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The ethics and politics of design for the common good: a lesson from Alibaug
Get PDFIn this chapter I argue for a socio-technical approach to technology design for the common good that addresses its ethical and political aspects. The background is that the life of marginalized people in contemporary society is challenging and uncertain. The marginalized can face health and cognitive issues as well as a lack of stability in social structures such as family, work and social inclusion. In this context, certain democratic values embedded in technology design can conceal political asymmetries and fail to deliver ethical value exchange, where value extraction is not dominated by one party but equally shared across all stakeholders. I discuss two socio-technical perspectives called human work interaction design (HWID) and Technological Frames (TF) to expose and tackle the challenges of designing technology for the common good. I introduce and evaluate an ongoing case of a digital service delivered through an app to support a fishing community in Alibaug, India. The evaluation of the socio-technical infrastructure surrounding this app is done in two parts: firstly, I use HWID to highlight inwardly and outwardly socio-technical, ethical and power relations between human work and interaction design; secondly, an argument for the use of the concept of TF to understand the constructionist and semiotic power dynamics of different groups in participatory technology design is presented. It is shown how dominant groups’ frames can construct meanings of design decisions in terms of whether they are appropriate or not. The political leverage of the scripts embedded in artefacts used in the process of design is also explained from a semiotic perspective. I conclude by highlighting the value of an ethical and political socio-technical framework for technology design for the common good with people at the margins
Aesthetics and dementia: exploring the role of everyday aesthetics in dementia care settings
Get PDFThis paper explores how everyday aesthetics shape and are shaped within dementia care settings. The authors draw upon research that explored the significance of clothing and textiles in care home settings, to identify the varied and complex aesthetic experiences of people with dementia. The study was carried out using a series of creative, sensory and embodied research methods working with people with dementia and care home staff. Findings demonstrate that aesthetics are important in care homes at a number of levels. People with dementia discussed personal aesthetic preferences and demonstrated such preferences through embodied practices. Attending to aesthetics facilitated moments of togetherness between people with dementia and care home staff, creating person-centred encounters outside task-orientated conversations. This paper supports the importance of everyday aesthetics within dementia care settings and demonstrates that greater attention should be paid to this, to reconsider and enhance not only the look and feel of care homes and everyday items, including clothing, but also dementia care practice more broadly
A novel framework for planning policy and responsible stakeholders in industrial wastewater reuse projects: a case study in Iran
Get PDFIndustrial wastewater recycling projects are mainly used for alleviation of both water scarcity and contamination of freshwater bodies. These projects mainly address major challenges related to technological, and economic aspects rather than stakeholders responsibility. Hence, little is known for the role of responsible stakeholders as a major part of planning policy, which requires recognition of their crucial role and integration into associated procedures. This paper presents a new decision support framework to identify responsible stakeholders and reveal the role of their motivations. The approach integrates qualitative and frequency analysis methods into a comprehensive framework to identify the problems over the project lifetime from visible to their roots and link them together with stakeholders through deep mapping. The planning policy framework is applied to a real-world case study of industrial parks in Iran. The results of the case study show that visible economic, social, and technological problems are caused by responsible stakeholders with no direct role in those projects. Additionally, deep mapping analysis shows various deep roots caused by the government and industry are linked to visible problems across all project phases that are related to the role of stakeholders, their behaviour, and deep beliefs
Dietary therapy to improve nutrition and gut health in paediatric Crohn’s disease; a feasibility study
Get PDFBovine colostrum (BC) has anti-inflammatory, anti-infective, growth and intestinal repair factors that may be beneficial in Crohn’s disease (CD). We assessed whether daily BC for up to 3 months was acceptable to children and young people (CYP) with CD in remission or of mild/moderate severity. CYP were randomised to receive either BC or matching placebo milk daily for 6 weeks (blinded phase); all received BC for the following 6 weeks (open phase). In 23 CYP, median (inter-quartile range) age was 15.2 (13.9–16.1) years and 9 (39.1%) were girls. A similar proportion of CYP in the BC and placebo arms completed the blinded phase (8/12, 75.0% and 9/11, 81.8% respectively). Twelve (70.6%) CYP completed the open phase with 7 (58.3%) tolerating BC for 3 months. Diaries in weeks 2, 6 and 12 revealed that most CYP took BC every day (5/7, 71.4%; 5/8, 62,5% and 6/11, 54.5% respectively). In interviews, opinions were divided as to preference of BC over the placebo milk and some preferred BC over other nutritional supplements. Symptoms, clinical and laboratory variables and quality of life were similar in the two arms. BC may be an acceptable nutritional supplement for daily, longer-term use in CYP with CD
Investigation of bioaccumulation and human health risk assessment of heavy metals in crayfish (procambarus clarkii) farming with a rice-crayfish-based coculture breeding modes
Get PDFDue to the rapid development of the crayfish (Procambarus clarkii) industry in Chinese catering, people are paying more attention to the bioaccumulation of heavy metals in crayfish. To evaluate the health risks associated with the consumption of crayfish, nine types of heavy metals in both crayfish and abdominal muscles of crayfish were investigated. Crayfish samples were collected from rice-crayfish-based coculture breeding modes from different areas located in the middle and lower reaches of the Yangtze River. The average concentrations of heavy metals in the whole crayfish were much higher than the abdominal muscle of crayfish. The estimated daily intake (EDI) of heavy metals in the abdomen of crayfish was calculated to assess the noncarcinogenic risk and the overall noncarcinogenic risk including the target hazard quotient (THQ), the hazard index (HI) and carcinogenic risk (CR). The results of the present study showed that the consumption of crayfish may not present an obvious health risk to humans associated with heavy metals. However, the THQ values of As in the abdominal muscles of crayfish for adults in EnShi (ES) and children in JiaYu (JY) should be of concern due to the higher contribution to the potential health risks of crayfish compared to other metals. Through X-ray photoelectron spectroscopy (XPS) detection of heavy metal As, it was found that As in the crayfish culture environment mainly exists in the form of As3+.Therefore, the quality and quantity of crayfish consumption should be moderated to prevent the bioaccumulation of As. The results indicate that crayfish cultured in different areas may have similar pollution levels and/or emissions from the same pollution sources
ECG-based real-time arrhythmia monitoring using quantized deep neural networks: a feasibility study
Get PDFContinuous ambulatory cardiac monitoring plays a critical role in early detection of abnormality in at-risk patients, thereby increasing the chance of early intervention. In this study, we present an automated ECG classification approach for distinguishing between healthy heartbeats and pathological rhythms. The proposed lightweight solution uses quantized one-dimensional deep convolutional neural networks and is ideal for real-time continuous monitoring of cardiac rhythm, capable of providing one output prediction per second. Raw ECG data is used as the input to the classifier, eliminating the need for complex data preprocessing on low-powered wearable devices. In contrast to many compute-intensive approaches, the data analysis can be carried out locally on edge devices, providing privacy and portability. The proposed lightweight solution is accurate (sensitivity of 98.5% and specificity of 99.8%), and implemented on a smartphone, it is energy-efficient and fast, requiring 5.85mJ and 7.65ms per prediction, respectively
An evaluation of the impact of databases on end‐of‐life embodied carbon estimation
Get PDFThe growing awareness of the need to minimise greenhouse gas (GHG) and mitigate climate change has resulted in a greater focus on the embodied carbon (EC) of construction material. One way to ensure the environmental impact of building activities is minimised to a reasonable level is the calculation of their EC. Whilst there are a few studies investigating the role of embodied carbon factor (ECF) databases on the accuracy of EC calculation from cradle to gate, very little is known about the impact of different databases on the end‐of‐life (EoL) EC calculation. Using ECFs derived from the UK Department for Business, Energy and Industrial Strategy (BEIS), the Royal Institute of Chartered Surveyors (RICS) default values and the Institution of Structural Engineers (IStructE) suggested percentages for different elements of a building’s lifecycle stages, this study presents the impact of different data sources on the calculation of EoL EC. The study revealed that a lack of EoL ECFs databases could result in a significant difference of about 61% and 141% in the calculation of EC
Teaching Here and There; Episode 10, with Tessa Rogowski and Mark Glynn
No full textThis is episode 10, featuring university technology and learning enhancement managers from Essex and Dublin. Tessa Rogowski is the Director of IT Customer Experience at the University of Essex and Mark Glynn is the Head of Teaching Enhancement Unit at Dublin City University.
In an absorbing and insightful discussion, Dom Pates, James Rutherford and Dr. Ivan Sikora, invited their guests to talk about their experiences, reflections and plans for the design and development of technologies and support for hybrid teaching at their respective institutions.
Our guests give a comprehensive and intriguing insight into how their respective institutions coped with a pivot to online learning and in particular how they supported the live multi-modal practice as known at DCU as Hyflex and at Essex as hybrid teaching. They said that the hybrid classroom is not the end of the classroom from an HE perspective, but it's perhaps an evolution of it. As it has been reinforced before in our podcasts, it's so much more than just the technology that enables hybrid to be effective. Both Mark and Tessa agreed how we really need to be listening more to the students' voice and hear what their experiences are, ultimately, this is all for their benefit and their learning.
We hope you enjoy listening to this latest episode
Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants
Get PDFAutomatic modulation recognition (AMR) is used in various domains—from general-purpose communication to many military applications—thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier
Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates
Get PDFWith the advent of technological advancements and the widespread Internet connectivity during the last couple of decades, social media platforms (such as Facebook,Twitter, andInstagram)haveconsumedalargeproportion of time in our daily lives. People tend to stay alive on their social media with recent updates,asithasbecometheprimarysourceofinteractionwithinsocial circles. Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities. Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression, anxiety, suicide commitment, and mental disorder, particularly in the young adults whohaveexcessively spent time on social media whichnecessitates a thorough psychological analysis of all these platforms. This study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status updates. In this paper, we start with depression detection in the first instance and then expand on analyzing six other psychotic issues (e.g., depression, anxiety, psychopathic deviate, hypochondria, unrealistic, andhypomania)commonlyfoundinadultsduetoextremeuseofsocialmedia networks. To classify the psychotic issues with the user’s mental state, we have employed different Machine Learning (ML) classifiers i.e., Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor(KNN).TheusedMLmodelsaretrainedandtestedbyusingdifferentcombinationsoffeaturesselectiontechniques.Toobservethemostsuitable classifiers for psychotic issue classification, a cost-benefit function (sometimes termed as ‘Suitability’) has been used which combines the accuracy of the model with its execution time. The experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set