Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning-based classification and risk factor analysis of frailty in Korean community-dwelling older adults

Frailty is a dynamic reversible state, characterized by frequent transitions between frailty status over time. The timely and effective detection of frailty is important to prevent adverse health outcomes. This study aims to develop machine learning-based classification models for frailty assessment and to investigate its risk factors. A total of 1,482 subjects, 1,266 robust and 216 frail older adults, were analyzed. Sixteen frail risk factors were selected from a random forest-based feature selection method, then used for the inputs of five ML models: logistic regression, K-nearest neighbor, support vector machine, gaussian naïve bayes, and random forest. Data resampling, stratified 10-fold cross-validation, and grid search were applied to improve the classification performance. The logistic regression model using the selected features showed the best performance with an accuracy of 0.93 and an F1-score of 0.92. The results suggest that machine learning techniques are an effective method for classifying frailty status and exploring frailty-related factors.Clinical Relevance— Our approach can predict frailty using data collectable in clinical setting and can help prevent and improve by identifying variables that change frailty status.
Leveraging Multimodal Sensing and Topometric Mapping for Human-Like Autonomous Navigation in Complex Environments

Autonomous vehicle navigation in complex and unpredictable outdoor environments requires extensive and detailed understanding of the surrounding area and compliance with the traffic rules. In this paper, we attempt to imitate human driver behavior towards autonomous navigation that is suitable for diverse, challenging environments, whether urban, semi-structured or rural-like. Our approach starts with a novel method that we propose for extracting free space area using RGB and LiDAR data, in combination with a rough topometric map for route planning. Local goals are extracted in the final drivable region, and the vehicle draws a local path in the free space that is approximately in line with the overall path via a lattice planner. Our method is evaluated both in the publicly available KITTI urban dataset and a custom-made dataset of a semi-structured environment. In both cases, the results highlight the potential of our approach for further advancements in autonomous navigation and the development of safer and more human-like behaviors in driverless vehicles compared to the existing trajectory prediction state-of-the-art methods that make use of a topometric map.
Counterexample Guided Abstraction Refinement with Non-Refined Abstractions for Multi-Goal Multi-Robot Path Planning

We address the problem of multi-goal multi robot path planning (MG-MRPP) via counterexample guided abstraction refinement (CEGAR) framework. MG-MRPP generalizes the standard discrete multi-robot path planning (MRPP) problem. While the task in MRPP is to navigate robots in an undirected graph from their starting vertices to one individual goal vertex per robot, MG-MRPP assigns each robot multiple goal vertices and the task is to visit each of them at least once. Solving MG-MRPP not only requires finding collision free paths for individual robots but also determining the order of visiting robot's goal vertices so that common objectives like the sum-of-costs are optimized. We use the Boolean satisfiability (SAT) techniques as the underlying paradigm. A specifically novel in this work is the use of non-refined abstractions when formulating the MG-MRPP problem as SAT. While the standard CEGAR approach for MG-MRPP does not encode collision elimination constraints in the initial abstraction and leave them to subsequent refinements. The novel CEGAR approach leaves some abstractions deliberately non-refined. This adds the necessity to post-process the answers obtained from the underlying SAT solver as these answers slightly differ from the correct MG-MRPP solutions. Non-refining however yields order-of-magnitude smaller SAT encodings than those of the previous CEGAR approach and speeds up the overall solving process.
Digital Twin: Enabling Technology, Challenges and Open Research

Digital Twin technology is an emerging concept that has recently become the centre of attention for industry and in more recent year's academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research area; Manufacturing, Healthcare and Smart cities. Discussing a range of papers that reflect these areas and the current state of research. The paper outlines the open research opportunities and challenges.
Developing a Dynamic Graph Network for Interpretable Analysis of Multi-Modal MRI Data in Parkinson’s Disease Diagnosis

Following the aging of the population, Parkinson's disease (PD) poses a severe challenge to public health. For the diagnosis of PD and the prediction of its progression, numerous computer-aided diagnosis procedures have been developed. Recently, Graph Convolutional Networks (GCN) are widely applied in deep learning to effectively integrate multi-modal features and model subject correlation. However, many GCNs which are used for node classification build large-scale fixed graph topologies using the entire dataset, which could make them impossible to verify independently. Furthermore, past GCN algorithms would need more interpretability, limiting their real-world applications. In this paper, an Interpretable Graph-Learning Convolutional Network (iGLCN) is proposed to enhance the performance of personalized diagnosis for PD while simultaneously producing interpretable results. The proposed method can dynamically adjust the graph structure for GCN to better diagnose outcomes by learning the optimal underlying latent graph. Through interpretable feature learning, the proposed network can interpret diagnosis outcomes. The experiments showed that the proposed method increased flexibility while maintaining a high level of classification performance and could be interpretable for PD diagnosis.Clinical Relevance— The proposed method is expected to have good performance in its strong practicability, feasibility, and interpretability for Parkinson’s disease diagnosis.
V2-Net: An Attention-guided Volumetric Regression Network for Tooth Landmark Localization on CT Images with Metal Artifacts

For virtual surgical planning in orthognathic surgery, marking tooth landmarks on CT images is an important procedure. However, the manual localization procedure of tooth landmarks is time-consuming, labor-intensive, and requires expert knowledge. Also, direct and automatic tooth landmark localization on CT images is difficult because of the lower resolution and metal artifacts of dental images. The purpose of this study was to propose an attention-guided volumetric regression network (V2-Net) for accurate tooth landmark localization on CT images with metal artifacts and lower resolution. V2-Net has an attention-guided network architecture using a coarse-to-fine-attention mechanism that guided the 3D probability distribution of tooth landmark locations within anatomical structures from the coarse V-Net to the fine V-Net for more focus on tooth landmarks. In addition, we combined attention-guided learning and a 3D attention module with optimal Pseudo Huber loss to improve the localization accuracy. Our results show that the proposed method achieves state-of-the-art accuracy of 0.85 ± 0.40 mm in terms of mean radial error, outperforming previous studies. In ablation studies, we observed that the proposed attention-guided learning and a 3D attention module improved the accuracy of tooth landmark localization in CT images of lower resolution and metal artifacts. Furthermore, our method achieved 97.92% in terms of the success detection rate within the clinically accepted accuracy range of 2.0 mm.
Demographic Information Fusion Using Attentive Pooling In CNN-GRU Model For Systolic Blood Pressure Estimation

Fusing demographic information into deep learning models has become of interest in recent end-to-end cuff-less blood pressure (BP) estimation studies in order to achieve improved performance. Conventionally, the demographic feature vector is concatenated with the pooled embedding vector. Here, using an attention-based convolutional neural network-gated recurrent unit (CNN-GRU), we present a new approach and fuse the demographic information into the attentive pooling module. Our results demonstrate that, under calibration-based testing protocol, the proposed approach provides improved systolic blood pressure (SBP) estimation accuracy (with R2=0.86 and mean absolute error (MAE)=4.90 mmHg) compared to both the baseline model with no demographic information fused, and the conventional approach of fusing demographic information. Our work showcases the feasibility of using attention-based methods to combine demographic features with deep learning models, and suggests new ways for fusing demographic information in deep learning models to achieve improved BP estimation accuracy.
Remote Assessment of ADHD Symptoms Based on Mobile Game Performance in Children with ADHD: A Proof of Concept

The use of game-based digital medicine is gaining increasing interest in helping children with ADHD to improve their attention outside the clinical setting. In this process, it is important to continue monitoring children’s responses to the use of digital medicine. In this work, we introduce novel digital markers and an analytic pipeline to estimate ADHD-related symptomatic levels during the self-administration of attention games. The digital markers, capturing the children’s characteristics of attention and inattention spans, were extracted and translated into clinically-accepted measures of ADHD symptoms, specifically the ADHD-Rating Scale (ADHD-RS) and Child Behavior Checklist (CBCL). To validate the feasibility of our approach, we collected game-specific performance data from 15 children with ADHD, which was used to train machine learning-based regression models to estimate their corresponding ADHD-RS and CBCL scores. Our experiment results showed mean absolute errors of 5.14 and 4.05 points between the actual and estimated ADHD-RS and CBCL scores respectively. This study enables new clinical and research opportunities for accurate longitudinal assessment of symptomatic levels of ADHD via an interactive means of playing mobile games.
The National Bathymetric Source

Bathymetric data are foundational for many important marine and coastal uses such as increasing the safety of navigation through updated high-resolution nautical charts and services, studying the changing of coastline features in response to climate change, creating hydrodynamic models for coastal resilience efforts, and studying marine life, among others. NOAA's Office of Coast Survey's National Bathymetric Source (NBS) program curates and regularly maintains a bathymetric data compilation for navigation, planning, and public needs. The traditional manual workflow of various disparate sources applied to a navigation product has been reimagined by leveraging automation in compiling sources using hydrographic quality metrics to determine the best available data for any geographic area. All bathymetric data sources acquired must be evaluated for depth, quality, and source information to make NBS data-driven workflows effective. The originating metadata informs the normalization of format, coordinate reference system, coverage, and quality assessment to prepare each source for compilation. The quality metrics used during the compilation phase to determine the best available bathymetry are carried through to the extracted products to inform effective use. The extracted bathymetry is prepared for navigation, planning, and public pipelines in customer-specific format, coordinate reference system, and resolution. All phases of the NBS workflow are discussed, from acquiring source through extracting bathymetry. The NBS program is driven to support the United States' bathymetry needs with timely, curated, and accessible bathymetric data.
Atrous spatial pyramid pooling and multi-image data fusion for smooth muscle segmentation in upper gastrointestinal sphincters *

Gastrointestinal (GI) sphincters provide critical roles in regulating the transport of contents along the GI tract. Dysfunctions of GI sphincters are associated with a range of major digestive disorders. Despite their importance, the microstructures of GI sphincters are not well investigated. While micro-computed tomography (μ-CT) provides detailed 3D images, conventional segmentation methods rely on manual correction, which is both time-consuming and prone to human error. This study proposes a segmentation method using atrous spatial pyramid pooling (ASPP), which helps in capturing different effective fields of view from a given input feature map, thereof providing finer local and global information for a given pixel. Additionally, we explored the use of multi-species data fusion to make the model more generalized. The proposed segmentation network incorporating ASPP and multi-species data fusion improved the segmentation of sphincter muscle images. Specifically, it increased the dice score and Jaccard index by 3.7% and 5.8%, respectively, while reducing the variance compared to conventional methods.Clinical relevance— Techniques developed in this study will inform μ-CT segmentation of human upper GI sphincters for detailed structural analysis of muscular dysfunction.
Privacy-Preserving Model Predictive Control Using Secure Multi-Party Computation

In this paper, a secure multi-party computation strategy based on secret sharing is used to derive a privacy-preserving model predictive control for a class of cyber-physical systems. In the proposed framework, the underlying optimization problem of model predictive control is solved by a variation of projected gradient method. All required computations are carried out by outsourced computation units at the cloud level, while data privacy is maintained using a secret sharing scheme. The original values of system private parameters are not revealed to any external eavesdroppers and the cloud computing units. Simulation results demonstrate the efficiency of the proposed method in terms of performance and privacy.
A Pilot Study of Deep Learning Models for Camera based Hand Hygiene Monitoring in ICU

Hand hygiene is key to preventing cross-infections in the Intensive Care Unit (ICU). Monitoring of hand washing activities can effectively increase the compliance of clinicians to hand hygiene. In this paper, we explored the feasibility of recognizing clinicians’ hand-washing activities using a clinical dataset recorded in ICU using CCTV cameras. We benchmarked three types of hand hygiene detection methods on the dataset with the aim of identifying the 7-step hand washing procedure defined by WHO. Experimental results show that our approach achieves 97% average accuracy for personalized and 67% for generalized modeling. Preliminary results indicate that hand hygiene recognition is subject-dependent, and thus cross-subject modeling or subject-adaptive learning should be used to enhance generalization. The feasibility and challenges of CCTV-camera-based hand hygiene recognition are discussed. The results may contribute to design a hand hygiene scoring and alert system as part of the IoT system in hospitals. The hospital data and code are available at https://github.com/SunnySideUp11/ICU-MH-20
SoK: Essentials of BFT Consensus for Blockchains

Blockchain technology has emerged as a transformative force within the contemporary Internet infrastructure, introducing distinctive attributes that have revolutionized existing distributed systems. The principles of decentralization, immutability, and transparency inherent to blockchain have prompted numerous applications to embrace its design philosophy and tailor diverse replicated solutions. Central to the blockchain framework, consensus protocols play a pivotal role in achieving distributed replication systems. The distributed systems research community has devoted considerable effort to dissecting the technical in-tricacies of consensus mechanisms, aimed at facilitating consensus among a cluster of nodes. However, persistent trust concerns make crafting resilient systems for practical scenarios challenging, given the array of potential faults. Byzantine fault-tolerant (BFT) state machine replication (SMR) emerges as an optimal contender capable of withstanding arbitrary faulty behaviors. Yet, the inherent intricacies of BFT consensus protocols, coupled with their rapid evolution, pose practical adaptation challenges across various application domains. Several noteworthy Byzantine-based replicated solutions and innovative concepts have contributed substan-tially to enhancing performance, availability, and resource efficiency. This paper undertakes a comprehensive and systematic exploration of BFT consensus protocols, with a distinct focus on the blockchain era. We delve into the core facets of BFT consensus protocols tailored for Byzantine settings within the realm of blockchain. Further, we deconstruct state-of-the-art solutions to gain a detailed understanding of BFT consensus. For each constituent, we engage in an in-depth discourse concerning its pivotal architectural foundations and key techniques. Our objective is to provide system researchers and developers with a tangible perspective of the present design landscape, thereby aiding them in formulating solutions to specific challenges.
Design and Electromagnetic Analysis of Brushless Salient Pole Switching Flux Synchronous Generator with DC Auxiliary Field Winding for Wind Energy Converter Systems

Due to the depletion of fossil resources, employing renewable energies has been increased and experienced significant growth in recent years. Among these long-lasting sources, Wind energy stands out as a superior renewable energy source for energy supply in the entire world due to its availability and continuity. The specific generators used in wind turbines, such as doubly-fed generators, have dominated wind turbines due to their low cost. But the main issue associated with these types of generators is their sensitivity to voltage drop. In fact, with the occurrence of faults within the grid and the resulting voltage drop, the current on the stator side increases and due to the connection between the stator and the rotor, this current is transferred to the rotor leading to utilizing complex and expensive electronic power converters. In this paper, the design and electromagnetic analysis of brushless salient pole induction generator with direct current auxiliary field winding is investigated. On the proposed stator structure, in addition to the phase windings, there are also field windings while there are no windings placed on the rotor. The proposed structure can easily manage the flux through the simple control of the direct current of the field winding. This way, generator output voltage is effectively controlled. The analysis and simulation results guarantee the suggested structure.
Voltage stability enhancement based optimal reactive power control

Power system networks are exposed to various environmental and economic challenges that can have a detrimental impact on their stability and capacity to provide a consistent power supply, particularly in developing countries. The voltage stability monitoring is crucial for the system's status and capability to provide an uninterrupted power supply. Delayed responses to voltage instability can significantly damage the power grid system, making it essential to take prompt and appropriate action to mitigate voltage instability and ensure the system's safe and efficient operation. This paper proposes two approaches to enhance the stability of power systems. A neural network employing feedforward backpropagation to detect the critical bus in the IEEE 9-bus system based on Voltage Stability Index Lmn and loadability Index. The FFBPNN identifies the critical bus prone to voltage collapse in the power system, trained using a backpropagation algorithm. The second approach utilizes an optimization technique based on Particle Swarm Optimization PSO to optimize the reactive power control of a Static Var Compensator SVC device through its size and location, where the SVC device considers a type of Flexible AC Transmission System FACT device. This approach minimizes the reactive power effect which causing of voltage instability during high load demand, improving the voltage stability of the system. The proposed method is evaluated under different load levels and network configurations and validated based on the Newton-Raphson method for optimal power flow analysis.
Virtual Instrument for Recognition of F.Moniliforme Infected Corn Seeds Based on Color Image Analysis and Labview

A virtual instrument for color image analysis of sound and infected with Fusarium Moniliforme corn seeds developed in LabView is presented in this paper. A modified approach for classification of seeds based on extracted features using Karhunen-Loeve transformation has been proposed. Classification of 16 varieties of corn by using different Bayesian classifiers, two types of linear classifiers - with an average weighted and average arithmetic covariance matrix, and quadratic classifiers is allowed by the instrument. Experimental results for 16 Bulgarian varieties are obtained and showed total error of 0.55 % - 6.9 % for control measurements of the seed sample.
Using Four-Point Impedance to Detect and Locate Blood during Cochlear Implantation*

Biosensing technologies are emerging as an important consideration when designing implantable medical devices. For cochlear implants, biosensors may help preserve the natural hearing a patient has prior to implantation by detecting blood in the cochlea during insertion. If blood enters the cochlea, it creates a hostile environment leading to further hearing loss and reduced device function. Here we present four-point impedance, measured directly from a commercial cochlear implant, as a biosensor for real-time detection of blood in the cochlea. The four-point impedance of different concentrations of whole blood in saline were measured using the impedance-measuring capabilities of a cochlear implant with a square-wave stimulation. Impedance derived from a cochlear implant succeeded in differentiating concentrations of blood in saline with results from a sensitivity analysis showing the lowest concentration the system could detect was between 12 % to 21 % of whole blood. In a subsequent in-vitro study, continuous four-point impedance was measured from a cochlear implant while it was inserted into a 3D printed cochlear model, followed by an injection of blood to emulate surgical events. These results demonstrated four-point impedance from a cochlear implant can instantaneously detect the addition of blood within the cochlea and localize it along the electrode array. The adaptation of a biosensing tool using a cochlear implant provides more information that can be relayed to the surgeon intraoperatively to potentially enhance hearing outcomes with the implant.Clinical Relevance — Using the cochlear implant itself to detect intra-cochlear bleeding may open therapeutic avenues to prevent further hearing loss.