2,455 research outputs found
Discovery of selective saccharide receptors via Dynamic Combinatorial Chemistry
The diagnosis of various diseases and pathological conditions can be accomplished by screening and detecting glycans in cells. Certain glycans serve as excellent biomarkers, being related to cell malfunctioning, while other structurally similar glycans perform completely different functions and are naturally present in healthy cells. Despite the theoretical feasibility of using glycans as biomarkers for early disease detection, our current inability to discriminate between them limits their use.
One promising approach to distinguishing between glycans is targeting their dissimilarities in saccharide chains. However, designing selective receptors for saccharides is challenging due to the complexity of these molecules. Their vast diversity, the fact that they exist in many interconvertible forms, their lack of recognisable functional groups, or the fact that they are normally heavily solvated in aqueous environments have made the design of receptors for saccharides a challenge that has kept the scientific community busy for the last 35 years. Although there have been ground-breaking discoveries in the field, improvements are needed to enhance our disease detection and risk stratification tools.
To address this challenge, we employed a technique known as Dynamic Combinatorial Chemistry (DCC). DCC enables the self-formation and self-selection of the best possible receptor for a given target from a pool or library of potentially good ligands. DCC has been effective for creating receptors for biomolecules such as DNA, RNA, and proteins, but its use for discovering sugar receptors is less explored. In this work, we filled this gap by implementing DCC for screening common saccharides (glucose, galactose, mannose, and fructose) using small, simple, and inexpensive building blocks. Our results indicated that molecule 2DD, which consists of a benzene ring with 2 units of amino acid aspartic acid derivatives connected in positions 1 and 3, is the best receptor in a library of very similar structures for the saccharides glucose, galactose, and mannose. For fructose, molecule 1P, a benzene ring linked to just one unit of the amino acid phenylaldehyde, was appointed as the best receptor. The differential behaviour of fructose can provide insight into the relatively unknown processes behind molecular recognition of sugars.
Molecules 2DD and 1P, as well as some other library members as negative controls, were then synthesised for further testing and DCC results were then validated by Isothermal Titration Calorimetry (ITC) and NMR techniques, proving the effectiveness of DCC as a molecular recognition tool for the creation of receptors for saccharides. Moreover, molecule 1P was found to have a high binding constant (K = 1762 M) and selectivity (50-100 times over other sugars) for fructose, which is surprisingly good considering the simplicity of the receptor.
A much more challenging approach was attempted by employing short peptides as scaffolds in DCC experiments. The benefits of using peptides are numerous but can be summarised in three bullet points: customisability, flexibility, and easiness in their synthesis. Unfortunately, we encountered many difficulties for the complete functionalisation of the peptides within the Dynamic Combinatorial Library (DCL) and this approach did not yield the desired results before the research project came to an end. However, we believe in its potential and the knowledge that we gained on the topic helped to stablish the foundations on which new research will be carried out in the near future within the research group.
In summary, this thesis reports the development of a rapid methodology for the discovery of selective receptors for monosaccharides, employing a library of simple and inexpensive starting building blocks. While this was a proof-of-concept study, it can be scalable to larger library sizes and to target more complex biomolecules, becoming a useful tool that could accelerate the discovery of new molecules with biomedical applications
A Trust Management Framework for Vehicular Ad Hoc Networks
The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a userâs trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driverâs future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These âuntrue attacksâ are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driverâs truthfulness is influenced by their trust score and trust state. For each trust state, the driverâs likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
Kinetic energy fluctuation-driven locomotor transitions on potential energy landscapes of beam obstacle traversal and self-righting
Despite contending with constraints imposed by the environment, morphology,
and physiology, animals move well by physically interactingwith the environment
to use and transition between modes such as running, climbing, and
self-righting. By contrast, robots struggle to do so in real world.
Understanding the principles of how locomotor transitions emerge from
constrained physical interaction is necessary for robots to move robustly using
similar strategies. Recent studies discovered that discoid cockroaches use and
transition between diverse locomotor modes to traverse beams and self-right on
ground. For both systems, animals probabilistically transitioned between modes
via multiple pathways, while its self-propulsion created kinetic energy
fluctuation. Here, we seek mechanistic explanations for these observations by
adopting a physics-based approach that integrates biological and robotic
studies.
We discovered that animal and robot locomotor transitions during beam
obstacle traversal and ground self-righting are barrier-crossing transitions on
potential energy landscapes. Whereas animals and robot traversed stiff beams by
rolling their body betweenbeam, they pushed across flimsy beams, suggesting a
concept of terradynamic favorability where modes with easier physical
interaction are more likely to occur. Robotic beam traversal revealed that,
system state either remains in a favorable mode or transitions to one when
energy fluctuation is comparable to the transition barrier. Robotic
self-righting transitions occurred similarly and revealed that changing system
parameters lowers barriers over which comparable fluctuation can induce
transitions. Thetransitionsof animalsin both systems mostly occurred similarly,
but sensory feedback may facilitate its beam traversal. Finally, we developed a
method to measure animal movement across large spatiotemporal scales in a
terrain treadmill.Comment: arXiv admin note: substantial text overlap with arXiv:2006.1271
Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes
Robotic Information Gathering (RIG) is a foundational research topic that
answers how a robot (team) collects informative data to efficiently build an
accurate model of an unknown target function under robot embodiment
constraints. RIG has many applications, including but not limited to autonomous
exploration and mapping, 3D reconstruction or inspection, search and rescue,
and environmental monitoring. A RIG system relies on a probabilistic model's
prediction uncertainty to identify critical areas for informative data
collection. Gaussian Processes (GPs) with stationary kernels have been widely
adopted for spatial modeling. However, real-world spatial data is typically
non-stationary -- different locations do not have the same degree of
variability. As a result, the prediction uncertainty does not accurately reveal
prediction error, limiting the success of RIG algorithms. We propose a family
of non-stationary kernels named Attentive Kernel (AK), which is simple, robust,
and can extend any existing kernel to a non-stationary one. We evaluate the new
kernel in elevation mapping tasks, where AK provides better accuracy and
uncertainty quantification over the commonly used stationary kernels and the
leading non-stationary kernels. The improved uncertainty quantification guides
the downstream informative planner to collect more valuable data around the
high-error area, further increasing prediction accuracy. A field experiment
demonstrates that the proposed method can guide an Autonomous Surface Vehicle
(ASV) to prioritize data collection in locations with significant spatial
variations, enabling the model to characterize salient environmental features.Comment: International Journal of Robotics Research (IJRR). arXiv admin note:
text overlap with arXiv:2205.0642
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steerâa gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
Probabilistic Inference for Model Based Control
Robotic systems are essential for enhancing productivity, automation, and performing hazardous tasks. Addressing the unpredictability of physical systems, this thesis advances robotic planning and control under uncertainty, introducing learning-based methods for managing uncertain parameters and adapting to changing environments in real-time.
Our first contribution is a framework using Bayesian statistics for likelihood-free inference of model parameters. This allows employing complex simulators for designing efficient, robust controllers. The method, integrating the unscented transform with a variant of information theoretical model predictive control, shows better performance in trajectory evaluation compared to Monte Carlo sampling, easing the computational load in various control and robotics tasks.
Next, we reframe robotic planning and control as a Bayesian inference problem, focusing on the posterior distribution of actions and model parameters. An implicit variational inference algorithm, performing Stein Variational Gradient Descent, estimates distributions over model parameters and control inputs in real-time. This Bayesian approach effectively handles complex multi-modal posterior distributions, vital for dynamic and realistic robot navigation.
Finally, we tackle diversity in high-dimensional spaces. Our approach mitigates underestimation of uncertainty in posterior distributions, which leads to locally optimal solutions. Using the theory of rough paths, we develop an algorithm for parallel trajectory optimisation, enhancing solution diversity and avoiding mode collapse. This method extends our variational inference approach for trajectory estimation, employing diversity-enhancing kernels and leveraging path signature representation of trajectories. Empirical tests, ranging from 2-D navigation to robotic manipulators in cluttered environments, affirm our method's efficiency, outperforming existing alternatives
ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting
Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perceptionâactionâdecision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment.
The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platformâs action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented.
Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable
Active SLAM: A Review On Last Decade
This article presents a comprehensive review of the Active Simultaneous
Localization and Mapping (A-SLAM) research conducted over the past decade. It
explores the formulation, applications, and methodologies employed in A-SLAM,
particularly in trajectory generation and control-action selection, drawing on
concepts from Information Theory (IT) and the Theory of Optimal Experimental
Design (TOED). This review includes both qualitative and quantitative analyses
of various approaches, deployment scenarios, configurations, path-planning
methods, and utility functions within A-SLAM research. Furthermore, this
article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM),
focusing on collaborative aspects within SLAM systems. It includes a thorough
examination of collaborative parameters and approaches, supported by both
qualitative and statistical assessments. This study also identifies limitations
in the existing literature and suggests potential avenues for future research.
This survey serves as a valuable resource for researchers seeking insights into
A-SLAM methods and techniques, offering a current overview of A-SLAM
formulation.Comment: 34 pages, 8 figures, 6 table
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