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A vocabulary of meaning of designed commercial artefacts based on naturally occurring language use
Get PDFThis study is a survey of the English-language words that are used when speaking about meaning with specific focus on the categories of function, ritual and myth. Such words can be used in interviews, questionnaires, measurement metrics and other forms of ethnography and testing. Understanding why consumers perceive designed artefacts to be personally relevant is a commercial imperative. Previous research has suggested that three categories of meaning are commonly encountered, i.e. function, ritual and myth. They cover a spectrum from the purely instrumental to the purely symbolic. However, despite the logical and philosophical groundwork, there has been little analysis of the actual words and phrases that are in everyday use by people when describing the meanings of designed artefacts. The objectives of the study described here were (1) to identify the words and phrases that are most frequently encountered in everyday language when discussing meaning, (2) to determine for each word or phrase its degree of belonging to the formal categories of function, ritual and myth and (3) to thematically group the words and phrases into macro-components of meaning. Three different analysis were performed. The first was based on the contents of major online dictionaries and thesauri, the second was based on the results from queries of the online lexical database WordNet and the third was based on a corpus analysis approach involving neural network word embedding algorithms. Thematic grouping of the database of extracted words and phrases suggested that in all three cases the macro-components of the concept of ‘function’, ‘ritual’ and ‘myth’ cover a spectrum that can be considered to be from an essential property (‘intention’, ‘ceremonial’ and ‘belief’) to an emergent property (‘action’, ‘spiritual’ and ‘symbolism’). The list of words, phrases and macro-components provides a first empirically established vocabulary of meaning for use in design activity
The extent that Term Extensions, (SPCs), are creating a barrier to access to pharmaceuticals
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis research critically assesses the development of Term Extensions, its impact and
practicalities on generic market entry and with a focus on the European
Supplementary Protection Certificates, the extent and effect on access to cheaper
medication in terms of cost and availability. From a positivist theoretical perspective,
using doctrinal research and secondary use of published data from the European
Union, (EU), Canada and International bodies, this research provides in-depth
illustration of the impact of term extensions on access.
Patent Term Extensions, PTEs, appear to be at the forefront of the EU battle-ground
between pharmaceutical originators and generic companies. Policy making for
pharmaceuticals requires considerations on availability and price. For developing
countries, it appears impossible to form policy without promoting generic medicines,
which is proving to be an effective health care remedy to access and availability.
A 20-year maximum patent term is generally recognised by originators to be
insufficient with claims that it is inadequate to provide incentives for research into new
active substances as the term is significantly reduced by delays surrounding securing
regulatory approval enabling a product to be placed on the market. The EU’s response
was the institution of SPCs which have been officially administered since the 1980’s
but numerous complaints have been made on its efficacy and fit for purpose.
The findings in this research suggests that term extensions provide significant barriers
to entry for generic medications, continued operations within the EU market will have
extended effects on cost and availability and most significantly, has acted as a legal
transplant on the Canadian system, which previously did not recognise extensions.
Additionally, this research demonstrates that the actual workings of the system
suggests that the cost implications represent one dimension of impact and that other
legal ramifications are at play that affect overall access matters, which are explored.
Conclusions are drawn based on legislative review and re-analysis/interpretation of
published data which facilitates suppositions on means of optimising existing legal
structures and dissects practical solutions for addressing access and public health
obligations under International Law, especially for countries with little or no
manufacturing capabilities
Hydrogen induced cracking in energy pipelines and its monitoring with acoustic emission
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThis research investigated the development of Hydrogen Introduced Cracks (HIC) on steel plates monitored with Acoustic Emission (AE) technology in detail. The work focuses on identifying and clustering HIC signals from mixed signals with different experimental setups, as well as describing HIC development in the light of signals’ characteristics.
Electrochemical hydrogen charging (ECHC) method was applied to generate HIC on A516 steel plates with mixed solution of 0.5mol/L H2SO4 and 0.5g/L NaAsO2. Four defect mechanisms were found to generate elastic waves during a test, which were H2 evolution, HIC, crevice corrosion and uniform corrosion. An experimental procedure was designed for pattern recognition of these mixed signals. Short-time tests with or without current were carried out using only 0.5mol/L H2SO4 solution for identifying non-HIC signals separately. The energy proportions in the frequency range of 0-100kHz (PE1) and 100-200kHz (PE2) in the energy spectrum as well as the parameters in the time domain of the Duration, the Energy and the Counts are the main characteristics used for pattern recognition. These characters were the basic parameters for signals identification, irrespective of the profile of the plate, the value of applied current, the locations of the sensors.
Due to the large amount of data, manual classification would be extensively time-consuming. In this study, a two-step Gaussian Mixed Model (GMM) clustering method was proposed for automatically clustering the mixed signals, in which the parameters of PE1 and PE2 obtained from the frequency domain were used for identifying signals from corrosions, and the Duration, the Counts and the Energy were then used to distinguish signals between H2 evolution and HIC. The effectiveness of this method was verified by experiments with different setup parameters.
For the two types of sensors (Nano30 and VS150-RSC) used in this study, it was found that more components of the signals acquired by VS150-RSC were concentrated at 150kHz compared with those by Nano30 sensors. Therefore, the accurate rate of the proposed automatic clustering method was lower due to the scattering frequency distribution among the critical signals. However, sensor VS150-RSC is more suitable for HIC monitoring in situations of a low signal-to-noise ratio or over a long distance between the event and the sensor. For the different thickness of the specimen (5mm, 10mm and 20mm), it was found that the HIC signals acquired by VS150-RSC had components between 160-190kHz when the thicknesses of specimens were 10mm or 20mm. For the test with a complex structural profile including a hole and a seam of the same size, it was found that the seam heavily influenced the frequency distribution of HIC signals while a hole only influenced the characteristics of signals in the time domain.
This research also investigated the source localisation of HIC events under different experimental setups, including the specifications of specimens and the type of sensors. The Simplex method was used to calculate the location based on the parallelogram sensor array. The onset time of each signal was determined by the Akaike Information Criterion (AIC) method to improve the accuracy. This method located HIC events well on simple plates with different setups but not on complex structures, such as a structure with holes, seams, welds, flanges and others. The delta-T mapping method was then investigated to localise HIC events on the complex plate with a hole and a seam. A FE model was also built by Abaqus to simulate the delta-T maps for each pair of sensors under this setup. Compared to the location results calculated by the Simplex method, the accuracy of localisation obtained by overlapping the simulated delta-T maps was greatly improved.National Structural Integrity Research Centre (NSIRC
Human evolutionary demography
Get PDFHuman evolutionary demography is an emerging field blending natural science with social science. This edited volume provides a much-needed, interdisciplinary introduction to the field and highlights cutting-edge research for interested readers and researchers in demography, the evolutionary behavioural sciences, biology, and related disciplines. By bridging the boundaries between social and biological sciences, the volume stresses the importance of a unified understanding of both in order to grasp past and current demographic patterns. Demographic traits, and traits related to demographic outcomes, including fertility and mortality rates, marriage, parental care, menopause, and cooperative behavior are subject to evolutionary processes. Bringing an understanding of evolution into demography therefore incorporates valuable insights into this field; just as knowledge of demography is key to understanding evolutionary processes. By asking questions about old patterns from a new perspective, the volume-composed of contributions from established and early-career academics-demonstrates that a combination of social science research and evolutionary theory offers holistic understandings and approaches that benefit both fields. Human Evolutionary Demography introduces an emerging field in an accessible style. It is suitable for graduate courses in demography, as well as upper-level undergraduates. Its range of research is sure to be of interest to academics working on demographic topics (anthropologists, sociologists, demographers), natural scientists working on evolutionary processes, and disciplines which cross-cut natural and social science, such as evolutionary psychology, human behavioral ecology, cultural evolution, and evolutionary medicine. As an accessible introduction, it should interest readers whether or not they are currently familiar with human evolutionary demography
Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks
Get PDFData Availability Statement: The data are private and the University Ethics Committee does not allow public access to the data.Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expressions—the traditional way to recognize something limited and is associated with errors. This study presents a new automatic method using electroencephalogram (EEG) signals based on combining graph theory with convolutional networks for emotion recognition. In the proposed model, firstly, a comprehensive database based on musical stimuli is provided to induce two and three emotional classes, including positive, negative, and neutral emotions. Generative adversarial networks (GANs) are used to supplement the recorded data, which are then input into the suggested deep network for feature extraction and classification. The suggested deep network can extract the dynamic information from the EEG data in an optimal manner and has 4 GConv layers. The accuracy of the categorization for two classes and three classes, respectively, is 99% and 98%, according to the suggested strategy. The suggested model has been compared with recent research and algorithms and has provided promising results. The proposed method can be used to complete the brain-computer-interface (BCI) systems puzzle.This research received no external funding
An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks
Get PDFData Availability Statement: The data are private and the University Ethics Committee does not allow public access to the data.In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. However, the polygraph instrument has limitations and needs to be more reliable. This study introduces a new model for detecting lies using electroencephalogram (EEG) signals. An EEG database of 20 study participants was created to accomplish this goal. This study also used a six-layer graph convolutional network and type 2 fuzzy (TF-2) sets for feature selection/extraction and automatic classification. The classification results show that the proposed deep model effectively distinguishes between truths and lies. As a result, even in a noisy environment (SNR = 0 dB), the classification accuracy remains above 90%. The proposed strategy outperforms current research and algorithms. Its superior performance makes it suitable for a wide range of practical applications.This research received no external funding
Investigating acoustic startle habituation and prepulse inhibition with silent functional MRI and electromyography in young, healthy adults
Get PDFData availability statement: The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://osf.io/j5vhp. The MRI data that support the findings of this study are available from LN upon reasonable request.Supplementary material: The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnhum.2024.1436156/full#supplementary-material .Introduction: Startle habituation and prepulse inhibition (PPI) are distinct measures of different sensory information processes, yet both result in the attenuation of the startle reflex. Identifying startle habituation and PPI neural mechanisms in humans has mostly evolved from acoustic-focused rodent models. Human functional magnetic resonance imaging (fMRI) studies have used tactile startle paradigms to avoid the confounding effects of gradient-related acoustic noise on auditory paradigms and blood-oxygen-level-dependent (BOLD) measures. This study aimed to examine the neurofunctional basis of acoustic startle habituation and PPI in humans with silent fMRI.
Methods: Using silent fMRI and simultaneous electromyography (EMG) to measure startle, the neural correlates of acoustic short-term startle habituation and PPI [stimulus onset asynchronies (SOA) of 60 ms and 120 ms] were investigated in 42 healthy adults (28 females). To derive stronger inferences about brain-behaviour correlations at the group-level, models included EMG-assessed measures of startle habituation (regression slope) or PPI (percentage) as a covariate. A linear temporal modulator was modelled at the individual-level to characterise functional changes in neural activity during startle habituation.
Results: Over time, participants showed a decrease in startle response (habituation), accompanied by decreasing thalamic, striatal, insula, and brainstem activity. Startle habituation was associated with the linear temporal modulation of BOLD response amplitude in several regions, with thalamus, insula, and parietal lobe activity decreasing over time, and frontal lobe, dorsal striatum, and posterior cingulate activity increasing over time. The paradigm yielded a small amount of PPI (9–13%). No significant neural activity for PPI was detected.
Discussion: Startle habituation was associated with the thalamus, putamen, insula, and brainstem, and with linear BOLD response modulation in thalamic, striatal, insula, parietal, frontal, and posterior cingulate regions. These findings provide insight into the mediation and functional basis of the acoustic primary startle circuit. Instead, whilst reduced compared to conventional MRI, scanner noise may have disrupted prepulse detection and processing, resulting in low PPI and impacting our ability to map its neural signatures. Our findings encourage optimisation of the MRI environment for acoustic PPI-based investigations in humans. Combining EMG and functional neuroimaging methods shows promise for mapping short-term startle habituation in healthy and clinical populations.The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. LN is funded by Biotechnology and Biological Sciences Research Council London Interdisciplinary Doctoral Program (BBSRC LIDo DTP)
Effect of Lattice Misfit on the Stability of the Misfit Layer Compound (SnS)1+xNbS2
Get PDFData Availability Statement: The original contributions presented in the study are included in the article/Supplementary Materials (https://www.mdpi.com/2073-4352/14/9/756#app1-crystals-14-00756), further inquiries can be directed to the corresponding author/s.The prototype misfit layer compound (SnS)1.17NbS2 consists alternatingly of a metallic triatomic NbS2 layer, in which Nb atoms are sandwiched by S atoms, and an insulating SnS double layer featuring a NaCl-type structure. Here we investigate the effect of lattice misfit on the stability and chemical bonding in the misfit layer compound using a first-principles density functional theory approach. The calculations show that for the (SnS)1+xNbS2 approximants, the most stable one has x = 0.167, close to the experimental observations. Charge analysis finds a moderate charge transfer from SnS to NbS2. Sn or S vacancies in the SnS part affect the electronic properties and interlayer interactions. The obtained information here helps in understanding the mechanism of formation and stability of misfit layer compounds and ferecrystals and further contributes to the design of novel multilayer compounds and emerging van der Waals heterostructures.EPSRC (UK) under grant number EP/S005102/1
Performance Evaluation of Deep Q Networks for Hybrid Reconfigurable Intelligent Surface in 6G Networks
Get PDFThe emergence of 6G wireless communication in-troduces a new era of connectivity demands, marked by high data rates and varying network conditions. To address these challenges, we propose HRISDQN, a framework that combines Hybrid Reconfigurable Intelligent Surfaces (HRIS) with Deep Q-Network (DQN)-based reinforcement learning. HRISDQN represents a significant advancement in optimising communication in 6G networks, enabling transformative improvements. In our work, we compare HRISDQN with conventional Semi Definite Relaxation (SDR), Maximum Ratio Transmission (MRT), and Minimum Mean Square Error (MMSE) as traditional beam-forming techniques. We demonstrate HRISDQN's adaptability to dynamic scenarios through extensive simulations and evaluations, including varying Signal-to-Noise Ratios (SNR) and changing user densities. Our results show that HRISDQN consistently outperforms its counterparts; HRISDQN's resource allocation capability ensures 40% better fairness, lower delay by 80%, and three times higher spectral efficiency, even in high-density user environments. The designed HRISDQN excels under diverse SNR conditions, providing robust and reliable connectivity. HRIS-DQN's exceptional performance holds great promise for the future of 6G communication. HRISDQN offers ultra-efficient, low-latency, and adaptive communication networks for augmented reality and autonomous vehicles using HRIS and DQN
‘It hooks them in, it’s straight in there’: leveraging game culture for learning in the Key Stage 2 science curriculum
Get PDFEngagement with game culture is an important component of young peoples’ lives yet little is known about the potential of drawing on this culture in the primary classroom science curriculum to improve engagement across diverse socio-economic cohorts. Therefore, the aim of the study was to understand the engagement potential of the distinct pedagogy of Checkpoint Magazine’s prepared lesson materials for Key Stage 2 Science, and to evaluate feedback on any perceived enhanced classroom learning that takes place. Using mixed methods, five teachers from four schools delivered a Key Stage 2 science lesson on classification. The teachers were interviewed, and we gathered questionnaire data from most of the children who participated in the lesson in each school. The findings were positive with both teachers and children reporting increased enjoyment and engagement with the learning process. The outcomes of this project have the potential to deliver more inclusive learning for young people.This work was supported by Brunel University London: Brunel Research Interdisciplinary Lab (BRIL) project: 'Natural’ participatory online interactions
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