Finding Black Holes with Black Boxes -- Using Machine Learning to Identify Globular Clusters with Black Hole Subsystems

Machine learning is a powerful technique, becoming increasingly popular in astrophysics. In this paper, we apply machine learning to more than a thousand globular cluster (GC) models simulated as part of the 'MOCCA-Survey Database I' project in order to correlate present-day observable properties with the presence of a subsystem of stellar mass black holes (BHs). The machine learning model is then applied to available observed parameters for Galactic GCs to identify which of them that are most likely to be hosting a sizeable number of BHs and reveal insights into what properties lead to the formation of BH subsystems. With our machine learning model, we were able to shortlist 21 Galactic GCs that are most likely to contain a BH subsystem. We show that the clusters shortlisted by the machine learning classifier include those in which BH candidates have been observed (M22, M10 and NGC 3201) and that our results line up well with independent simulations and previous studies that manually compared simulated GC models with observed properties of Galactic GCs. These results can be useful for observers searching for elusive stellar mass BH candidates in GCs and further our understanding of the role BHs play in GC evolution. In addition, we have released an online tool that allows one to get predictions from our model after they input observable properties.Comment: 20 pages, 9 figures, 7 tables. Accepted for publication in MNRAS. Source code available at https://github.com/ammaraskar/black-holes-black-boxe

A Hierarchical Framework for Interpretable, Safe, and Specialised Deep Reinforcement Learning

Safety-critical systems, which are crucial for human safety and the environment, are difficult to control and operate. Traditional controllers need precise models of these complex systems, which is hard to develop. \acrfull{drl} offers a potential solution by learning from interactions rather than detailed models, but it faces limitations such as non-transparent decision-making and an expensive, unsafe learning process. Additionally, a key challenge in DRL is ensuring effective decision-making in rare situations. This thesis proposes a novel approach called the \acrfull{prop_frame} that enables safe and reliable control of critical systems. SRLA combines probabilistic modelling with reinforcement learning to create an interpretable system that can focus on the filtered state space. SRLA is activated in specific situations identified autonomously through the combination of probabilistic modelling and DRL, such as when the system is in an abnormal state or performing a sub-task. It uses policy cloning to initialise a baseline policy, which minimises the need for expensive exploration. Additionally, SRLA works alongside conventional control strategies to ensure safe and reliable decision-making. SRLA\u27s effectiveness is demonstrated through diverse safety-critical industrial case studies. It outperforms other methods in the predictive maintenance of turbofan engines by accurately predicting failures and identifying health states and root causes. In process control, SRLA autonomously synchronizes with conventional controllers and activates in critical situations. As a control room decision support for human operators, SRLA provides real-time suggestions to help operators avoid failures and can predict human errors using process data and human-computer interaction logs. In the case of industrial robotics, SRLA enables robots to learn complex tasks by breaking them into specialised sub-tasks in simulation and safely transferring the policies to the real world, outperforming traditional DRL

Motion Recording for Surveillance Camera

In this paper we present an operational computer vision system for real-time motion detection and recording that can be used in surveillance system. The system captures a video of a scene and identifies the frames that contains motion and record them in such a way that only the frames that is important to us is recorded and a report is made in the form of a movie is made and can be displayed. All parts that are captured by the camera are recorded to compare both movies. This serves as both a proof-of- concept and a verification of other existing algorithms for motion detection. Motion frames are detected using frame differencing. The results of the experiments with the system indicate the ability to minimize some of the problems false detection and missed detections (like in a sudden change of light in the scene). The software part is written in Matlab language as an M-file and using the Simulink library, the hardware part we used a Pentium 4 computer with a web camera or a laptop integrated camera

Single-case Experimental Research: Designing emotions by designing spaces - A pilot study

The belief that the environment shapes human emotions followed by behaviour is not new, as acknowledged by many researchers. Recent studies show that the most significant illness by 2030 is depression, as most of our time spent inside the buildings. Hence, the importance of "re-connecting architecture with emotions" is an essential solution to improve the quality of life. A single-case experimental design (SCED) aimed to investigate the relationship between neural underpinnings of the brain, for a single participant and various environments. Data collected was based on the Electroencephalography tests. Findings showed a significant contrast between different water elements and environmental settings, each with its unique effect on participant emotions as well as the electrical activity of the brain.Keywords: Depression; Neural underpinnings; Water-bodies environment; Quality of Life.eISSN: 2398-4287 © 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v5i13.210

Blue-Space Restoration Theory extends the Understanding of the Quranic Verses of Water

Through all times, scholars interpreted the Quranic verses of water by emphasising on the essential role of water upon the physical formation of plants. This study aimed to extend this interpretation from a behavioural science background. Mix methods were used, systematic literature review and integrative analysis. With the blue-space theory, It concluded that water does not have a role in building the physical-form only. But, also the spiritual-creation. God mentioned in the Quran that water brings everything alive; it seemed that it was not limited to the physical form of a human, as mentioned by scholars. But also the spiritual form.  Keywords: Blue-space; Water; Quran; Quality Of Life. eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5i14.219

Speed controller design for three-phase induction motor based on dynamic adjustment grasshopper optimization algorithm

Three-phase induction motor (TIM) is widely used in industrial application like paper mills, water treatment and sewage plants in the urban area. In these applications, the speed of TIM is very important that should be not varying with applied load torque. In this study, direct on line (DOL) motor starting without controller is modelled to evaluate the motor response when connected directly to main supply. Conventional PI controller for stator direct current and stator quadrature current of induction motor are designed as an inner loop controller as well as a second conventional PI controller is designed in the outer loop for controlling the TIM speed. Proposed combined PI-lead (CPIL) controllers for inner and outer loops are designed to improve the overall performance of the TIM as compared with the conventional controller. In this paper, dynamic adjustment grasshopper optimization algorithm (DAGOA) is proposed for tuning the proposed controller of the system. Numerical results based on well-selected test function demonstrate that DAGOA has a better performance in terms of speed of convergence, solution accuracy and reliability than SGOA. The study results revealed that the currents and speed of TIM system using CPIL-DAGOA are faster than system using conventional PI and CPIL controllers tuned by SGOA. Moreover, the speed controller of TIM system with CPIL controlling scheme based on DAGOA reached the steady state faster than others when applied load torque