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Treating the whole person: Philosophical health
Get PDFEditorial for the 2023 philosophy thematic edition
The influence of Hollywood fiction on attitudes towards transgender people
Get PDFPrevious research found that viewing positively portrayed transgender characters in Hollywood TV series reduces negative attitudes towards transgender people. This study investigates whether exposure to short clips from Hollywood fiction can also influence participant attitudes. Using a feeling thermometer (FT), a total of 132 participants viewed either a positive, negative or no-portrayal video and indicated their attitudes towards transgender people pre- and post-exposure. A 3x2 mixed-design ANOVA found a significant interaction effect between the type of portrayal and the point in time of measurement. The results highlight the importance of displaying more positive transgender storylines in films and TV series, and the potential of using (short) videos as part of interventions in educational and the therapeutical settings
Librarians for critical digital justice: a space to explore digital inequities in academic institutions
No full textAcademic Librarians have been silent for too long about the inequalities embedded within the digital technologies that are used within their academic institutions. This webinar departs from the Global North Higher Education's techno-deterministic narratives. A selection of Critical Librarians will deliver 15 minute ‘provocations’ of truth-telling to promote the accountability that is needed if genuine digital equity within the Academy is to be achieved
Whole-genome sequencing and comparative genomics analysis of a newly emerged multidrug-resistant Klebsiella pneumoniae isolate of ST967
Get PDFWhole-genome sequencing and population genetics analysis of K. pneumoniae are scarce from LMICs, and none has been reported for Armenia. Multilevel comparative analysis revealed that ARM01 (an isolate belonging to a newly emerged K. pneumoniae ST967 lineage) was genetically similar to two isolates recovered from Qatar
Role of satellite precipitation products in real-time predictions of urban rainfall-runoff by using machine learning modelling
Get PDFThe accurate prediction of runoff features such as water level and flow is valuable for planning and operation of urban drainage systems (UDS), especially for appropriately acting as flood control mechanisms during extreme rainfall events which are constantly impacted by climate change variables [1]. In addition, cost-effective design, and operation of flood control measures such as smart UDS require highly accurate rainfall predictions across the catchment area, i.e., intensity and duration [2]. Furthermore, sufficient lead time is needed to activate the control mechanisms on the UDS without affecting the accuracy of the predictions. It seems that the emerging use of satellite precipitation products (SPPs) is promising for obtaining predictions with longer lead times [3]. Hence, more exploration of potential runoff predictions by using SPPs is worth investigating to achieve a more accurate and longer lead time.
This study employs a type of SPPs i.e., global precipitation measurement-integrated multi-satellite retrieval product (GPM-IMERG) to predict rainfall-runoff duration, peak and volume, as well as changes in flow over the course of the event at 30-minute intervals. In order to train and validate the machine learning model, the data from GPM-IMERG V06 was merged with ground data from the catchment precipitation gauge and flow sensor. The methodology is demonstrated by its application to the rainfall-runoff modelling of a real-world small urban sub-catchment area and its performance is evaluated by comparing it with the runoff predictions from physically based simulation models [4].
Results show that while using SPPs solely can provide accurate predictions, significant improvement can be obtained when this data is integrated with ground monitoring data. The model output can be utilised for better design, planning and management of UDS technologies as flood control tools and consequently real-time operation of UDS in urban flooding.
[1] Ferrans, P., Torres, M., Temprano, J., Sánchez, J., (2022). Sustainable Urban Drainage System (SUDS) modelling supporting decision-making: A systematic quantitative review. Science of The Total Environment. 806(2), 150447.
[2] Guptha, G., Swain, S., Al-Ansari, N., Taloor, A., Dayal, D. (2022). Assessing the role of SuDS in resilience enhancement of urban drainage system: A case study of Gurugram City, India. Urban Climate, 41, 101075.
[3] Piadeh, F., Behzadian, K., Alani, A. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, 127476.
[4] Broekhuizen, I., Leonhardt, G., Marsalek, J., & Viklander, M. (2020). Event selection and two-stage approach for calibrating models of green urban drainage systems. Hydrology and Earth System Sciences, 24(2), 869–885
Using ensemble data mining modelling for nonbinary overflow detection in urban flooding
Get PDFApplication of data-driven modelling especially using data mining techniques in flood warning systems has received significant attention recently due mainly to its well-explored sustainable solution for alleviating disruptive socio-economic effects of flood occurrence [1]. Various machine learning models with hybrid data mining techniques have been applied for water level prediction or overflow detection. However, the concept of time-series ensemble modelling has yet to be perceived well, particularly application of nonbinary classification for overflow detection and associated flood risk management [2].
This study presents a new real-time nonbinary overflow detection in urban flooding through extraction of rainfall key features by developing weak learner base models and proposing time-series multi-classification ensemble model. This framework is demonstrated by its application on real case study of urban drainage systems (UDS) located in London, UK. Extracted rainfall features which are selected by partial least squares analysis include (1) rainfall duration, (2) rainfall intensity, (3) evidence of previous rainfall occurrence, and (4) rainfall date of the year. These features are then used to develop seven base models including (1) discriminant analysis, (2) decision tree, (3) Gaussian process regression, (4) K-nearest neighbourhood, (5) Naïve bayes, (6) neural network pattern recognition, and (7) support vector machine to detect one of the three condition of (1) overflow, (2) water level rise is expected but drained successfully without any overflow occurrence, (3) no water level rise is expected. A novel ensemble model (ENS) which blends the performance of developed base models into the decision tree structure was then developed for overflow detection of next twelve 15-min timesteps (i.e., 3 hrs). The result performance of this model is compared by two well-practiced models i.e., stacked random forest (ERF), and nagging K-nearest neighbourhood (EKN) [3]. Confusion matrix is selected as a method of performance assessment in which total positive ratio, accuracy, and total negative ratio are picked up as key performance indicators.
Results show two new proposed rainfall features named “evidence of previous rainfall occurrence” and “rainfall date of the year” could significantly enhance the base model’s accuracy. Furthermore, ENS model could reduce overestimation and underestimation miss rates by nearly 10% in total for 3 hrs-ahead overflow detection, whereas these figures are 37% and 39% for total miss rate of ERF and EKN respectively in the same detection duration. Furthermore, the rate of correct high-hazard overflow detections is 88% in comparison to 64% in ERF and 24% in EKN, which highlights superior ability of the proposed model in early warning alarms of high-hazard situations.
References
[1] Rezaie Adaryani, F., Mousavi, S. Jafari, F. (2022). Short-term rainfall forecasting using machine learning-based approaches of PSO-SVR, LSTM and CNN. Journal of Hydrology, 614(A), 128463.
[2] Piadeh, F., Behzadian, K., Alani, A. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, 127476.
[3] Piadeh, F., Behzadian, K., Alani, A.M. (2022). Multi-Step Flood Forecasting in Urban Drainage Systems Using Time-series Data Mining Techniques. Water Efficiency Conference, West Indies, Trinidad and Tobago. repository.uwl.ac.uk/id/eprint/9690 [Accessed 31/12/2022]
Few interventions support the affected other on their own: A systematic review of individual level psychosocial interventions to support those harmed by others’ alcohol use
Get PDFIntroduction: Over 100 million individuals worldwide experience negative outcomes as a function of a family member's substance use. Other reviews have summarized evidence on interventions; however, success often depends on the behavior of the individual causing harm, and they may not be ready or able to change. Aim: To identify and describe evaluations of psychosocial interventions which can support those affected by alcohol harm to others independent of their drinking relative or friend.
Methods: A systematic review/narrative synthesis of articles from 11 databases pre-registered on PROSPERO (CRD42021203204).
Results: Those experiencing the harm were spouses/partners, or adult children/students who have parents with alcohol problems. Studies (n=7) are from the UK, USA, Korea, Sweden, Mexico, and India. Most participants were female (71-100%). Interventions varied from guided imagery, cognitive-behavioral therapy, motivational interviewing, and anger management. Independent interventions may support those affected by another's alcohol use, although there was considerable variation in outcomes targeted by the intervention design.
Conclusions: Small-scale studies suggest psychosocial interventions ease suffering from alcohol's harm to others, independent of the drinking family member. Understanding affected others’ experience and need is important given the impact of alcohol’s harm to others; however, there is a lack of quality evidence informing strategies to support these individuals
State-of-the-art report: the self-healing capability of alkali-activated slag (AAS) concrete
Get PDFAlkali-activated slag (AAS) has emerged as a potentially sustainable alternative to ordinary Portland cement (OPC) in various applications since OPC production contributed about 12% of global CO2 emissions in 2020. AAS offers great ecological advantages over OPC at some levels such as the utilization of industrial by-products and overcoming the issue of disposal, low energy consumption, and low greenhouse gas emission. Apart from these environmental benefits, the novel binder has shown enhanced resistance to high temperatures and chemical attacks. However, many studies have mentioned the risk of its considerably higher drying shrinkage and early-age cracking compared to OPC concrete. Despite the abundant research on the self-healing mechanism of OPC, limited work has been devoted to studying the self-healing behavior of AAS. Self-healing AAS is a revolutionary product that provides the solution for these drawbacks. This study is a critical review of the self-healing ability of AAS and its effect on the mechanical properties of AAS mortars. Several self-healing approaches, applications, and challenges of each mechanism are taken into account and compared regarding their impacts
An SRN-based model for quantitative evaluation of IoT quality attributes
Get PDFToday, the Internet of Things (IoT) is widely used in various fields, including health control, smart cities, intelligent buildings, and so on. One of the severe concerns in IoT systems is the issue of energy consumption and its management. IoT systems have limited energy resources, and in this regard, these limited resources must be managed appropriately. To design and build IoT systems, various aspects such as usable chips, types of communication protocols, timing of sending and receiving data, and so on, directly affect the system’s energy consumption. Therefore, it is necessary to model and evaluate the energy consumption of IoT systems before building and implementing the system. Using an appropriate model makes it possible to investigate and understand how much the system consumes energy and how it is in conformity with the system’s demands. This paper presents a stochastic reward net (SRN)-based model for modeling and quantitative evaluation of system energy consumption. To solve and evaluate the model, the proposed model is converted into an SRN model based on a series of automatic transformations. The proposed model is used in a case study to show how the model works and the results are given in the paper
