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Community level impact of solar entrepreneurs in rural Odisha, India: the rise of women led solar energy-based enterprises
No full textContemporary research in the area of renewable energy-based entrepreneurship has largely ignored studying the effects of women led solar businesses in a regional context, particularly rural areas. While there are studies recognising entrepreneurship as a key instrument in bringing in regional transformation and thereby development, very little insight has been provided to gain an understanding of solar entrepreneurship and its effect at regional levels. This research explores the community level impact of women led solar businesses by using an exploratory qualitative method and carrying out semi-structured interviews and participant observation on solar entrepreneurs in rural Odisha, India. The paper offers empirical analysis from discussions led by thematic analysis method that introduces the varied impact of women led solar entrepreneurship on rural Odisha and how that is evidently realised at various levels as well as time scales
The Aortic-Femoral Arterial Stiffness Gradient: An Atherosclerosis Risk in Communities (ARIC) Study
Get PDFBackground: The aortic to femoral arterial stiffness gradient (af-SG) may be a novel measure of arterial health and cardiovascular disease (CVD) risk, but its association with CVD risk factors and CVD status, and whether or not they differ from the referent measure, carotid-femoral pulse-wave velocity (cfPWV), is not known.Method: Accordingly, we compared the associations of the af-SG and cfPWV with (i) age and traditional CVD risk factors and (ii) CVD status. We evaluated 4183 older-aged (75.2 ± 5.0 years) men and women in the community-based Atherosclerosis Risk in Communities (ARIC) Study. cfPWV and femoral-ankle PWV (faPWV) were measured using an automated cardiovascular screening device. The af-SG was calculated as faPWV divided by cfPWV. Associations of af-SG and cfPWV with age, CVD risk factors (age, BMI, blood pressure, heart rate, glucose and blood lipid levels) and CVD status (hypertension, diabetes, coronary heart disease, heart failure, stroke) were determined using linear and logistic regression analyses.Results: (i) the af-SG and cfPWV demonstrated comparable associations with age and CVD risk factors, except BMI. (ii) a low af-SG was associated with diabetes, coronary heart disease, heart failure and stroke, whilst a high cfPWV was only associated with diabetes.Conclusion: Although future studies are necessary to confirm clinical utility, the af-SG is a promising tool that may provide a unique picture of hemodynamic integration and identification of CVD risk when compared with cfPWV
Discovering Sociology
No full textThis second edition of a major textbook uses lively prose and a series of carefully-crafted pedagogical features to both introduce sociology as a discipline and to help students realize how deeply sociological issues impact on their own lives. Over the book's 12 chapters, students discover what sociology is, alongside its historical development and emergent new concerns. They will be led through the theories that underpin the discipline and familiarized with what it takes to undertake good sociological research. Ultimately students will be led and inspired to develop their own sociological imagination – learning to question their own assumptions about the society, the culture and the world around them today.Historically, the majority of introductory sociology textbooks have run to many hundreds of pages, discouraging students from further reading. By contrast, Discovering Sociology has been carefully designed and developed as a true introduction, covering the key ideas and topics that first year undergraduate students need to engage with without sacrificing intellectual rigour.New to this Edition:- Two new chapters adding coverage on crime, deviance and political sociology- Updated examples, Vox Pops and case studies keep this new edition feeling fresh and contemporary and ensure diverse coverage, including from beyond Western sociology- Thoughtfully updated and refreshed layout and visual features.Accompanying online resources for this title can be found at bloomsburyonlineresources.com/discovering-sociology-2e. These resources are designed to support teaching and learning when using this textbook and are available at no extra cost
Out of the classroom:‘informal’ education and histories of education: History of Education Society presidential address, November 2019
No full textHistorians of education are well placed to engage in applied historical approaches providing authoritative evidence of the past to inform policy and practice. This article is based on the presidential keynote delivered at the History of Education Society (UK) annual conference in 2019. As such it reflects on possible future directions for the history of education and considers the role of informal educational activity that takes place outside the classroom, including that of children’s reading for pleasure. Stories in the interwar British, Canadian and Australian Girl’s and Boy’s annuals provide an example of how appropriate gender roles were presented to their young readers at a time of intense social and political change for Britain. With Brexit heralding a similar significant moment of change and the pandemic lockdowns resulting in children spending more time at home, it is concluded that the significance of ‘informal’ education, both historically and today, requires our attention.</p
Facilitating recall and particularisation of repeated events in adults using a multi-method interviewing format
Get PDFReports about repeated experiences tend to include more schematic information than information about specific instances. However, investigators in both forensic and intelligence settings typically seek specific over general information. We tested a multi-method interviewing format (MMIF) to facilitate recall and particularisation of repeated events through the use of the self-generated cues mnemonic, the timeline technique, and follow-up questions. Over separate sessions, 150 adult participants watched four scripted films depicting a series of meetings in which a terrorist group planned attacks and planted explosive devices. For half of our sample, the third witnessed event included two deviations (one new detail and one changed detail). A week later, participants provided their account using the MMIF, the timeline technique with self-generated cues, or a free recall format followed by open-ended questions. As expected, more information was reported overall in the MMIF condition compared to the other format conditions, for two types of details, correct details, and correct gist details. The reporting of internal intrusions was comparable across format conditions. Contrary to hypotheses, the presence of deviations did not benefit recall or source monitoring. Our findings have implications for information elicitation in applied settings and for future research on adults’ retrieval of repeated events
The recent contribution of scientific techniques to the study of Nokalakevi in Samegrelo, Georgia
Get PDFThe site of Nokalakevi, in western Georgia, has seen significant excavation since 1973, including, since 2001, a collaborative Anglo-Georgian project. However, the interpretation of the site has largely rested on architectural analysis of standing remains and the relative dating of deposits based on the study of ceramics. Since 2013, the Anglo-Georgian Expedition to Nokalakevi has collected a diverse dataset derived from multiple scientific techniques including optically stimulated luminescence (OSL) dating of ceramics, radiocarbon dating, δ13C and δ15N analysis and 87Sr/86Sr analysis. The full results of these analyses are reported here for the first time along with implications for the interpretation of the archaeology, which include greater detail in the site chronology but also indicators of diet and migration.</p
A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection
Get PDFFalls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection
Multi-modal Generative Adversarial Networks for Traffic Event Detection in Smart Cities
Get PDFAdvances in the Internet of Things have enabled the development of many smart city applications and expert systems that help citizens and authorities better understand the dynamics of the cities, and make better planning and utilisation of city resources. Smart cities are composed of complex systems that usually process and analyse big data from the Cyber, Physical, and Social worlds. Traffic event detection is an important and complex task in smart transportation modelling and management.We address this problem using semi-supervised deep learning with data of different modalities, e.g., physical sensor observations and social media data. Unlike most existing studies focusing on data of single modality, the proposed method makes use of data of multiple modalities that appear to complement and reinforce each other. Meanwhile, as the amount of labelled data in big data applications is usually extremely limited, we extend the multi-modal Generative Adversarial Network model to a semi-supervised architecture to characterise traffic events. We evaluate the model with a large, real world dataset consisting of traffic sensor observations and social media data collected from the San Francisco Bay Area over a period of four months. The evaluation results clearly demonstrate the advantages of the proposed model in extracting and classifying traffic events