Assessing the reliability and accuracy of wearable devices in monitoring recovery biomarkers in athletes with spinal cord injury

Context: Athletes with spinal cord injury (SCI) face unique challenges related to exercise-induced fatigue and recovery. Monitoring recovery in this population using wearable technologies in ecologically valid settings is underexplored. This study aims to assess the reliability and accuracy of wearable sensors for monitoring recovery in athletes with SCI, focusing on thermoregulation, energy expenditure, sweating, and mental recovery across different settings. Research Aim: This study aims to assess the reliability and accuracy of wearable sensors for monitoring recovery in athletes with spinal cord injury (SCI), focusing on thermo regulation, energy expenditure, sweating, and mental recovery across different settings. Methods: This study employs a cross-sectional design with eight eligible participants who will complete both anaerobic and aerobic submaximal exercise tests, with acute and chronic recovery monitored through a multi-sensor system. Recovery strategies and perturbations are introduced as test conditions to explore the sensitivity of wearable sensors in detecting physiological changes across recovery domains. The study has received ethical approval (IRB-2024-702) from the NTU Institutional Review Board (Research Integrity and Ethics Office, Singapore), and all participants will provide informed consent. Data collection: Continuous monitoring includes heart rate and variability, sweat biomarkers, respiratory gas exchange, hydration status, body composition, and psychological scales to provide a comprehensive assessment of recovery dynamics. Analysis procedure: Data analysis will evaluate the reliability, and validity, of the wearable sensor system for athletes with spinal cord injury. Concurrent validity will be assessed using correlation analyses and Bland–Altman plots, while test–retest reliability will be examined via intra class correlation coefficients (ICCs) and linear mixed-effects models. The sensitivity of the system to recovery-related physiological changes will be explored through within subject contrasts. Finally, relationships between subjective experiences and objective markers will be analyzed using regression and mixed models to assess alignment across settings. Conclusion: This protocol aims to establish the reliability, and accuracy of sensor set, and methodological framework necessary for future large-scale trials investigating optimized recovery strategies in athletes with SCI. This research contributes to the advancement of monitoring recovery kinetics in athletes with SCI, aiming to enhance sports compliance, personalize workload, prevent injuries, and validate non-invasive tools in real-world scenarios

Is interfaith dialogue in Southeast Asia losing its relevance? A call for renewal

As society changes digitally, generationally, and demographically, older interreligious dialogue models struggle to be effective, and a renewal is arguably needed.Published versio

A generalized graph signal processing framework for multiple hypothesis testing over networks

We consider the multiple hypothesis testing (MHT) problem over the joint domain formed by a graph and a measure space. On each sample point of this joint domain, we assign a hypothesis test and a corresponding p-value. The goal is to make decisions for all hypotheses simultaneously, using all available p-values. In practice, this problem resembles the detection problem over a sensor network during a period of time. To solve this problem, we extend the traditional two-groups model such that the prior probability of the null hypothesis and the alternative distribution of p-values can be inhomogeneous over the joint domain. We model the inhomogeneity via a generalized graph signal. This more flexible statistical model yields a more powerful detection strategy by leveraging the information from the joint domain.National Research Foundation (NRF)Info-communications Media Development Authority (IMDA)Submitted/Accepted versionThis research/project is supported by the National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research & Development Programme

Biosecurity in the age of AI: risks and opportunities

Biosecurity has become more complex with the emergence of artificial intelligence-powered biotechnologies. The biotechnology-AI nexus can potentially strengthen biosecurity but amplify biological risks if misused. There is an urgent need for integrated governance frameworks to manage the dual-use nature of AI-powered biotechnology tools and regional cooperation through ASEAN to future-proof biosecurity governance in Southeast Asia.Published versio

Can Trump safeguard the US dollar?

President Trump has threatened to impose tariffs on countries that try to move away from the US dollar. Why he wants to protect the US dollar hegemony is quite clear – it gives America an “exorbitant privilege”. Whether he will succeed is less clear. Quite possibly, Trump’s efforts will end up dethroning the US dollar.Published versio

The augmented human - visual movement magnification

Humans rely heavily on their vision, yet there are limitations to what the eye can perceive. While technologies like the Bionic Eye and Microsoft’s HoloLens 2 augment human vision, detecting subtle movements such as micro-expressions, breathing, or pulse remains challenging to this current day. Two advanced motion magnification techniques are presented through a web application to allow users to amplify subtle motions in video footages or live streaming: FlowMagnification and Axial-Motion Magnification. Subtle motions focused include pulses, breathing and facial expressions. Adopting a Human-Computer Interaction (HCI) approach, this project showcases the development of development, design and evaluation of a web application that allows users to upload videos or connect via live streams to apply these magnification techniques. With React as frontend and Django as backend, the application offers a user-friendly user interface to interact and manipulate visual content. To ensure application’s goals are met, low and hide-fidelity prototypes are created, showcasing the progress via drafts. Feedback is gathered through online surveys among selected group of target audience. The goal is to provide an accessible application that allows an optimal experience in exploration of basic motion magnification or for health purposes, contributing to fields such as healthcare monitoring or surveillance.Bachelor's degre

Cleavage and reassembly of 1,3-dicarbonyls with enaminones to synthesize highly functionalized naphthols

The cleavage of carbon-carbon bonds and their subsequent reassembly into highly functionalized and useful molecules in an atom-efficient manner has always been a central focus in the realm of organic synthesis. In this report, we describe the construction of highly functionalized naphthol esters via a tandem reassembly process, driven by Ullmann-type coupling of enaminones and 1,3-dicarbonyl compounds. Mechanistic investigations suggest the involvement of C(sp2)-C(sp3) coupling, cyclization, two acyl migrations, aromatization, and additional transformations within this tandem sequence. This methodology offers several notable advantages, such as the use of inexpensive and easily accessible starting materials, the elimination of the need for expensive transition metal catalysis, simple operation in the atmosphere, exceptional compatibility with a wide range of substrates, and ease of conversion into drug scaffolds.Agency for Science, Technology and Research (A*STAR)Nanyang Technological UniversitySubmitted/Accepted versionWe gratefully acknowledge the financial support from the Natural Science Basic Research Plan of Shaanxi Province (2023-JC-YB-109), the Fundamental Research Funds for the Central Universities (D5000210701), and the Distinguished University Professor grant (Nanyang Technological University) and the Agency for Science, Technology, and Research (A*STAR) under its MTC Individual Research Grant (M21K2c0114) and RIE2025 MTC Programmatic Fund (M22K9b0049) for T.-P. L

A recommender system for employee recruitment

With the rapid and constant influx of data in today’s increasing digital world, job seeking has also evolved where applicants can apply for numerous positions with just a few clicks of a button within a few minutes. Coupled with an increasingly mobile world, this has significantly increased competition for jobs, with applicants eyeing both local and international roles. Consequently, Human Resources face an immense challenge in efficiently filtering and evaluating the overwhelming number of resumes they receive. Furthermore, Human Resources may lack the necessary domain knowledge to accurately assess an applicant’s qualifications in highly specialised fields. To address these challenges, this project explores the development of a recommender system for employee recruitment, focusing on job title prediction based on the resume. Natural Language Processing techniques and Machine Learning models were used on an online dataset to classify resumes into their relevant roles. Models such as Random Forest, Logistic Regression, Support Vector Classifier and k-Nearest Neighbours were implemented and evaluated. Hyperparameter tuning, feature selection and varying dataset size were also done to assess their impact on the model accuracy. This project demonstrated that Machine Learning models can be an effective approach for job classification across a range of roles. However, incorporating additional factors could further enhance the comprehensiveness of the model’s assessment.Bachelor's degre

Fully printable perovskite devices

Halide perovskites are an emerging class of semiconductors materials. They possess excellent optoelectronic properties, have high defect tolerance, long carrier diffusion lengths and large absorption coefficients. Halide perovskites also support a wide range of switching physics, which makes them suitable for application in neuromorphic systems, in particular memristors. Additive Manufacturing (AM) is being researched as an improvement over conventional manufacturing in ways such as increased customisability, little to no post-processing and reduction of material waste. Printed electronics is a product market that benefits from AM and has been forecasted to continue growing over the next decade. Of the current AM technologies being used in the field of printable electronics, Direct Ink Writing (DIW) has not been actively explored, and even less so for halide perovskite electronics. In this project, we aim to leverage the extrusion technique of DIW and its ability to work with a wide range of ink viscosities to pattern halide perovskite memristors. We will approach this project with solvent engineering and printing parameter optimisation to print the perovskite memristors, characterise the morphology of the prints and memristive behaviour of the prints via electrical measurements. Preliminary experiments yielded prints with discrete perovskite islands and, in general, a very sparse microstructure. An additional approach of substrate engineering was required, to be used in conjunction with solvent engineering and print parameter optimisation to improve the compactness of the perovskite prints. Substrate engineering involved the use of a mesoporous Titanium Dioxide (mTiO2) template. After characterising the microstructure of the perovskite prints on the mTiO2, the compactness of the perovskite grains were observed to have improved. Electrical analysis was then conducted on the perovskite devices. Memristive behaviour was indicated via the hysteresis loops measured. This indicated the effectiveness of substrate engineering. The morphology of the fully printed devices was characterised and observed to be similar to those of the devices that included the spin-coating step for the mTiO2. Electrical analysis on the fully printed devices also indicated memristive behaviour, proving the functionality of fully printed perovskite devices.Bachelor's degre

Integrating evolutionary algorithms with large language models for enhanced problem solving

Evolutionary computation (EC) is a computational method motivated by biological evolution and has been widely recognized for its effectiveness in solving complex and computationally expensive optimization problems. Recent advancements in Large Language Models (LLMs) have empowered Automatic Heuristic Design (AHD), a field dedicated to the automated creation of effective heuristics for a given problem, to process natural language descriptions and code, enabling generations of diverse algorithms through evolutionary operations. However, previously proposed frameworks of AHD could easily suffer from premature convergence due to their inability to capture the contextual information of the iterative optimization process and to diversify the population. Furthermore, only partial information about the thinking process of LLM could be captured despite its automatic operations of generating codes. Devised to handle those gaps, this is the first study that applies Parrondo's paradox-inspired evolutionary computation in AHD, unveiling the potential avenue for the application of Parrondo's paradox in the engineering field. This study introduces a novel LLM-empowered AHD designed to extend the search capability by commanding rational inference and random operations. This was achieved by the introduction of the essence of Parrondo's paradox, a superficially paradoxical phenomenon that the synthesis of two losing games in a certain manner can yield a winning outcome. PPLLM outperformed the earlier LLM-empowered AHDs: those empirical results underscore the efficacy of incorporating both rational and random search mechanisms to maintain solution diversity and escape premature convergence in AHD.Bachelor's degre