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Institution

Coventry University

EducationCoventry, United Kingdom
About: Coventry University is a education organization based out in Coventry, United Kingdom. It is known for research contribution in the topics: Population & Higher education. The organization has 4964 authors who have published 12700 publications receiving 255898 citations. The organization is also known as: Lanchester Polytechnic & Coventry Polytechnic.


Papers
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Journal ArticleDOI
TL;DR: Results suggest that camouflaging autistic traits is associated with increased risk of experiencing thwarted belongingness and lifetime suicidality.
Abstract: The current study explored whether people who camouflage autistic traits are more likely to experience thwarted belongingness and suicidality, as predicted by the Interpersonal Psychological Theory of Suicide (IPTS). 160 undergraduate students (86.9% female, 18–23 years) completed a cross-sectional online survey from 8th February to 30th May 2019 including self-report measures of thwarted belongingness and perceived burdensomeness, autistic traits, depression, anxiety, camouflaging autistic traits, and lifetime suicidality. Results suggest that camouflaging autistic traits is associated with increased risk of experiencing thwarted belongingness and lifetime suicidality. It is important for suicide theories such as the IPTS to include variables relevant to the broader autism phenotype, to increase applicability of models to both autistic and non-autistic people.

103 citations

Journal ArticleDOI
TL;DR: A simple solution for thermal modeling of a house which includes experimental identification of the model's parameters is presented which will be used to simulate the thermal behavior of the house and to obtain solutions to reduce energy consumption.

103 citations

Journal ArticleDOI
TL;DR: An agent-based approach has been developed to facilitate the integration of process planning and scheduling simultaneously, and an optimization agent based on an evolutionary algorithm is used to manage the interactions and communications between agents to enable proper decisions to be made.
Abstract: Traditionally, process planning and scheduling were performed sequentially, where scheduling was done after process plans had been generated. Considering the fact that these two functions are usually complementary, it is necessary to integrate them more tightly so that the performance of a manufacturing system can be improved greatly. In this paper, an agent-based approach has been developed to facilitate the integration of these two functions. In the approach, the two functions are carried out simultaneously, and an optimization agent based on an evolutionary algorithm is used to manage the interactions and communications between agents to enable proper decisions to be made. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show the proposed approach has achieved significant improvement.

103 citations

Journal ArticleDOI
06 May 2020-Sensors
TL;DR: This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method and produces a dataset that contains patterns of radio wave signals obtained using software-defined radios to establish if a subject is standing up or sitting down as a test case.
Abstract: Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

103 citations

Journal ArticleDOI
TL;DR: In this article, a high-resolution, 5300-yr record of pollen and lithogenic elements (K, Ca, Ti, Rb, Sr, Y, Zr) from an ombrotrophic peat bog located in northwest Spain, reveals that the variations in the fluxes of lithogenic element supplied to the bog by atmospheric deposition were coupled to the evolution of the vegetation of the area.
Abstract: A high-resolution, 5300-yr record of pollen and lithogenic elements (K, Ca, Ti, Rb, Sr, Y, Zr) from an ombrotrophic peat bog located in northwest Spain, reveals that the variations in the fluxes of lithogenic elements supplied to the bog by atmospheric deposition were coupled to the evolution of the vegetation of the area. A strong negative correlation exists between the percentage of tree pollen and the concentration of lithogenic elements. For example, the correlation between total tree pollen and Sr concentrations is - 0.94. The main phases of decline of the deciduous forest occurred during known cultural periods (late Neolithic, the Metal Ages, the Roman Period, the Middle Ages and the Industrial period) suggesting a close link between human activities (fires and forest clearances), changes in the vegetation and soil erosion. The flux of lithogenic elements seems to have increased before a significant variation in pollen is detected, which may indicate that changes in soil erosion are reflected earlier than the changes in vegetation in the bog record. Variations in the composition of the deposited dust reflect impacts that occurred at different spatial scales, with local sources dominant in the late Neolithic, the Metal Ages and the Middle Ages, whilst regional sources are more important in the Roman period and the Industrial Revolution. During the prehistoric period, arboreal pollen percentages recovered to their former levels, suggesting that woodland regenerated following a disturbance phase, but for the last 1400 years no significant recovery took place until afforestation with pines was introduced 200 years ago. While this must be the result of continuous clearances to convert forest into arable land, a cumulative effect on soil degradation must also be implied. © 2005 Edward Arnold (Publishers) Ltd.

103 citations


Authors

Showing all 5097 results

NameH-indexPapersCitations
Xiang Zhang1541733117576
Zidong Wang12291450717
Stephen Joseph9548545357
Andrew Smith87102534127
John F. Allen7940123214
Craig E. Banks7756927520
Philip L. Smith7529124842
Tim H. Sparks6931519997
Nadine E. Foster6832018475
Michael G. Burton6651916736
Sarah E Lamb6539528825
Michael Gleeson6523417603
David Alexander6552016504
Timothy J. Mason6522515810
David S.G. Thomas6322814796
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202360
2022217
20211,419
20201,267
20191,097
20181,013