http://hdl.handle.net/10453/35199 2026-05-17T05:41:01Z http://hdl.handle.net/10453/195013 Title: Machine learning approach to predict 1-year mortality after heart transplantation: a single-centre study Authors: Allehyani, B; Savo, MT; Khwaji, A; Al Kholaif, N; Galzerano, D; Al Buraiki, J; Alamro, B; Al Sergani, H; Di Salvo, G; Cozac, DA; Pergola, V; Khaliel, F Abstract: Aims Heart transplantation is a critical life-saving procedure for patients with end-stage heart failure. However, predicting postoperative mortality remains challenging. The aim of this study is to examine the effectiveness of machine learning (ML) models for predicting 1-year mortality among heart transplant recipients in Saudi Arabia. Methods and results A retrospective observational study was conducted using data from King Faisal Specialist Hospital and Research Centre, a large tertiary hospital in Saudi Arabia, that included all heart transplant cases from January 2007 to December 2022. We evaluate and compare the accuracy of support vector machine (SVM) and logistic regression (LR) models in predicting 1-year mortality. We also identify key predictive variables influencing mortality rates among recipients. SVM and LR models were developed and compared using accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve as performance metrics. The study analysed data from 419 patients, revealing that ischaemia time, devices like left ventricle assist device, extracorporeal membrane oxygenation, and body mass index (BMI) were significant mortality predictors. The LR model showed a testing accuracy of 96.43 , with weight and BMI having the strongest influence on mortality prediction. The SVM model had a testing accuracy of 95.24 , demonstrating consistent performance across dataset. Conclusion The findings indicate that ML models, particularly SVM and LR, are effective in predicting 1-year mortality post-heart transplantation as well as identifying significant predictors of mortality. This research contributes to the global knowledge in heart transplant and highlights the importance of new technologies in tailoring healthcare strategies for the Saudi population. 2025-10-06T00:00:00Z http://hdl.handle.net/10453/195010 Title: Proof-of-work consensus by quantum sampling Authors: Singh, D; Muraleedharan, G; Fu, B; Cheng, C-M; Newton, NR; Rohde, PP; Brennen, GK Abstract: Since its advent in 2011, boson sampling has been a preferred candidate for demonstrating quantum advantage because of its simplicity and near-term requirements compared to other quantum algorithms. We propose to use a variant, called coarse-grained boson-sampling (CGBS), as a quantum proof-of-work (PoW) scheme for blockchain consensus. The miners perform boson sampling using input states that depend on the current block information and commit their samples to the network. Afterwards, CGBS strategies are determined which can be used to both validate samples and reward successful miners. By combining rewards for miners committing honest samples together with penalties for miners committing dishonest samples, a Nash equilibrium is found that incentivises honest miners. We provide numerical evidence that these validation tests are hard to spoof classically without knowing the binning scheme ahead of time and show the robustness of our protocol to small partial distinguishability of photons. The scheme works for both Fock state boson sampling and Gaussian boson sampling and provides dramatic speedup and energy savings relative to computation by classical hardware. 2025-04-01T00:00:00Z http://hdl.handle.net/10453/195009 Title: The Model of Consciousness: An Analysis of Causation, Self, Gödel’s Incompleteness, and Buddhist Philosophy Authors: Samarawickrama, M Abstract: This paper examines the limitations of analytical and computational methods in understanding reality, highlighting the secondary role of language and mathematics, which often leads to paradoxes. G del s incompleteness theorems underscore the inherent incompleteness and undecidability in logical and computational systems, such as the Turing machine. We propose that consciousness, operating as a non-material and chaotic finite-state machine (FSM) devoid of self-referencing, can achieve a complete and decidable understanding of reality. This contrasts with the self-referencing nature of logical systems that leads to paradoxes and limitations. Through a conceptual model of the mind inspired by Therav da Buddhist philosophy, we suggest that awareness of causation is free from self-referencing and coherent with the unpredictable yet causal and deterministic nature of reality. This alignment offers a pathway to a deeper and more comprehensive understanding of causation. The model illustrates the tight integrity between consciousness and causation, proposing that awareness of the present moment of causation can transcend the limitations of G del s incompleteness theorems. This awareness, free from analytical and computational constraints, preserves the integrity of conscious experience and provides a complete and decidable understanding of reality. Future research will focus on developing techniques to sustain this awareness, potentially leading to wisdom and deep insight into the fundamental nature of existence. 2025-06-01T00:00:00Z http://hdl.handle.net/10453/195008 Title: Consciousness and Mathematics: A Number Theoretic Approach to Modelling Reality Authors: Samarawickrama, M Abstract: This research analyses the fundamentals of numbers for interpreting consciousness and reality. Complementing G del s incompleteness theorems, we adopted number theory to explore non-referential and self-referential constructs. By examining consciousness, causation, and fundamental mathematical models of reality, we analysed stages of cognition and the emergence of self-referencing as a limitation, which brings incompleteness and undecidability to a framework. By postulating prime numbers as a non-referential fundamental basis, the study underscores their critical role in forming a complete and decidable framework for understanding consciousness and reality. We develop a framework that establishes composite numbers as secondary constructs dependent on prime numbers. Based on the foundation of prime numbers, we explored natural numbers, consisting of even and odd numbers. We analysed Goldbach conjecture and discussed the limitations of mathematical modelling based on our analysis of primes and natural numbers. Our analysis suggests that decomposing numerical systems into non-referential and self-referential components can transcend the limitations of modelling consciousness and reality. Our framework offers a profound foundation for modelling and interpreting consciousness and reality, bridging the gap between consciousness, causation, and mathematics. 2025-06-01T00:00:00Z