Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative Rare Disease Profile Creation via NormMap to Advance Rare Disease Research

Given the nature of rare diseases, lack of data and standards impedes research in rare diseases. A method to improve data interoperability is necessary to allow data reuse, integration, and exchange in rare disease. A computational package named NormMap was developed to identify rare disease related data from various types of resources in free text via semantic annotation with rare disease terms from NCATS Genetic and Rare Diseases (GARD). In this preliminary study, four different sources which include NIH funded projects, clinical trials, PubMed articles, and Reddit subreddits, were applied to generate rare disease profiles by extending and exploring NormMap. Those profiles would offer a complete view of rare diseases from different aspects, funding agencies, patient groups, scientific research, to ultimately advance rare disease research, which is demonstrated in our case study.
Photoluminescence Properties of ZnO Thin Films Grown by Electrochemical Deposition

We have investigated the photoluminescence properties of ZnO thin films grown by electrochemical deposition. By applying a thermal treatment at 500°C in O2 atmosphere, a free-exciton photoluminescence is observed. Furthermore, under high-density excitation conditions, a photoluminescence band appears on the low-energy side of the free exciton. The photoluminescence intensity exhibits an almost quadratic dependence on the excitation power, and the energy spacing between the photoluminescence band and the free exciton is almost equal to the exciton binding energy of ZnO. We conclude from the above results that the photoluminescence band under high-density excitation conditions originates from an inelastic scattering process of excitons, the so-called P emission.
A 3D-Printed Hybrid Dielectric Resonator Antenna with Low Cross-Polarization

A 3D-printed dielectric resonator antenna (DRA) working from 4.75 to 5.25 GHz with low cross-polarization is presented. The polarization purity is significantly enhanced by using an optimized T-shaped feeding structure, when compared to the utilization of a conventional probe-fed excitation. The T-shaped feeding structure can reduce the cross-polarization level up to 8.2 dB at lower frequencies, which makes the highest cross-polarized components around -15 dB over the whole operation bandwidth. A grounded metal strip is introduced to compensate the increase in working frequency due to the T-shaped feed. The agreement between simulation and measurement results validates the effectiveness of the proposed approach in polarization purity improvement.
A Complex-Valued Dual-Domain Dilated Convolution Neural Network for Brain MRI Reconstruction

Magnetic resonance imaging (MRI) is a powerful imaging method that provides rich anatomical information in clinical applications, leading to more accurate diagnosis and pathological analysis. However, MRI acquisition is limited by the hardware performance and scan time, making it challenging to obtain complete high-quality images. In recent years, MRI reconstruction algorithms using deep learning (DL) have demonstrated good capabilities in improving image quality and accelerating image acquisition. Thus, a new complex-valued dual-domain dilated convolution neural network (C3DNet) providing fast and accurate MRI reconstruction is proposed in this paper. Unlike existing DL-based methods, the developed C3DNet uses complex-valued convolution to extract complex-valued features from the k-space and image-domain data separately and perform dual-domain feature fusion. Additionally, we use dilated convolution to expand the features’ receptive field and thus capture contextual information. To fully use the prior knowledge, we utilize two data consistency (DC) methods and apply them several times to both the k-space and image-domain feature maps. Experimental results demonstrate that our proposed method outperforms several state-of-the-art algorithms regarding imaging results and computation time.
Modeling and Dynamic performance of Energy Storage -Rotary Series Elastic Actuator for Lumbar Support Exoskeleton

Assistive exoskeletons are rapidly being developed to collaborate with humans, and the demand for the safety of human-robot interaction has become more crucial. Series elastic actuators (SEAs) have recently been developed for various possible advantages, such as providing a safe human-robot interaction, reducing the impacts' effects, and increasing energy efficiency. However, achieving good dynamic performances of SEAs is still challenging, especially in fulfilling the high bandwidth with good compliance. In this rapidly growing research field, the actuation system involving the storage device combined with the rotary series elastic actuator (ES-RSEA) is being investigated to exploit the biomechanical energy while maintaining compliance features. In this article, the modeling and control design of the energy storage rotary series elastic actuator (ES-RSEA) for the lumbar support exoskeleton is proposed, and its dynamic performances are analyzed. The ES-RSEA was designed to store kinetic energy during lifting tasks and generate assistive torque while maintaining excellent compliant characteristics. The dynamic performances and characteristics of ES-RSEA are presented in terms of force sensitivity, level of compliance, transmission ratio, and bandwidth. Simulation studies indicate that the actuator can provide excellent dynamic performance through its high bandwidth (12.44 Hz) and high force sensitivity. At the same time, it shows excellent compliance and good torque transmissibility in the low-frequency range. A PID controller can achieve high torque tracking performance and good dynamic response with a root-mean-square (RMS) error of 0.1 N.m. This article demonstrates the excellent performance and characteristics of ES-RSEA to guarantee compliance and high response to prevent injury of undesired human movements.
A Review of Research on Industrial Time Series Classification for Machinery based on Deep Learning

This research investigates detecting machine failures in a manufacturing process using multivariate time series data. From a methodological standpoint, fault detection and diagnosis in industrial machines based on supervised deep learning could be divided into convolutional neural network-based and recurrent neural network-based methods. This paper provides a systematic overview of the most recent research developments regarding the two methods. This study uses the raw data from a public Machinery Fault Database to evaluate these models. The results of this study indicate that the Residual Network (ResNet) model adapted for time series provides the best performance. The paper concludes with a summary of the significant challenges from the perspective of industrial applications, followed by a projection of future development directions.
MEST: Multi-plane Embedding and Spatial-temporal Transformer for Parkinson’s disease diagnosis

Parkinson’s disease (PD) is a common neurodegenerative disorder that impairs the patient’s quality of life. Medical imaging technology has provided a variety of neuroimages for PD diagnosis studies. However, how to effectively integrate the rich representations from multi-modality data is still a challenging task. To address this challenging task, we propose a multiplane embedding and spatial-temporal Transformer (MEST) framework for PD diagnosis. Firstly, we project structural magnetic resonance imaging (sMRI) into 2D images from coronal, sagittal and axial directions, respectively. Then, the multi-plane dynamic images are learned by pre-trained VGG11 and attention mechanism for representation learning. Afterwards, time series information of functional magnetic resonance imaging (fMRI) is used to construct dynamic functional connection images. To capture information changes in brain, spatial-temporal connectivity Transformer (SCTransformer) is utilized to address spatial-temporal redundancy and dependencies. To integrate multimodality data, ensemble learning is designed based on majority voting strategy to perform PD diagnosis. We evaluate the proposed method on 279 subjects from an in-house and Parkinsons Progression Markers Initiative (PPMI) dataset. Experimental results show that the MEST achieves promising performance with accuracies of 0.856 and 0.885, and outperforms some state-of-the-art methods.
Super Learner Ensemble for Sound Classification using Spectral Features

Audio samples have emerged as a trend for monitoring and improving decision-making in smart cities, medical applications, and environmental event detections. This paper proposes a Super Learner ensemble application in two scenarios: to distinguish urban from domestic sounds, and detect abnormal samples in industrial machines. The Super Learner combines supervised classifiers to detect abnormal samples or determine a class of an event from spectral features extracted from original sounds. We study the impact on time processing and performance of varying the number of K-folds in the cross-validation step using the Environmental Sound Classification (ESC-50) and Malfunctioning Industrial Machine Investigation and Inspection (MIMII) datasets. The performance evaluation demonstrates that RF is the best classifier in the ESC-50 dataset and SVM in the MIMII dataset. However, the Super Learner reaches AUC and F1-Score values near the best algorithm in the majority of cases analyzed, representing the best tradeoff solution.
Gene Ontology based protein functional annotation using pretrained embeddings

The Gene Ontology (GO) database contains approximately 40,000 classes of terms arranged in a hierarchical relationship. These terms mainly define protein functions and are used in bioinformatics to automatically predict protein functions using their sequences. Recently, several models have been studied, such as ProtBert and ProteinBERT, which predict protein functions by fine-tuning a pretrained model of the nucleotide sequence using a self-supervised deep method. We proposed two models to predict GO using protein features extracted by the ProtBert model to annotate proteins with their GO terms. Additionally, we customized the ProteinBERT model and fine-tuned it to predict GO terms. The experiment showed that protein embeddings created using pretrained transformer models can be used as a source of data for tasks involving sequence prediction, with a focus on protein functions. The suggested models allow flexible sequence lengths and provide improved performance compared to other comparison methods.
3D Printed Anisotropic Dielectric Polarizer for 38 GHz Low Profile Horn Antenna Array

The following article presents the design of a 3D- printed anisotropic material used as a circular polarizer for a low- profile horn antenna array at 38 GHz. The proposed design is manufactured and measured. Measurement results shoiv that the use of loiv-loss filaments is suitable for this type of designs at mm-wave frequencies, being a cost-effective solution.
Optimizing Traffic Controllers along the MLK Smart Corridor Using Reinforcement Learning and Digital Twin

With advancements in Intelligent Transportation Systems (ITS), sensors, and computing resources, several cities across the world are investing in the development of smart/connected corridors. These corridors are being equipped with advanced sensors that provide real-time, high-resolution data from the corridor and enable vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. The objective of this study is to optimize signal timings for one such smart corridor – MLK Smart Corridor – in Chattanooga, Tennessee, USA with respect to fuel and energy consumption (represented by Fuel Consumption Intersection Control Performance Index, EcoPI, that determines the excess fuel consumption due to stops and delays caused by traffic controllers).
DeepTracer-Denoising: Deep Learning for 3D Electron Density Map Denoising

Cryo-electron microscopy (Cryo-EM) is widely used in molecular structure determination and drug discovery. Experimental cryo-EM images suffer from the noises introduced by electron beam dose and sample preparation. Although many approaches have been proposed to improve the signal-to-noise ratio (SNR) for cryo-EM image denoising, the noises are still presented after 3D reconstruction and can obstruct the analysis and visualization of the 3D density map. Here we present DeepTracer-Denoising, a method for 3D electron density map denoising. We employ a 3D Neural Network to learn the pattern of noises and the biological structure from density maps. Our method is designed to work on medium to high-resolution maps ranging from 2.5 A to 10.0A. It is configurated with two modes to tackle both background noise and structural noise in a 3D density map. Our method can correctly identify 97.70% background noise while preserving 96.46% density of the native structure. For the maps that contain structural noise, DeepTracer-Denoising achieves an overall accuracy of 98.95%.
Crime Scene Prediction Using the Integration of K-Means Clustering and Support Vector Machine

An increasing crime rate among urban residents has become a major concern over the last decade. Prevention can be done with advanced prediction based on criminal activities and locations. A publicly available dataset from kaggle.com was used in this research, consisting of 500 records of information, such as the coordinates of the crime locations and the types of crimes. An unsupervised machine learning algorithm, K-Means Clustering, is applied to group the data based on the locations of the reported crimes. Then, Support Vector Machine, a supervised machine learning algorithm, is applied to predict the potential crime locations. Thus, law enforcement agencies can make strategic plans and deploy their units to the predicted crime scenes, decreasing the chances of crimes being committed. Even though the integration of K-Means Clustering and Support Vector Machine for crime scene prediction only shows 0.65 accuracies, improvements can still be made for future work with larger datasets and integrating other machine learning algorithms.
Energy-Efficient Radix-4 Belief Propagation Polar Code Decoding Using an Efficient Sign-Magnitude Adder and Clock Gating

Polar encoding is the first information coding method that has been proven to achieve channel capacity for binary-input discrete memoryless channels. Since its introduction, much research has been done on improving decoding performance, execution time and energy efficiency. Classic belief propagation uses radix-2 decoding, but a recent study proposed radix-4 decoding which reduces memory usage by 50%. However a drawback is its higher computational complexity, negatively impacting energy usage and throughput. In this paper we present an energy-efficient radix-4 belief propagation polar decoder architecture that uses a new sign-magnitude adder that does not require conversion to two's complement and back. On top of that we also propose using clock gating of input values by checking if all $R$ inputs of the decoder are zero. These two key contributions lead to a more energy -efficient design that is smaller and has higher maximum clock speed and throughput. Post-layout simulation results show that compared to the previously proposed 1024-bit radix-4 belief propagation polar code decoder, our decoder uses between 30.22 % and 32.80 % less power and is 5.2 % smaller at the same clock speed. Also, our design can achieve a 15.7% higher clock speed at which it is still up to 10.76% more power efficient and 4.8% smaller.
Poster: Towards Efficient Multilayer Collaboration for CAV Applications

Connected and autonomous vehicles (CAV s) are facing increasing amounts of data and more complex data analysis, which creates challenges for them to make reliable decisions in real-time. To enable time-sensitive CAV applications, we design and implement a vehicle-edge-cloud framework that integrates compressed imaging (CI) and edge computing into CAV systems. Specifically, a lightweight model is used on the vehicle to perform real-time detection based on optical domain compressed data (called measurements). The edge is responsible for receiving the measurements and performing video reconstruction to support (more accurate) analysis based on the reconstructed video with a trigger. At the same time, the measurements, reconstructed videos, and analysis results are sent to the cloud to continuously update the vehicle model. In addition, we apply reinforcement learning to adapt the compression rate in different driving scenarios. The proposed framework is fully evaluated using our designed roadside platform and outdoor delivery vehicles.
Graduates′ views on the curriculum and the transition to the world of work: skills, knowledge, and generic engineering competencies

The aim of this work was to gather insights from our recent graduates (≤10 years since graduation) on what aspects of their undergraduate experience were useful, what skills could have been foregrounded more, what aspects could be considered for change or update, considering their experiences after graduation, as well as how their expectations met reality in the world of work. Graduates strongly indicated support for, and appreciation of, the strong technical content of the curriculum, especially process design. Core chemical engineering skills were discussed as strongly grounded in the current curriculum, along with the development of generic competencies. Further, problem solving and critical thinking, as well as personal development (working under pressure, work ethic, time management and grit) were valued. Suggestions for changes in curriculum included an emphasis on financial, economic, and business subject content, development of leadership, management and interpersonal skills, and stronger ties with, and integration of, industry into technical courses, calling for real-world practical application of knowledge and skills. However, it must be noted that insertion of additional content into an already full curriculum is inadvisable, and a more subtle approach to including the recommended ideas should be considered. In consideration of expectations, there were several cases where alumni expectations of either their relative skill level, or what the world of work is like mismatched with reality. An emerging theme from this research is that educators could do more to align expectations to smooth the transition to industry. Although large-scale recurriculation is not always possible (or feasible), existing programs can be modified to embed or integrate many of the suggestions put forward by graduates, diminishing the gap between the world of work and the curriculum, and enhancing the programme offering.
Risk Management: Information Technology and Public Value Delivery

The regulations, guidelines and procedures defined to support organizations with regard to information and communication technology (ICT) play a crucial role when any organization wants to carry out good corporate governance. In an attempt to ensure adequate control and management of activities, processes and procedures, the public sector is currently subject to the implementation and adoption of multiple models and standards. Risks related to communication and ICT still occur in these bodies. Despite the development of ICT risk management methodologies that have been published in various frameworks and/or standards to help organizations deal with ICT risks, it has still been questioned whether their methodology has demonstrated success. However, there is no single model that provides a unified solution, allowing for an integrated and global improvement of the processes used in this context. In addition to the need to support public institutions interested in introducing new processes, or improving existing ones in the area of ICT risk, with a view to better results in delivering public value.
A Meta-surface-inspired Circularly Polarized Antenna with Magneto-Electric Feeding Structure

This paper presents a meta-surface-inspired antenna with magneto-electric features to reduce the antenna height and achieve low cross-polarization. An advantage of the antenna is that the proposed antenna has a simple design. I addition to this, the proposed antenna has stable gain characteristics of RHCP in the frequency band ranging from 8.2 GHz to 10.2 GHz with a good impedance matching and low-cross polarization in a wide azimuth range.
A combination of multi and univariate anomaly detection in urban irrigation systems

Water scarcity is a global concern that requires a more intelligent way to manage the water network in order to minimize consumption and losses. There is a current need for new automated and intelligent decision support systems that employ data analytic advances to analyze in real-time the anomalies and problems that appear in water usage. In our case, we have developed a 3 steps system using unsupervised algorithms, that has been tested on the irrigation of urban parks. Those steps consist of grouping the parks according to similarities in the irrigation by clustering the consumption time series, searching for the dates where multivariate anomalies occur in every group using the Vector Autoregressive Model, and finally, applying an ARIMA framework to each series in the area of those dates. Our methodology reduces the time that anomaly detection univariate systems require for analysing the whole univariate time series by extracting previous knowledge on a multivariate approach. This methodology, when integrated with an IoT platform, is a tool for easing the labelling of real anomalies and can help create supervised datasets for future research in the area. The approach is used in urban scenarios, however, can easily be extended to be an application for smart agriculture scenarios.
Semantic Segmentation Method for Tooth Type with Data Augmentation Using Alpha-Blending

The purpose of this study was to develop a semantic segmentation method for each tooth type by DeepLab v3+ with data augmentation using alpha-blending. Our database consisted of 119 dental panoramic X-ray images and 119 truth label images for 32 tooth types. In the proposed method, training dataset was augmented using alpha-blending to be more focused to tooth shape in training DeepLab v3+. Mean intersection over union (IoU) for each tooth with the proposed method was 0.671, which was greater than that for DeepLab v3+ without alpha-blending (0.643).
Immortal Under the Edge of a Knife: Self-Healing Distributed Services on MANET Partitioning

Wireless and mobile ad hoc networks will consolidate future network architectures. Due to limited resources and the high churn of nodes, these networks are prone to network partitioning. So far, other work has considered this problem exclusively on the connectivity level by restoring connections by repositioning nodes. However, the application level perspective is missing, although distributed services, like network services, can be built to be robust against random failures. In this work, we examine how distributed storage can adapt its redundancy at the application layer to counteract the repercussions of network partitioning. Adjusting redundancy to present situations is necessary to deal with the dynamic topology in Mobile Ad Hoc Networks (MANET). Consequently, in this work, we propose a formal model for network partitioning from the application point of view. We propose two schemes to add and remove redundancy, to create a self-healing network and compare them to the state-of-the-art concepts. Furthermore, we show the cost of service availability facing partitioning and the disconnection of individual participants. With our proposed schemes, we were able to reduce the storage cost by 80% to ensure the same level of availability as by using state-of-the-art techniques.