Miracle-dz/UniFAR-FTUs huggingface dataset:
Robotic Micromanipulation for Active Pin Alignment in Electronic Soldering Industry

In the context of robotic high-precision soldering, we propose an image-based pin alignment control method based on active plastic deformation. The plastic deformation is a well-known failure mechanism in most situations, which includes a phenomenon that the objects do not return original state. Here, in contrast to this convention, we utilize the plastic deformation of the metal pin to do pin alignment for improving the quality of the solder joint. To address this, we embed the springback compensation into the image-based pin alignment controller. Lastly, the proposed strategy is successfully demonstrated and evaluated in a practical modified robotic manipulation system. The result shows that the alignment error is less than 20µm, which is far less than pin alignment without considering plastic deformation and elastic recovery. This work considers active plastic deformation and spontaneous elastic recovery of soft object, which would greatly promote the use of robotics in micromanufacturing and microfabrication in lad and industry, especially for soft objects.
EMG-Based Neural Network Model of Human Arm Dynamics in a Haptic Training Simulator of Sinus Endoscopy*

This paper proposes an EMG-dependant neural network-based model of human forearm during interaction with a haptic training simulator of sinus endoscopy. We used a conventional lumped mass-spring-damper model as a base model, beside which we took effects of muscle activation level, using surface electromyography (EMG) signals, into consideration. Unknown parameters of a five-parameter mass-spring-damper model are optimised using experimental force and position data with a Levenberg–Marquardt (LM) algorithm. In the training phase, parallel to this lumped model, a neural network (NN) structure is trained to learn the nonlinear mapping between the EMG signals (a way of measuring the muscles activation level that can be interpreted as muscle stiffness) and the parameters of the lumped model. In prediction (operational) phase, the trained neural network makes an estimate of the lumped parameters, using EMG and position data. Therefore, as apposed to conventional constant-parameter (CP) models, the parameters of the lumped model are not fixed in this method and are dependent to the muscle stiffness. Eight trials were performed while the operator was asked to to hold one’s arm in a vertical plane such that their elbow had a right angle keep exerting a quasi-static and also reciprocating force in one direction–a linear motion coaxial to their forearm. Haptic interface was programmed in a way to mimic the impedance model of sinus tissue, a nonlinear viscoelastic Kelvin-Voigt model previously developed by the authors. The estimated forces and the experimental forces are compared for two scenarios: once for the proposed EMG-dependant NN-based model and once again for the constant-parameter lumped model. Results demonstrate the precision improvement on the estimation of the exerted force from human hand to the haptic interface in the proposed model.
Detecting blindspots in colonoscopy by modelling curvature

Optical colonoscopy is the gold standard for colorectal cancer screening, however even under optimal conditions only 81% of the internal tissue is inspected, in part causing up to 22% of early adenomas to be missed. Blindspots commonly occur at acute bends where the camera’s view is blocked. 3D reconstruction alone is insufficient to assess screening completeness as predictions of both the seen and unseen mucosa are required. Existing works in blindspot detection nevertheless use a highly detailed 3D reconstruction as the first step, the complexity of which degrades processing speed and reliability. We demonstrate that this complexity is not needed to predict whether acute bends have been adequately inspected. We propose a parametric model of the colon with only 2 variables: radius and curvature. By incorporating curvature, our method can predict the occlusion which acute bends cause. We use CT scans from 12 patients which on average contain 20 bends. We use a custom colonoscopy simulator and, assuming known geometry, show that a curved model reliably predicts these blindspots while a non-curved model always misses them. From our frame-by-frame predictions we build a panoramic map of the tissue inspected over the procedure. We show that this correctly identifies all 10 blindspots during 3 minutes of colonoscopy. We envisage that by alerting clinicians to mucosa which they have missed in real time, they can revisit these areas, improving the detection of polyps and cancers.
Robust Elbow Angle Prediction With Aging Soft Sensors via Output-Level Domain Adaptation

Wearable devices equipped with soft sensors provide a promising solution for body movement monitoring. Specifically, body movements like elbow flexion can be captured by monitoring the stretched soft sensors’ resistance changes. However, in addition to stretching, the resistance of a soft sensor is also influenced by its aging, which makes the resistance a less stable indicator of the elbow angle. In this paper, we leverage the recent progress in Deep Learning and address the aforementioned issue by formulating the aging-invariant prediction of elbow angles as a domain adaption problem. Specifically, we define the soft sensor data (i.e., resistance values) collected at different aging levels as different domains and adapt a regression neural network among them to learn domain-invariant features. However, unlike the popular pairwise domain adaptation problem that only involves one source and one target domain, ours is more challenging as it has “infinite” target domains due to the non-stop aging. To address this challenge, we novelly propose an output-level domain adaptation approach which builds on the fact that the elbow angles are in a fixed range regardless of aging. Experimental results show that our method enables robust and accurate prediction of elbow angles with aging soft sensors, which significantly outperforms supervised learning ones that fail to generalize to aged sensor data.
MuSDRI: Multi-Seasonal Decomposition Based Recurrent Imputation for Time Series

Missing values are a common problem of time series such as sensor-generated data due to issues of data collection, transmission, and storage. To deal with this problem, various data imputation methods have been proposed. Among these methods, variants of recurrent neural networks (RNNs) have attracted a lot of attention as they provide a natural representation of temporal dynamics. Though such methods have been shown to achieve higher imputation accuracy, they mostly focus on the modeling of temporal dynamics from a short-term perspective while ignoring the long-term features such as trend and seasonal information embedded in time series data. In this paper, we investigate different paradigms of time series imputation methods to capture temporal dynamics from both long-term and short-term perspectives. In particular, we propose a new learning paradigm called MuSDRI which combines the time series decomposition method Seasonal Trend decomposition using Loess (STL) with RNNs. Specifically, STL is used to decompose long sequences of time series into trend, seasonal, and remainder components such that long-term dynamics can be revealed; RNNs are used to capture local patterns from the remainder component extracted by STL such that short-term dynamics can be refined. Moreover, MuSDRI is able to handle time series with multiple seasonal patterns by learning their contributions with respect to the imputation task. We evaluate the proposed imputation methods on three real-world datasets. Experimental results show that the imputation methods considering both the long-term and short-term dynamics achieve higher imputation accuracy, and among them MuSDRI in general achieves the best performance.
Robot Arm Motion Planning Based on Geodesics

Naturally, finding joint trajectories for robotic manipulators involves competing optimization goals. On the one hand, the end-effector should move along a predictable and short path while on the other hand joint movement and acceleration should be kept to a minimum. Obstacles in the workspace or joint limits complicate the situation even further. Constructing a metric that makes undesired configurations more expensive to travel through, we equip the joint space with a notion of cost. The motion planning task then reduces to the problem of finding cheapest paths connecting two configurations – so-called geodesics. We show how to construct suitable metrics for a variety of typical scenarios and present an efficient algorithm for the computation of the corresponding geodesics. Our approach makes it very easy to balance different optimization goals and produces natural and smooth manipulator movement.
Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (−3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.
L-Type Ca2+ Channels of NG2 Glia Determine Proliferation and NMDA Receptor-Dependent Plasticity

NG2 (nerve/glial antigen 2) glia are uniformly distributed in the gray and white matter of the central nervous system (CNS). They are the major proliferating cells in the brain and can differentiate into oligodendrocytes. NG2 glia do not only receive synaptic input from excitatory and inhibitory neurons, but also secrete growth factors and cytokines, modulating CNS homeostasis. They express several receptors and ion channels that play a role in rapidly responding upon synaptic signals and generating fast feedback, potentially regulating their own properties. Ca2+ influx via voltage-gated Ca2+ channels (VGCCs) induces an intracellular Ca2+ rise initiating a series of cellular activities. We confirmed that NG2 glia express L-type VGCCs in the white and gray matter during CNS development, particularly in the early postnatal stage. However, the function of L-type VGCCs in NG2 glia remains elusive. Therefore, we deleted L-type VGCC subtypes Cav1.2 and Cav1.3 genes conditionally in NG2 glia by crossbreeding NG2-CreERT2 knock-in mice to floxed Cav1.2 and flexed Cav1.3 transgenic mice. Our results showed that ablation of Cav1.2 and Cav1.3 strongly inhibited the proliferation of cortical NG2 glia, while differentiation in white and gray matter was not affected. As a consequence, no difference on myelination could be detected in various brain regions. In addition, we observed morphological alterations of the nodes of Ranvier induced by VGCC-deficient NG2 glia, i.e., shortened paired paranodes in the corpus callosum. Furthermore, deletion of Cav1.2 and Cav1.3 largely eliminated N-methyl-D-aspartate (NMDA)-dependent long-term depression (LTD) and potentiation in the hippocampus while the synaptic input to NG2 glia from axons remained unaltered. We conclude that L-type VGCCs of NG2 glia are essential for cell proliferation and proper structural organization of nodes of Ranvier, but not for differentiation and myelin compaction. In addition, L-type VGCCs of NG2 glia contribute to the regulation of long-term neuronal plasticity.
Neuromuscular Age-Related Adjustment of Gait When Moving Upwards and Downwards

Locomotor movements are accommodated to various surface conditions by means of specific locomotor adjustments. This study examined underlying age-related differences in neuromuscular control during level walking and on a positive or negative slope, and during stepping upstairs and downstairs. Ten elderly and eight young adults walked on a treadmill at two different speeds and at three different inclinations (0°, +6°, and −6°). They were also asked to ascend and descend stairs at self-selected speeds. Full body kinematics and surface electromyography of 12 lower-limb muscles were recorded. We compared the intersegmental coordination, muscle activity, and corresponding modifications of spinal motoneuronal output in young and older adults. Despite great similarity between the neuromuscular control of young and older adults, our findings highlight subtle age-related differences in all conditions, potentially reflecting systematic age-related adjustments of the neuromuscular control of locomotion across various support surfaces. The main distinctive feature of walking in older adults is a significantly wider and earlier activation of muscles innervated by the sacral segments. These changes in neuromuscular control are reflected in a reduction or lack of propulsion observed at the end of stance in older adults at different slopes, with the result of a delay in the timing of redirection of the centre-of-mass velocity and of an unanticipated step-to-step transition strategy.
Silkworm Egg Development Intelligent Recognition by THz Imaging

Multidisciplinary cross-integrated researches and interdisciplinary cooperation can help inspire new ideas and accelerate development of disruptive application. Here we combine terahertz (THz) imaging technology and artificial intelligence (AI) algorithms to realize the dynamic monitoring of silkworm egg development. We not only demonstrate, for the first time, rapid recognition of silkworm egg development stage with a recognition accuracy of ~98.5% by intelligent THz imaging technique, but also push the accuracy up to 99.2% through fusing THz imaging and optical imaging. Such AI enabled THz technology can be expanded to other application circumstances.
Sodium-Glucose Cotransporter 2 (SGLT2) Inhibitors vs. Dipeptidyl Peptidase-4 (DPP4) Inhibitors for New-Onset Dementia: A Propensity Score-Matched Population-Based Study With Competing Risk Analysis

Introduction: The effects of sodium-glucose cotransporter 2 inhibitors (SGLT2I) and dipeptidyl peptidase-4 inhibitors (DPP4I) on new-onset cognitive dysfunction in type 2 diabetes mellitus remain unknown. This study aimed to evaluate the effects of the two novel antidiabetic agents on cognitive dysfunction by comparing the rates of dementia between SGLT2I and DPP4I users. Methods: This was a population-based cohort study of type 2 diabetes mellitus patients treated with SGLT2I and DPP4I between January 1, 2015 and December 31, 2019 in Hong Kong. Exclusion criteria were <1-month exposure or exposure to both medication classes, or prior diagnosis of dementia or major neurological/psychiatric diseases. Primary outcomes were new-onset dementia, Alzheimer's, and Parkinson's. Secondary outcomes were all-cause, cardiovascular, and cerebrovascular mortality. Results: A total of 13,276 SGLT2I and 36,544 DPP4I users (total n = 51,460; median age: 66.3 years old [interquartile range (IQR): 58–76], 55.65% men) were studied (follow-up: 472 [120–792] days). After 1:2 matching (SGLT2I: n = 13,283; DPP4I: n = 26,545), SGLT2I users had lower incidences of dementia (0.19 vs. 0.78%, p < 0.0001), Alzheimer's (0.01 vs. 0.1%, p = 0.0047), Parkinson's disease (0.02 vs. 0.14%, p = 0.0006), all-cause (5.48 vs. 12.69%, p < 0.0001), cerebrovascular (0.88 vs. 3.88%, p < 0.0001), and cardiovascular mortality (0.49 vs. 3.75%, p < 0.0001). Cox regression showed that SGLT2I use was associated with lower risks of dementia (hazard ratio [HR]: 0.41, 95% confidence interval [CI]: [0.27–0.61], P < 0.0001), Parkinson's (HR:0.28, 95% CI: [0.09–0.91], P = 0.0349), all-cause (HR:0.84, 95% CI: [0.77–0.91], P < 0.0001), cardiovascular (HR:0.64, 95% CI: [0.49–0.85], P = 0.0017), and cerebrovascular (HR:0.36, 95% CI: [0.3–0.43], P < 0.0001) mortality. Conclusions: The use of SGLT2I is associated with lower risks of dementia, Parkinson's disease, and cerebrovascular mortality compared with DPP4I use after 1:2 ratio propensity score matching.
Chip-Less Wireless Sensing of Kirigami Structural Morphing Under Various Mechanical Stimuli Using Home-Based Ink-Jet Printable Materials

The feasibility of using chip-based RFID designs as wireless sensor tags open a wide range of application possibilities in the field of robotics. However, multi-step lithography manufacturing and/or MEMS techniques are often required for the industrial-grade fabrication of such sensors. In this paper, we present a simple, home-based, two-step fabrication process to produce chipless RF-based wireless sensors. We use an office-based inkjet printer to produce the antenna traces using silver conductive ink. Kirigami-inspired designs are used to produce four sensors responsive to various mechanical stimuli commonly used for DIY robotic projects (contact, compression, extension, and bend). We demonstrate sensing and wireless transmission of the detected mechanical stimuli through the proposed chipless sensor tags with reliable consistency. The tags can be replicated quickly with the inkjet-printing method. This paper also contains analyses on the effects of varying the dimensions and electrical parameters of the tags. The developed antennas in this work can be used as wireless mechano-responsive sensors for robotic applications.
Population, Reproductive and Fishery Dynamics of Spotted Box Crab (Calappa philargius), a New Claw-Only Fishery Species, in the Southern Taiwan Strait, China

The spotted box crab Calappa philargius (Calappidae) is an increasingly consumed species in China, mainly sourced from a claw-only fishery. Being a not well-characterized species in the literature, this study is warranted to inform sustainable management approaches. Here we report on the first in-depth overview on biology and fishery dynamics of C. philargius in the southern Taiwan Strait of China. Whole body crabs (N = 1,009) were collected monthly from January to December 2019 from trawlers, operating in the southern Taiwan Strait; sample collection was absent from May to July because of the national fishing moratorium regulation. Sex ratio, growth pattern, size at maturity and fecundity were estimated. The overall male: female ratio was 1: 1.47, significantly differing from the 1: 1 (p < 0.01); monthly sex ratio variations were also significant (p < 0.01), suggesting possible seasonal sexual segregation. We found the carapace widths (CW) of males were significantly larger than those of females (p < 0.01). Based on the occurrence and percentages of berried females, we identified twin spawning events in January–April and August–October with the overall peak occurring in February. Size at 50% female maturity was 11.47 cm CW. Female absolute fecundity was significantly related to CW via a power function relationship (N = 14, p < 0.01). The estimated average claw yield was 36.28 ± 3.07% of the whole body weight (N = 95), irrespective of sex and size (p > 0.5). Capture and trade data of C. philargius using trawl and trap fishing gears were documented to characterize claw-only fishery dynamics for future research and sustainable use.
Feasibility and Reliability of the AWIN Welfare Assessment Protocol for Dairy Goats in Semi-extensive Farming Conditions

The aim of this study was to test the feasibility and reliability of the Animal Welfare Indicators (AWIN) protocol for welfare assessment of dairy goats when applied to semi-extensive farming conditions. We recruited 13 farms located in the NW Italian Alps where three assessors individually and independently applied a modified version of the AWIN welfare assessment protocol for goats integrated with some indicators derived from the AWIN welfare assessment protocol for sheep. The applied protocol consisted of nine individual-level (body condition score, hair coat condition, abscesses, overgrown claws, udder asymmetry, fecal soiling, nasal discharge, ocular discharge, and improper disbudding) and seven group-level (severe lameness, Qualitative Behavior Assessment-QBA, thermal stress, oblivion, Familiar Human Approach Test-FHAT, synchrony at grazing, synchrony at resting) animal-based indicators. On most farms, the level of welfare was good. Many of the considered welfare problems (overgrown claws, fecal soiling, discharges, and thermal stress) were never recorded. However, oblivion, severe lameness, hair coat condition and abscesses were detected on some farms, with percentages ranging from 5 to 35%. The mean percentage of animals with normal body condition was 67.9 ± 5.7. The level of synchronization during resting was on average low (14.3 ± 7.2%). The application of the whole protocol required more than 4 h/farm and 3 min/goat. The inter-observer reliability varied from excellent (udder asymmetry, overgrown claws, discharges, synchrony at resting, use of shelter) to acceptable (abscesses, fecal soiling, and oblivion), but insufficient for hair coat condition, improper disbudding, synchrony at grazing, QBA. Differences in background of the assessors and feasibility constraints (i.e., use of binoculars in unfenced pastures, individual-level assessment conducted during the morning milking in narrow and dark pens, difficulties when using the scan and instantaneous sampling method due to the high number of animals that moved at the same time) can affect the reliability of data collection. Extensive training seems necessary for properly scoring animals when applying the QBA, whereas the FHAT to evaluate the Human-Animal Relationship of goats at pasture seems promising but needs to be validated. Indicators that evaluate the synchrony of activities require to be validated to identify the best moment to perform the observations during the day.