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Introduction: Must We Burn Masud Khan?
No full textFree access to the published article is kindly provided by Edinburgh University Press online at: https://www.euppublishing.com/doi/abs/10.3366/pah.2024.0506 .Following the publication of a first instalment of the 39 Work Books of M. Masud R. Khan in November 2022 and the recent donation to the Freud Museum London of the full original correspondence between Khan and Wladimir Granoff, and between Khan and Victor Smirnoff, this essay serves the dual purpose of ensuring that Khan’s memory is being kept alive and informing its readership of the newly available archival documents. It briefly retraces the history of Khan’s conflicts with the British Psychoanalytical Society up to the destruction, in July 2019, of the Khan archives that were in the possession of the International Psychoanalytical Association. In addition, the author recounts how he came into possession of the letters Khan exchanged with Granoff and Smirnoff, and why it was decided to establish a Khan archive at the Freud Museum London. The prospect of this new archive being supplemented with the letters from Khan to his second wife, Svetlana Beriosova, and a full copy of the Work Books is also discussed
Sentiments Organize Affect Concepts in Yasawa, Fiji: a Cultural Domain Analysis
Get PDFFor decades, intensive research on emotion has advanced general theories of culture and cognition. Yet few theories can comfortably accommodate both the regularities and variation empirically manifest in affective phenomena around the world. One recent theoretical model (Gervais & Fessler, 2017) aims to do so. The Attitude-Scenario-Emotion (ASE) model of sentiments specifies an evolved psychological architecture that potentiates regular variation in affective experience and behavior in lived interaction with social, ecological and normative contexts. This model holds that sentiments – functional networks of bookkeeping attitudes and commitment emotions – produce context-dependent universals in salient social-relational experiences, predictably patterning affect concepts. The present research aims to empirically evaluate implications of the ASE model of sentiments using quantitative data from 10 months of fieldwork in Indigenous iTaukei villages on Yasawa Island, Fiji. Study 1 is a series of structured interviews that aim to elicit the full breadth of the Yasawan affect lexicon. In freelists and sentence frames, Yasawans use distinct sets of terms to refer to “feelings about” particular people (attitudes), and “feelings because of” particular events (emotions). Study 2 uses a pile sort task to show that the salient features of Yasawan affective experience are social-relational dimensions of communion and power, while both HCA and MDS reveal distinct social attitudes – “love” (lomani) and “like’ (taleitaki), “respect” (dokai), “contempt” (beci), “hate” (sevaki), and “fear” (rerevaki) – anchoring the conceptual organization of Yasawan emotions. Study 3 uses hypothetical vignettes with a between-subjects attitude manipulation and Likert-style emotion ratings to show that these attitudes differentially moderate emotions across social scenarios; differences are both quantitative and qualitative; each attitude is emotionally pluripotent; and divergent attitudes (e.g., “love” and “hate”) produce the same emotions in starkly different situations – a predicted three-way interaction of attitude x scenario x emotion. These data are broadly consistent with ASE hypotheses; population variation in affective worlds may follow from differential engagement of universal attitude-emotion networks (sentiments) experienced across social, ecological and normative contexts.This research was funded by NSF DDIG #1061496 to MG and Daniel M. T. Fessler. This publication was made possible through the support of a grant from the John Templeton Foundation
A joint diffusion/collision model for crystal growth in pure liquid metals
Get PDFData availability:
The datasets generated in this study have been deposited in the Brunel University London database, Figshare [https://doi.org/10.17633/rd.brunel.26029045.v1]71. Additional raw data can be found in Source Data file. Source data are provided with this paper.Code availability:
The code used in the current study has been deposited in Code Ocean [https://doi.org/10.24433/CO.8127284.v1]72.Supplementary information is available online at: https://www.nature.com/articles/s41467-024-50182-7#Sec14 .Source data are available online at: https://www.nature.com/articles/s41467-024-50182-7#Sec15 .The kinetics of atomic attachments at the liquid/solid interface is one of the foundations of solidification theory, and to date one of the long-standing questions remains: whether or not the growth is thermal activated in pure liquid metals. Using molecular dynamics simulations and machine learning, I have demonstrated that a considerable fraction of liquid atoms at the interfaces of Al(111), (110) and (100) needs thermal activation for growth to take place while the others attach to the crystal without an energy barrier. My joint diffusion/collision model is proved to be robust in predicting the general growth behaviour of pure metals. Here, I show this model is able to quantitatively describe the temperature dependence of growth kinetics and to properly interpret some important experimental observations, and it significantly advances our understanding of solidification theory and also is useful for modelling solidification, phase change materials and lithium dendrite growth in lithium-ion battery.The EPSRC was gratefully acknowledged for providing financial support under Grant No. EP/N007638/1 and EP/S036296/1. We also were grateful to the UK Materials and Molecular Modelling Hub for computational resources, which is partially funded by EPSRC (Grant Nos. EP/P020194/1 and EP/T022213/1), and maintained with support from Brunel University London
TaneNet: Two-Level Attention Network Based on Emojis for Sentiment Analysis
Get PDFDuring online communication, users often use irregular and ambiguous words, and sometimes use irony to express sarcasm. These words are difficult to analyze through text analysis, which poses a significant challenge for text sentiment analysis. As a novel communication method, emojis have a significant correlation with user emotions. In this paper, we use emojis to analyze the sentiment of short texts. Firstly, we validate that user information can help reduce the uncertainty of some emojis and use this information to identify the polarity of emojis. Then, we generate emoji representations by merging positional information, semantic information, emotional information, and frequency of appearance. Furthermore, we propose TaneNet, a two-level attention network based on emojis, which combines clause vectors and emoji representations to study the impact of emojis on the emotions of each clause in the text. Empirical results on two real-world datasets demonstrate that TaneNet outperforms existing state-of-the-art methodsNational Key Research and Development Program of China (Grant Number: 2022YFB4501704);
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62302308, U2142206, 62372300 and 61702333);
Shanghai Sailing Program (Grant Number: 21YF1432900
Research On Discrimination Method Of Carbon Deposit Degree Of Automobile Engine Based On Deep Learning
Get PDFThe detection of carbon deposit degree is of great significance to the maintenance of automobile engine. Due to issues with poor feature aggregation, inter-class similarity, and intra-class variance in carbon deposit data with a small number of samples, model-based discriminative approaches cannot be widely implemented in the market. In order to overcome this technical barrier, the article examines the impact of DCNNs (Deep Convolutional Neural Networks) level on the recognition effect of the degree of carbon deposit, introduces a dropout structure and data enhancement strategy to lower the risk of overfitting brought on by the small dataset, and suggests a recognition method based on the kernel of dual-dimensional multiscale-multifrequency information features to enhance the differentiation characteristic. After experimental testing, the accuracy of this method is 86.9 %, the F1-score is 87.2 %, and the inference speed is 190 FPS, which can meet the practical requirements and provide basic support for the large-scale promotion of the model discrimination.This work was supported by the Shanxi Province Key Research and Development Program Projects (No. 202302020101008) and the Research Project Supported by Shanxi Scholarship Council of China (No. 2022-145) and the Graduate Science and Technology Project Supported by North University of China (No. 2022180506)
The evolving jurisdiction of the Shura council in the Kingdom of Saudi Arabia
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Kingdom of Saudi Arabia prides itself as amongst the top twenty economies in the world. At a regional level, Saudi’s economy is undoubtedly the largest. However, the economy solely relies on oil production and the oil industry, a matter that increases the economy’s vulnerability as a result of severe fluctuations in oil and gas markets.
In this context, the Saudi’s 2030 vision has been formulated to reduce the nation's dependence on oil and to expand the base of economic resources. The transformation of a developing country into a prosperous, capitalist system comparable to the developed nations would neither be possible nor beneficial without an efficient organization through a legislative body that functions as part of the distribution of political powers. Namely, this body is the Shura Council.
In this respect, this study aims to evaluate the role of the Shura Council in Saudi Arabia's transition into a developed country. The thesis attempts to address the following primary question, and some other relevant questions: How does the evolution of the Shura Council and its involvement in the decision-making process mirror the political system in Saudi Arabia and its evolution?
The study argues that the Shura Council can indeed discuss and provide recommendations pertaining to regulations, agreements and treaties. It is to be noted that the Shura Council studies and interprets the laws, development plans and the annual reports of ministries and institutions of the public sector. It also proposes and amends laws. The study states that in the current structure of the Shura Council, it would hardly be possible for it to deliver its objectives and meet people’s expectations. As long as the Shura Council are appointed members, the political, social and economic reforms that Saudi nationals are eagerly seeking will take time to be fully realised . The study recommends adoption of certain constitutional reforms that have the potential to lead to significant improvements in the performance and impact of the Shura Council
Drone Safety and Security Surveillance System (D4S)
Get PDFData Availability Statement: The data presented in the study are available on request from the corresponding author due to privacy.Drones offer significant safety and security advantages by enhancing situational awareness across various fields. However, realizing these benefits hinges on well-designed drone systems. This study builds upon previous research on drone deployment challenges and proposes the Drone Safety and Security Surveillance System (D4S). D4S aims to standardize similar drone-based systems, enhancing situational awareness and supporting decision-making processes. While initially tailored for safety and security, D4S holds potential for broader applications. Two system architectures have been proposed and evaluated with positive feedback from safety and security professionals. D4S has the potential to revolutionize safety practices, improve situational awareness, and facilitate timely decision making in critical scenarios.This research received no external funding
Accurate COVID-19 detection using full blood count data and machine learning
Get PDFData availability statement: The raw dataset generated or analyzed during this study is not publicly available due to them containing information that could compromise patient privacy. The models generated by this dataset are available at request.COVID-19 has spread rapidly worldwide in the past three years, triggering partial and full lockdowns globally. The successful control of the COVID-19 pandemic on a global scale depended heavily upon the accurate detection of COVID-19. However, the main diagnostic tests for COVID-19 have some significant limitations, e.g. the major nucleic acid (RT-PCR) tests while having a high sensitivity are time-consuming and require expensive equipment with the shortage of test kits in many countries. Antigen lateral flow tests have a lower sensitivity and they cannot be used during the early pandemic as well as usually more expensive than the full or complete blood count test used in this paper which can be potentially performed using a finger blood sample. The last decade has seen rapid growth of AI, particularly deep learning, which has found wide applications in medical image analysis, with results comparable to and even surpassing human expert performance. There have been several machine learning models reported for COVID-19 diagnostics or prognosis predictions, most of them based on CT and X-ray images. In this paper we have applied traditional machine learning and convolutional neural networks (CNNs) based deep learning to the blood test data obtained from hematology analyzers and demonstrated that the AI models can be used to detect COVID-19 with a high degree of accuracy (>97%). The performance of different classifiers will be compared and discussed. The work should have potential applications in current COVID-19 and future pandemics.This work was partly funded by Brunel University London
Research on the mechanical and thermal properties of potting adhesive with different fillers of h-BN and MPCM
Get PDFData availability: Data will be made available on request.Using packaging materials to reduce contact thermal resistance has become a promising method to solve the problem of insufficient heat dissipation capacity of electronic components. The purpose of this work is to optimize the mechanical and thermodynamic performance of potting adhesive using phase change microcapsules (MPCM) and hexagonal boron nitride nano-powder (h-BN) as thermal conductive fillers. The experimental results indicated that h-BN has a positive effect on the tensile strength of the potting adhesive, with a 7.1% increase in tensile strength at a mass fraction of 30%. However, the addition of MPCM will weaken the tensile strength of the potting adhesive. Adding MPCM and h-BN can both effectively improve the thermal conductivity of the potting adhesive: when the filler mass fraction is lower than 20%, the potting adhesive with MPCM filler exhibits more strengthening capability than h-BN type; while with the continuous increase of filler mass fraction, the thermal conductivity of the potting adhesive with h-BN filler is better. The thermal buffering capacity of the potting adhesive significantly increases with the mass fraction of MPCM, while the effect of h-BN on thermal buffering capacity is not significant. In addition, the addition of h-BN and MPCM significantly improves the temperature uniformity of the potting adhesive.National Natural Science Foundation of China (Grant No. 52306167), Six talent peaks project of Jiangsu Province (Grant No. 2015-ZBZZ-035)
The effects of physical and transition climate risk on stock markets: Some multi-Country evidence
Get PDFJEL classification: C33, G12, G18.This paper examines the impact of transition and physical climate risk on stock markets using, for the first time in this context, the annual Climate Change Performance Index (CCPI) calculated by Germanwatch as well as its components (in addition to a wide range of other indices) for 48 countries from 2007 to 2023. Specifically, a balanced panel VAR model is estimated to obtain impulse responses for the whole set of countries considered as well as for a subset including the EU-28 only; other methods such as Forecast Error Variance Decomposition and Local Projections (Jordà, 2005; 2023) are then applied for robustness checks. The results suggest a positive impact of transition risk on stock returns and a negative one of physical risk, especially in the short term. Further, while physical risk appears to have an immediate impact, transition risk is shown to affect stock markets also over a longer time horizon. Finally, national climate policies seem to be more effective when implemented within a supranational framework as in the case of the EU-28
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