together, which in the interviews was described as a consequence of caring for high-need patients.CONCLUSIONS: These mixed-methods findings illustrate that healthcare personnel's perceptions do not always correspond to the observed results since risk behaviours frequently occurred regardless of the observed and perceived working conditions. Facilitating the possibility for healthcare personnel to work undisturbed when needed is essential for their benefit and for patient safety.RELEVANCE FOR CLINICAL PRACTICE: The results can be used to enlighten healthcare personnel and managers and when designing future infection prevention work.
|
|
| 53. |
- Aslani, Mohammad, et al.
(författare)
-
A fast instance selection method for support vector machines in building extraction
- 2020
-
Ingår i: Applied Soft Computing. - : Elsevier BV. - 1568-4946 .- 1872-9681. ; 97
-
Tidskriftsartikel (refereegranskat)abstract
- Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as DR.LSH. The intuition of DR.LSH rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). DR.LSH is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that DR.LSH outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of DR.LSH can be found in https://github.com/mohaslani/DR.LSH.
|
|
| 54. |
- Aslani, Mohammad, et al.
(författare)
-
A Spatially Detailed Approach to the Assessment of Rooftop Solar Energy Potential based on LiDAR Data
- 2022
-
Ingår i: Proceedings of the 8th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM. - Setúbal : ScitePress. - 9789897585715 ; , s. 56-63
-
Konferensbidrag (refereegranskat)abstract
- Rooftop solar energy has long been regarded as a promising solution to cities’ growing energy demand and environmental problems. A reliable estimate of rooftop solar energy facilitates the deployment of photovoltaics and helps formulate renewable-related policies. This reliable estimate underpins the necessity of accurately pinpointing the areas utilizable for mounting photovoltaics. The size, shape, and superstructures of rooftops as well as shadow effects are the important factors that have a considerable impact on utilizable areas. In this study, the utilizable areas and solar energy potential of rooftops are estimated by considering the mentioned factors using a three-step methodology. The first step involves training PointNet++, a deep network for object detection in point clouds, to recognize rooftops in LiDAR data. Second, planar segments of rooftops are extracted using clustering. Finally, areas that receive sufficient solar irradiation, have an appropriate size, and fulfill photovoltaic installation requirements are identified using morphological operations and predefined thresholds. The obtained results show high accuracy for rooftop extraction (93%) and plane segmentation (99%). Moreover, the spatially detailed analysis indicates that 17% of rooftop areas are usable for photovoltaics.
|
|
| 55. |
- Aslani, Mohammad, et al.
(författare)
-
Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment
- 2022
-
Ingår i: Applied Energy. - : Elsevier. - 0306-2619 .- 1872-9118. ; 306
-
Tidskriftsartikel (refereegranskat)abstract
- The considerable potential of rooftop photovoltaics (RPVs) for alleviating the high energy demand of cities has made them a proven technology in local energy networks. Identification of rooftop areas suitable for installing RPVs is of importance for energy planning. Having these suitable areas referred to as utilizable areas greatly assists in a reliable estimate of RPVs energy production. Within such a context, this research aims to propose a spatially detailed methodology that involves (a) automatic extraction of buildings footprint, (b) automatic segmentation of roof faces, and (c) automatic identification of utilizable areas of roof faces for solar infrastructure installation. Specifically, the innovations of this work are a new method for roof face segmentation and a new method for the identification of utilizable rooftop areas. The proposed methodology only requires digital surface models (DSMs) as input, and it is independent of other auxiliary spatial data to become more functional. A part of downtown Gothenburg composed of vegetation and high-rise buildings with complex shapes was selected to demonstrate the methodology performance. According to the experimental results, the proposed methodology has a high success rate in building extraction (about 95% correctness and completeness) and roof face segmentation (about 85% completeness and correctness). Additionally, the results suggest that the effects of roof occlusions and roof superstructures are satisfactorily considered in the identification of utilizable rooftop areas. Thus, the methodology is practically effective and relevant for the detailed RPVs assessments in arbitrary urban regions where only DSMs are accessible.
|
|
| 56. |
- Aslani, Mohammad, et al.
(författare)
-
Continuous residual reinforcement learning for traffic signal control optimization
- 2018
-
Ingår i: Canadian journal of civil engineering (Print). - : NRC Research Press. - 0315-1468 .- 1208-6029. ; 45:8, s. 690-702
-
Tidskriftsartikel (refereegranskat)abstract
- Traffic signal control can be naturally regarded as a reinforcement learning problem. Unfortunately, it is one of the most difficult classes of reinforcement learning problems owing to its large state space. A straightforward approach to address this challenge is to control traffic signals based on continuous reinforcement learning. Although they have been successful in traffic signal control, they may become unstable and fail to converge to near-optimal solutions. We develop adaptive traffic signal controllers based on continuous residual reinforcement learning (CRL-TSC) that is more stable. The effect of three feature functions is empirically investigated in a microscopic traffic simulation. Furthermore, the effects of departing streets, more actions, and the use of the spatial distribution of the vehicles on the performance of CRL-TSCs are assessed. The results show that the best setup of the CRL-TSC leads to saving average travel time by 15% in comparison to an optimized fixed-time controller.
|
|
| 57. |
- Aslani, Mohammad, et al.
(författare)
-
Developing adaptive traffic signal control by actor-critic and direct exploration methods
- 2019
-
Ingår i: Proceedings of the Institution of Civil Engineers. - : Thomas Telford. - 0965-092X .- 1751-7710. ; 172:5, s. 289-298
-
Tidskriftsartikel (refereegranskat)abstract
- Designing efficient traffic signal controllers has always been an important concern in traffic engineering. This is owing to the complex and uncertain nature of traffic environments. Within such a context, reinforcement learning has been one of the most successful methods owing to its adaptability and its online learning ability. Reinforcement learning provides traffic signals with the ability automatically to determine the ideal behaviour for achieving their objective (alleviating traffic congestion). In fact, traffic signals based on reinforcement learning are able to learn and react flexibly to different traffic situations without the need of a predefined model of the environment. In this research, the actor-critic method is used for adaptive traffic signal control (ATSC-AC). Actor-critic has the advantages of both actor-only and critic-only methods. One of the most important issues in reinforcement learning is the trade-off between exploration of the traffic environment and exploitation of the knowledge already obtained. In order to tackle this challenge, two direct exploration methods are adapted to traffic signal control and compared with two indirect exploration methods. The results reveal that ATSC-ACs based on direct exploration methods have the best performance and they consistently outperform a fixed-time controller, reducing average travel time by 21%.
|
|
| 58. |
- Aslani, Mohammad, et al.
(författare)
-
Efficient and decision boundary aware instance selection for support vector machines
- 2021
-
Ingår i: Information Sciences. - : Elsevier. - 0020-0255 .- 1872-6291. ; 577, s. 579-598
-
Tidskriftsartikel (refereegranskat)abstract
- Support vector machines (SVMs) are powerful classifiers that have high computational complexity in the training phase, which can limit their applicability to large datasets. An effective approach to address this limitation is to select a small subset of the most representative training samples such that desirable results can be obtained. In this study, a novel instance selection method called border point extraction based on locality-sensitive hashing (BPLSH) is designed. BPLSH preserves instances that are near the decision boundaries and eliminates nonessential ones. The performance of BPLSH is benchmarked against four approaches on different classification problems. The experimental results indicate that BPLSH outperforms the other methods in terms of classification accuracy, preservation rate, and execution time. The source code of BPLSH can be found in https://github.com/mohaslani/BPLSH.
|
|
| 59. |
- Aslani, Mohammad, et al.
(författare)
-
Rooftop segmentation and optimization of photovoltaic panel layouts in digital surface models
- 2023
-
Ingår i: Computers, Environment and Urban Systems. - : Elsevier. - 0198-9715 .- 1873-7587. ; 105
-
Tidskriftsartikel (refereegranskat)abstract
- Rooftop photovoltaic panels (RPVs) are being increasingly used in urban areas as a promising means of achieving energy sustainability. Determining proper layouts of RPVs that make the best use of rooftop areas is of importance as they have a considerable impact on the RPVs performance in efficiently producing energy. In this study, a new spatial methodology for automatically determining the proper layouts of RPVs is proposed. It aims to both extract planar rooftop segments and identify feasible layouts with the highest number of RPVs in highly irradiated areas. It leverages digital surface models (DSMs) to consider roof shapes and occlusions in placing RPVs. The innovations of the work are twofold: (a) a new method for plane segmentation, and (b) a new method for optimally placing RPVs based on metaheuristic optimization, which best utilizes the limited rooftop areas. The proposed methodology is evaluated on two test sites that differ in urban morphology, building size, and spatial resolution. The results show that the plane segmentation method can accurately extract planar segments, achieving 88.7% and 99.5% precision in the test sites. In addition, the results indicate that complex rooftops are adequately handled for placing RPVs, and overestimation of solar energy potential is avoided if detailed analysis based on panel placement is employed.
|
|
| 60. |
- Aslani, Mohammad, et al.
(författare)
-
Traffic signal optimization through discrete and continuous reinforcement learning with robustness analysis in downtown Tehran
- 2018
-
Ingår i: Advanced Engineering Informatics. - : Elsevier BV. - 1474-0346 .- 1873-5320. ; 38, s. 639-655
-
Tidskriftsartikel (refereegranskat)abstract
- Traffic signal control plays a pivotal role in reducing traffic congestion. Traffic signals cannot be adequately controlled with conventional methods due to the high variations and complexity in traffic environments. In recent years, reinforcement learning (RL) has shown great potential for traffic signal control because of its high adaptability, flexibility, and scalability. However, designing RL-embedded traffic signal controllers (RLTSCs) for traffic systems with a high degree of realism is faced with several challenges, among others system disturbances and large state-action spaces are considered in this research.The contribution of the present work is founded on three features: (a) evaluating the robustness of different RLTSCs against system disturbances including incidents, jaywalking, and sensor noise, (b) handling a high-dimensional state-action space by both employing different continuous state RL algorithms and reducing the state-action space in order to improve the performance and learning speed of the system, and (c) presenting a detailed empirical study of traffic signals control of downtown Tehran through seven RL algorithms: discrete state Q-learning(λ" role="presentation">), SARSA(λ" role="presentation">), actor-critic(λ" role="presentation">), continuous state Q-learning(λ" role="presentation">), SARSA(λ" role="presentation">), actor-critic(λ" role="presentation">), and residual actor-critic(λ" role="presentation">).In this research, first a real-world microscopic traffic simulation of downtown Tehran is carried out, then four experiments are performed in order to find the best RLTSC with convincing robustness and strong performance. The results reveal that the RLTSC based on continuous state actor-critic(λ" role="presentation">) has the best performance. In addition, it is found that the best RLTSC leads to saving average travel time by 22% (at the presence of high system disturbances) when it is compared with an optimized fixed-time controller.
|
|
