Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis.
Human-Robot Collaboration for Unknown Flexible Surface Exploration and Treatment Based on Mesh Iterative Learning Control

Contact tooling operations like sanding and polishing have been high in demand for robotics and automation, as manual operations are labour-intensive with inconsistent quality. However, automating these operations remains a challenge since they are highly dependent on prior knowledge about the geometry of the workpiece. While several methods have been developed in existing research to automate the geometry learning process and adjust the contact force, human supervision is heavily required in the calibration of workpieces and the path planning of robot motion in such methods. Furthermore, the stiffness identification of the workpiece is not considered in most of these methods. This paper presents a human-robot collaboration (HRC) framework, which is able to perform surface exploration on an unknown object combining the operator's flexibility with the control precision of the robot. The operator moves the robot along the surface of the target object, and the robot recognizes the surface geometry and surface stiffness while exerting a desired contact force through control. For this purpose, a mesh iterative learning control (MILC) is developed to learn the surface stiffness, plan the exploration path, and adjust contact force through repetitive online correction based on HRC. The proof of learning convergence and the results of the simulation and experiments performed using a 7-DOF Sawyer robot demonstrate the validity of the proposed controller.
Introduce a Novel Approach to Orbital Maintenance in CRTBP

This paper presents a novel approach to designing an orbital maintenance strategy for the periodic Lyapunov orbits in the Circular Restricted Three-Body Problem. Since the position and velocity vector change simultaneously in the moment of non-periodic behavior, a short low-thrust maneuver must be established to return the satellite to the periodic orbit. The low-thrust trajectory is a Two-Boundary Value Problem (TBVP) and requires a particular solving approach. Previous investigations exploited numerical optimal control methods such as Non-Linear Programming (NLP). NLP implementation was hard, and the run time was too long. Hence the current paper utilizes Multi-Objective Particle Swarm Optimization (MOPSO) to design orbital maintenance missions. The following are three sequential steps for implementing the proposed method. It is first necessary to identify the first non-periodic moment of the satellite in the periodic Lyapunov orbit and its state vector. This step determines the criteria for non-periodic behavior through sensitivity analysis. As a groundbreaking idea, the desired state vector (the state vector to which the satellite needs to transfer to continue its periodic behavior) is considered as the corresponding point of the non-periodic starting point at the first period. Due to the characteristics of the Lyapunov orbits, this selection allows the satellite to continue its periodic motion. In the third step, the thruster components and their duration of working time are nominated as design variables. Two objective functions are suggested in this step and minimized by applying MOPSO to recognize the design variables: The first objective function is guaranteed that the state vector is converted from the first non-periodic moment state vector to the desired state vector by applying thruster components. Since the fuel consumption in the deep space mission is vital, the second objective function minimizes fuel consumption by minimizing the thrust magnitude. The Sun-Earth system is regarded as a system of interest, and the proposed method is simulated. The obtained result indicates that the proposed method, in addition to the significant reduction of calculations, will lead to the minimization of fuel consumption in orbital maintenance missions in the CRTBP.
Hand Grasp Motion Intention Recognition Based on High-Density Electromyography in Chronic Stroke Patients

Stroke is a debilitating condition that leads to a loss of motor function, inability to perform daily life activities, and ultimately worsening quality of life. Robot-based rehabilitation is a more effective method than conventional rehabilitation but needs to accurately recognize the patient's intention so that the robot can assist the patient's voluntary motion. This study focuses on recognizing hand grasp motion intention using high-density electromyography (HD-EMG) in patients with chronic stroke. The study was conducted with three chronic stroke patients and involved recording HD-EMG signals from the muscles involved in hand grasp motions. The adaptive onset detection algorithm was used to accurately identify the start of hand grasp motions accurately, and a convolutional neural network (CNN) was trained to classify the HD-EMG signals into one of four grasping motions. The average true positive and false positive rates of the grasp onset detection on three subjects were 91.6% and 9.8%, respectively, and the trained CNN classified the grasping motion with an average accuracy of 76.3%. The results showed that using HD-EMG can provide accurate hand grasp motion intention recognition in chronic stroke patients, highlighting the potential for effective robot-based rehabilitation.
A High-Performance, Fully Encapsulated, Optics-Based Ocean Salinometer

Because salinity is an essential ocean variable whose distribution in time and space is critical to ocean and climate dynamics, in situ salinity measurements are of immeasurable value to numerous oceanographic enterprises. However, like any such measurement, salinity data is in short supply due to the expense and challenge of obtaining measurements, as well as the innate sparseness of point measurements. Thus, there is space for additional sensing capabilities. This effort is an exploration of a viable, optics-based sensor configuration that utilizes Total Internal Reflection (TIR) - the same phenomenon exploited by handheld refractometers and commercially available refractive salinometers such as those employed for aquarium maintenance. The difference in this approach from existing ones is to encapsulate the sensor components in a compact, fully integrated package that lends itself to operating in the harsh ocean environment. Additionally, this small size form factor readily lends itself to integration as a persistent, multi-node array capable of rapid and simultaneous measurements with performance levels comparable to the most trusted oceanographic sensors currently in use. What's more, the compactness, flexibility, and sensing modality enable arbitrary spatial sampling - down to centimeter spacing - and instantaneous profiles. These attributes, and the potential salinity sampling sensor networks they enable, have the potential to unlock as yet unobserved phenomena in ocean dynamics.
An End-to-end In-Silico and In-Vitro Drug Repurposing Pipeline for Glioblastoma

Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive brain cancer with poor prognosis. We propose a computational framework that utilizes machine learning-based propensity score matching to estimate counterfactual treatment effects and predict synergistic effects of drug combinations. Through our in-silico analysis, we identified promising drug candidates and drug combinations that warrant further investigation. To validate these computational findings, we conducted in-vitro experiments on two GBM cell lines, U87 and T98G. The experimental results demonstrated that some of the identified drugs and drug combinations indeed exhibit strong suppressive effects on GBM cell growth. Our end-to-end pipeline showcases the feasibility of integrating computational models with biological experiments to expedite drug repurposing and discovery efforts. By bridging the gap between in-silico analysis and in-vitro validation, we demonstrate the potential of this approach to accelerate the development of novel and effective treatments for glioblastoma.
“Choose-your-own” D3 labs for learning to adapt online code

D3 is a popular library for implementing data visualizations, and is often taught in data visualization classes due to its flexibility, expressivity, and the ubiquity of online D3 example code implementing a variety of visualization techniques. However, D3 can be difficult to learn for novices, and it can be especially challenging to make use of online examples, as many of them are either written in previous, incompatible versions of D3, or are written in Observable notebooks, which often require changes to work in standalone JavaScript. We have previously taught D3 to students using guided tutorials, but found that students struggled to apply what they had learned to other visualization types or contexts, and when given the choice of toolkit in final group projects, often opted not to use D3, limiting the expressiveness of those projects. To address this, we introduced a new assignment type: choose-your-own labs. In each of these labs, students were tasked with implementing a visualization technique from that week’s lecture. We provided a suggested code sample, deliberately choosing code samples in older versions of D3 and/or in Observable notebooks, and asked students to get the code sample working in the latest version of D3 as a standalone webpage. Depending on the complexity of updating the code, we sometimes also asked students to adapt the code sample to work with a different dataset. This paper reflects on our experiences using this new assignment. Although students seemed to find the process of updating and debugging real-world example D3 code to be tedious, they generally responded well to the assignment, and were excited to get more exposure to D3. We also observed that the quality and creativity of the final group projects in the class were noticeably improved from previous iterations of the course: more groups used D3 instead of easier-to-use but less-expressive tools (like Tableau), and the variety of visualization types and sophistication of interactions implemented was much improved. We provide suggestions for educators who want to use a similar format in their courses, and provide our materials at https://osf.io/47vfy/? view_only=7e478a23e3e5414086569694279d38fe.
An Improved Clustering Algorithm for Segmentation of MRI Brain Images Using Small-World Network

The process of image segmentation is widely used in dividing medical images into various clusters. A magnetic Resonance Image (MRI) is an essential auxiliary instrument to diagnose abnormalities. However, the drawback of the MRI image is the presence of noise. Reducing the noise is vital to achieving a suitable quality in segmentation. This paper presents an improved fuzzy clustering in which a small-world network architecture tries to increase the quality of the MRI images especially those related to the brain. The small-world network increases the clustering accuracy because of shortcuts between the cluster, which causes finding a suitable cluster. Experimenting on a real data set shows an improvement in image segmentation quality while increasing the time consumption is negligible.
Hand Design Approach for Planar Fully Actuated Manipulators

Robotic in-hand manipulation increases the capability of robotic hands to interact with the world. The amount of manipulation that a robot is capable of is highly dependent on the design of the robot hand, and previous works have shown success in designing hands to improve performance for different types of grasping and manipulation. In this paper we present a method for designing a fully-actuated planar manipulator that optimizes for specific in-hand motions. We demonstrate that, with the Asterisk Benchmark and a light-weight IK controller, we can translate our results from simulation to the real world with minimal effort and high-fidelity. Using the simulated data (over 4,000 simulated hand-designs) we begin to analyze which features contribute to improved planar manipulation.
Mental Health and care situation in school-aged children prenatally exposed to alcohol and other substances: a hospital based study

Background: Prenatal exposure to substances, including alcohol, opiates and illicit drugs, may influence a child’s neurodevelopment and possibly impact on their subsequent mental health. Maternal use of substances is an increasing public health problem, and it is difficult to obtain a true estimate of the prevalence of prenatally substance-exposed children, partly due to stigmatization and the illegal nature of substance use. Alcohol has well-known teratogenic effects, and its prenatal exposure is associated with fetal alcohol spectrum disorders (FASD), with the development of neurodevelopmental impairments across the lifespan, including an increased risk of various mental health problems. In contrast, less is known about the long-term consequences of prenatal exposure to substances other than alcohol, and previous studies examining its possible effects on neurodevelopment and mental health have shown mixed results, although some studies have reported neurodevelopmental impairments persisting into school age and later into adolescence. It is difficult, however, to distinguish between the possible effects of prenatal substance exposure and those of other factors associated with a child’s neurodevelopment and mental health such as genetic and environmental factors, including the caregiving environment. Objective: The objective of this study was to assess mental health and investigate the care situation and use of supportive measures in a hospital-based population of schoolaged children prenatally exposed to alcohol and other substances, compared to a reference group. Methods: This study included children, aged between 6 and 14 years, with prenatal substance exposure who were referred to the Department of Pediatrics at Haukeland University Hospital in Bergen, Norway, from January 1997 to December 2012. Referral criteria included symptoms of developmental impairment, in the presence of a medical
Immediate Effects of Lower Limb Sensory Simulation Using Smart Socks to Stabilize Gait in People with Parkinson’s Disease*

People with Parkinson’s disease (PD) experience gait impairment that can lead to falls and poor quality of life. Here we investigate the feasibility of using smart socks to stimulate the lower limbs of people with PD to reduce excessive step time variability during walking. We hypothesised that rythmic excitation of lower limb afferents, matched to a participant’s comfortable pace, would entrain deficient neuro-muscular signals resulting in improved gait. Five people with mild to moderate PD symptoms (70 ± 9 years) were tested on medication before and after a 30-minute familierization session. Paired t-tests and Cohen’s d were used to assess gait changes and report effect sizes. Participant experiences were recorded through structured interviews. Lower limb stimulation resulted in an acute 15% increase in gait speed (p=0.006, d=0.62), an 11% increase in step length (p=0.04, d=0.35), a 44% reduction in step time variability (p=0.03, d=0.91), a 22% increase in perceived gait quality (p=0.04, d=1.17), a 24% reduction in mental effort to walk (p=0.02, d=0.79) and no statistical difference for cadence (p=0.16). Participants commented positively on the benefit of stimulation during training but found that stimulation could be distracting when not walking and the socks hard to put on. While the large effects for step time variability and percieved gait quality (Cohen’s d > 0.8) are promising, limitations regarding sample size, potential placebo effects and translation to the home environment should be addressed by future studies.Clinical Relevance— This study demonstrates the feasibility of using smart stimulating socks to reduce excessive step time variability in people with PD. As step time variability is a risk factor for falls, the use of smart textiles to augment future rehabilitation programs warrants further investigation
RGCnet: An Efficient Recursive Gated Convolutional Network for EEG-based Auditory Attention Detection

Humans are able to listen to one speaker and disregard others in a speaking crowd, referred to as the "cocktail party effect". EEG-based auditory attention detection (AAD) seeks to identify whom a listener is listening to by decoding one’s EEG signals. Recent research has demonstrated that the self-attention mechanism is effective for AAD. In this paper, we present the Recursive Gated Convolutional network (RGCnet) for AAD, which implements long-range and high-order interactions as a self-attention mechanism, while maintaining a low computational cost. The RGCnet expands the 2nd order feature interactions to a higher order to model the complex interactions between EEG features. We evaluate RGCnet on two public datasets and compare it with other AAD models. Our results demonstrate that RGCnet outperforms other comparative models under various conditions, thus potentially improving the control of neuro-steered hearing devices.
Vine Robot Localization Via Collision

Localization of robots is a complex task that is often hindered by the sensors these systems use. Due to the majority of field robots being rigid, most of these sensing modalities have the same common faults, such as performance being hindered when their camera vision is obscured. In addition, rigid systems lack flexibility when traversing multiple environments: especially when traversing uneven and unpredictable ground. Soft robots, which can adaptably interact with the environment, could serve as a solution to both problems. One specific soft robot, the Vine Robot, has exhibited excellent performance while moving through constrained, unpredictable environments. This makes the Vine Robot an ideal candidate for a novel method of sensing and localizing in environments, obstacle collision localization. We use our understanding of the nature of Vine Robot motion to be able to predict the tip position of the robot at every instant based on sensor feedback. Through the single obstacle experiments, it was found that our algorithm can provide a precise picture of the tip position of the robot in differing environments. Further, in a multi obstacle demonstration, less than 5% max error relative to the full robot length was observed on the path prediction. Our study helps lay the foundation for a new method for Vine Robot localization using contact as a new sensing modality.
Deploying MANET on a Raspberry Pi 4 Model B Test Bench Using the B.A.T.M.A.N. Protocol

Mobile Ad Hoc Network (MANET) is a non-centralized, self-regulating, easy-to-deploy wireless ad hoc network that can be found in specific applications such as military scenarios, sensor networks, vehicle-to-vehicle communication. Better Approach to Mobile Ad hoc Networking (B.A.T.M.A.N.), developed by the Freifunk community, was chosen as the routing protocol in MANET. The Raspberry Pi series single board computers provide a compact, mobile and low-cost platform that satisfies the required hardware base for MANET deployment. In this paper, three minicomputers Raspberry Pi 4B computers with 4 gigabytes of RAM are used as in MANET as mobile nodes. The testbed presents a lot of experimentation, arranging communication between mobile nodes, analyzing the functionality of the routing protocol and managing communication messages on each node, which can help with many problems present in real-life scenarios for operating MANET systems. The main task is to evaluate performance of such a network is evaluated in two use scenarios using three important metrics: message latency, average channel throughput, and packet delivery ratio.
AI Fairness in Hip Bony Anatomy Segmentation: Analyzing and Mitigating Gender and Racial Bias in Plain Radiography Analysis

Automatic segmentation of hip bony anatomy is a critical component of orthopedics enabling healthcare providers and clinicians to efficiently and objectively accomplish several medical image analysis tasks, including the diagnosis of hip fractures, arthritis, deformity, and dislocation. This autonomous process assists surgeons in preoperative planning by determining the location and size of surgical incisions, the placement of hip implants, and/or other surgical instruments. While deep learning computer vision algorithms for hip segmentation have demonstrated almost human-like performance in past literature, analyzing the fairness and any potential bias within such models has been very limited so far. Thus, the present work aims to provide a better understanding of any visible gender, ethnicity, and racial bias in hip bony anatomy segmentation using plain radiographs.
An Empirical Study on Fault Diagnosis in Robotic Systems

Fault diagnosis in robotic systems is challenging due to their complex and heterogeneous structures and complex interactions with physical environments. Given the complexities and uncertainties, we think it may be helpful to diagnose faults of a robotic system by understanding its behaviors from the perspective of observability. In this paper, we conduct an empirical study to explore the efficacy of combining different kinds of common observability data (i.e., logs, traces, and trajectories) for fault diagnosis in robotic systems. In the study, we investigate root causes of 398 bug cases in robotic systems to understand their characteristics. Furthermore, we replicate 23 bugs out of them and perform a fault diagnosis study in which participants diagnose each of the replicated bug with only observability data and record how useful observability data is. The bug case analysis study revealed that the root causes of bugs in robotic systems originate from various levels, including physical environment interaction (11.81%), hardware usage (14.82%), software implementation (49.25%), and system configuration (24.12%). The fault diagnosis study shows the combination of trace and trajectory data improves the fault diagnosis success rate by 58.33% and 8.33%, respectively, compared to using only logs. Our study promotes the vision of observability-based fault diagnosis in robotic systems.