Miracle-dz/UniFAR-FTUs huggingface dataset:
Intuitive Spatial Tactile Feedback for Better Awareness about Robot Trajectory during Human–Robot Collaboration

In this work, we extend the previously proposed approach of improving mutual perception during human–robot collaboration by communicating the robot’s motion intentions and status to a human worker using hand-worn haptic feedback devices. The improvement is presented by introducing spatial tactile feedback, which provides the human worker with more intuitive information about the currently planned robot’s trajectory, given its spatial configuration. The enhanced feedback devices communicate directional information through activation of six tactors spatially organised to represent an orthogonal coordinate frame: the vibration activates on the side of the feedback device that is closest to the future path of the robot. To test the effectiveness of the improved human–machine interface, two user studies were prepared and conducted. The first study aimed to quantitatively evaluate the ease of differentiating activation of individual tactors of the notification devices. The second user study aimed to assess the overall usability of the enhanced notification mode for improving human awareness about the planned trajectory of a robot. The results of the first experiment allowed to identify the tactors for which vibration intensity was most often confused by users. The results of the second experiment showed that the enhanced notification system allowed the participants to complete the task faster and, in general, improved user awareness of the robot’s movement plan, according to both objective and subjective data. Moreover, the majority of participants (82%) favoured the improved notification system over its previous non-directional version and vision-based inspection.
Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning

Changeover times are an important element when evaluating the Overall Equipment Effectiveness (OEE) of a production machine. The article presents a machine learning (ML) approach that is based on an external sensor setup to automatically detect changeovers in a shopfloor environment. The door statuses, coolant flow, power consumption, and operator indoor GPS data of a milling machine were used in the ML approach. As ML methods, Decision Trees, Support Vector Machines, (Balanced) Random Forest algorithms, and Neural Networks were chosen, and their performance was compared. The best results were achieved with the Random Forest ML model (97% F1 score, 99.72% AUC score). It was also carried out that model performance is optimal when only a binary classification of a changeover phase and a production phase is considered and less subphases of the changeover process are applied.
The Effect of Cleat Position on Running Using Acceleration-Derived Data in the Context of Triathlons

Appropriate cycling cleat adjustment could improve triathlon performance in both cycling and running. Prior recommendations regarding cleat adjustment have comprised aligning the first metatarsal head above the pedal spindle or somewhat forward. However, contemporary research has questioned this approach in triathlons due to the need to run immediately after cycling. Subsequently, moving the pedal cleat posteriorly could be more appropriate. This study evaluated the effectiveness of a triaxial accelerometer to determine acceleration magnitudes of the trunk in outdoor cycling in two different bicycle cleat positions and the consequential impact on trunk acceleration during running. Seven recreational triathletes performed a 20 km cycle and a 5 km run using their own triathlon bicycle complete with aerodynamic bars and gearing. Interpretation of data was evaluated based on cadence changes whilst triathletes cycled in an aerodynamic position in two cleat positions immediately followed by a self-paced overground run. The evaluation of accelerometer-derived data within a characteristic overground setting suggests a significant increase in total trunk acceleration magnitude during cycling with a posterior cleat with significant increases to longitudinal acceleration (p = 0.04) despite a small effect (d = 0.2) to the ratings of perceived exertion (RPE). Cycling with a posterior cleat significantly reduced longitudinal trunk acceleration in running and overall acceleration magnitudes (p < 0.0001) with a large effect size (d = 0.9) and a significant reduction in RPE (p = 0.02). In addition, running after cycling in a posterior cleat was faster compared to running after cycling in a standard cleat location. Practically, the magnitude of trunk acceleration during cycling in a posterior cleat position as well as running after posterior cleat cycling differed from that when cycling in the fore-aft position followed by running. Therefore, the notion that running varies after cycling is not merely an individual athlete’s perception, but a valid observation that can be modified when cleat position is altered. Training specifically with a posterior cleat in cycling might improve running performance when trunk accelerations are analysed.
On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays

The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.
An Integrated Remote-Sensing and GIS Approach for Mapping Past Tin Mining Landscapes in Northwest Iberia

Northwest Iberia can be considered as one of the main areas where tin was exploited in antiquity. However, the location of ancient tin mining and metallurgy, their date and the intensity of tin production are still largely uncertain. The scale of mining activity and its socio-economical context have not been truly assessed, nor its evolution over time. With the present study, we intend to present an integrated, multiscale, multisensor and interdisciplinary methodology to tackle this problem. The integration of airborne LiDAR and historic aerial imagery has enabled us to identify and map ancient tin mining remains on the Tinto valley (Viana do Castelo, northern Portugal). The combination with historic mining documentation and literature review allowed us to confirm the impact of modern mining and define the best-preserved ancient mining areas for further archaeological research. After data processing and mapping, subsequent ground-truthing involved field survey and geological sampling that confirmed cassiterite exploitation as the key feature of the mining works. This non-invasive approach is of importance for informing future research and management of these landscapes.
Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion Linearization

The power amplifier (PA) is the most critical subsystem in terms of linearity and power efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA operates at levels close to saturation, where the device presents its highest power efficiency. Since the DPD is generally based on Volterra series models, its number of coefficients is high, producing ill-conditioned and over-fitted estimations. Recently, a plethora of techniques have been independently proposed for reducing their dimensionality. This paper is devoted to presenting a fair benchmark of the most relevant order reduction techniques present in the literature categorized by the following: (i) greedy pursuits, including Orthogonal Matching Pursuit (OMP), Doubly Orthogonal Matching Pursuit (DOMP), Subspace Pursuit (SP) and Random Forest (RF); (ii) regularization techniques, including ridge regression and least absolute shrinkage and selection operator (LASSO); (iii) heuristic local search methods, including hill climbing (HC) and dynamic model sizing (DMS); and (iv) global probabilistic optimization algorithms, including simulated annealing (SA), genetic algorithms (GA) and adaptive Lipschitz optimization (adaLIPO). The comparison is carried out with modeling and linearization performance and in terms of runtime. The results show that greedy pursuits, particularly the DOMP, provide the best trade-off between execution time and linearization robustness against dimensionality reduction.
Modifications of the Multi-Layer Perceptron for Hyperspectral Image Classification

Recently, many convolutional neural network (CNN)-based methods have been proposed to tackle the classification task of hyperspectral images (HSI). In fact, CNN has become the de-facto standard for HSI classification. It seems that the traditional neural networks such as multi-layer perceptron (MLP) are not competitive for HSI classification. However, in this study, we try to prove that the MLP can achieve good classification performance of HSI if it is properly designed and improved. The proposed Modified-MLP for HSI classification contains two special parts: spectral–spatial feature mapping and spectral–spatial information mixing. Specifically, for spectral–spatial feature mapping, each input sample of HSI is divided into a sequence of 3D patches with fixed length and then a linear layer is used to map the 3D patches to spectral–spatial features. For spectral–spatial information mixing, all the spectral–spatial features within a single sample are feed into the solely MLP architecture to model the spectral–spatial information across patches for following HSI classification. Furthermore, to obtain the abundant spectral–spatial information with different scales, Multiscale-MLP is proposed to aggregate neighboring patches with multiscale shapes for acquiring abundant spectral–spatial information. In addition, the Soft-MLP is proposed to further enhance the classification performance by applying soft split operation, which flexibly capture the global relations of patches at different positions in the input HSI sample. Finally, label smoothing is introduced to mitigate the overfitting problem in the Soft-MLP (Soft-MLP-L), which greatly improves the classification performance of MLP-based method. The proposed Modified-MLP, Multiscale-MLP, Soft-MLP, and Soft-MLP-L are tested on the three widely used hyperspectral datasets. The proposed Soft-MLP-L leads to the highest OA, which outperforms CNN by 5.76%, 2.55%, and 2.5% on the Salinas, Pavia, and Indian Pines datasets, respectively. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, which shows that the MLP-based methods are still competitive for HSI classification.
SFCW Radar with an Integrated Static Target Echo Cancellation System

Continuous Wave (CW) radars systems, especially air-coupled Ground-Penetrating Radar (GPR) or Through-Wall Imaging Radar (TWIR) systems, echo signals reflected from a stationary target with high energy, which may cause receiver saturation. Another effect caused by reflection of stationary targets is noticeable as background within a radargram. Nowadays, radar systems use automatic gain control to prevent receiver saturation. This paper proposes a method to remove stationary targets automatically from the received signal. The method was designed for a radar system with a moving platform, with an assumption that the distance between the surface and target is constant. The design is proposed of an SFCW radar with an integrated system for real-time multiple static target Echo Cancellation (EC). The proposed EC system removes the static target using active Integrated Circuit (IC) components, which generate the corresponding EC signal for each frequency step of the SFCW radar and sum it with the received echo signal. This has the main advantage of removing even multiple echoes at any distance, and excludes the need for a high-dynamic-range receiver. Additionally, the proposed system has minimal impact on the radar size and power consumption. Besides static target removal, the antenna coupling can be removed if the signal appears to be constant. The operating frequency was selected between 500 MHz and 2.5 GHz, due to the limitation of the used electronic components. The experimental results show that the simulated target’s echo using a cable with a known length could be suppressed to up to 38 dB. Experimental results using a moving radar platform and the real environment scenario with static and dynamic targets, show that the proposed EC system could achieve up to 20 dB attenuation of the static target. The system does not affect any other target of interest, which can even move at any distance during the measurement. Therefore, this could be a promising method for further compact implementation into SFCW radars, or any other radar type that generates CW single frequencies.
Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense.
Repetitive Transcranial Magnetic Stimulation Target Location Methods for Depression

Major depressive disorder (MDD) is a substantial global public health problem in need of novel and effective treatment strategies. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive and promising treatment for depression that has been approved by the U.S. Food and Drug Administration (FDA). However, the methodological weaknesses of existing work impairs the universal clinical use of rTMS. The variation of stimulated targets across the dorsolateral prefrontal cortex may account for most of the heterogeneity in the efficacy of rTMS. Many rTMS target location methods for MDD have been developed in recent decades. This review was conducted to assess this emerging field and to improve treatment outcomes in clinical practice.
HLA-G1+ Expression in GGTA1KO Pigs Suppresses Human and Monkey Anti-Pig T, B and NK Cell Responses

The human leukocyte antigen G1 (HLA-G1), a non-classical class I major histocompatibility complex (MHC-I) protein, is a potent immunomodulatory molecule at the maternal/fetal interface and other environments to regulate the cellular immune response. We created GGTA1-/HLAG1+ pigs to explore their use as organ and cell donors that may extend xenograft survival and function in both preclinical nonhuman primate (NHP) models and future clinical trials. In the present study, HLA-G1 was expressed from the porcine ROSA26 locus by homology directed repair (HDR) mediated knock-in (KI) with simultaneous deletion of α-1-3-galactotransferase gene (GGTA1; GTKO) using the clustered regularly interspersed palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9) (CRISPR/Cas9) gene-editing system. GTKO/HLAG1+ pigs showing immune inhibitory functions were generated through somatic cell nuclear transfer (SCNT). The presence of HLA-G1 at the ROSA26 locus and the deletion of GGTA1 were confirmed by next generation sequencing (NGS) and Sanger’s sequencing. Fibroblasts from piglets, biopsies from transplantable organs, and islets were positive for HLA-G1 expression by confocal microscopy, flow cytometry, or q-PCR. The expression of cell surface HLA-G1 molecule associated with endogenous β2-microglobulin (β2m) was confirmed by staining genetically engineered cells with fluorescently labeled recombinant ILT2 protein. Fibroblasts obtained from GTKO/HLAG1+ pigs were shown to modulate the immune response by lowering IFN-γ production by T cells and proliferation of CD4+ and CD8+ T cells, B cells and natural killer (NK) cells, as well as by augmenting phosphorylation of Src homology region 2 domain-containing phosphatase-2 (SHP-2), which plays a central role in immune suppression. Islets isolated from GTKO/HLA-G1+ genetically engineered pigs and transplanted into streptozotocin-diabetic nude mice restored normoglycemia, suggesting that the expression of HLA-G1 did not interfere with their ability to reverse diabetes. The findings presented here suggest that the HLA-G1+ transgene can be stably expressed from the ROSA26 locus of non-fetal maternal tissue at the cell surface. By providing an immunomodulatory signal, expression of HLA-G1+ may extend survival of porcine pancreatic islet and organ xenografts.
An Integrated Approach for the Efficient Extraction and Solubilization of Rice Microsomal Membrane Proteins for High-Throughput Proteomics

The preparation of microsomal membrane proteins (MPs) is critically important to microsomal proteomics. To date most research studies have utilized an ultracentrifugation-based approach for the isolation and solubilization of plant MPs. However, these approaches are labor-intensive, time-consuming, and unaffordable in certain cases. Furthermore, the use of sodium dodecyl sulfate (SDS) and its removal prior to a mass spectrometry (MS) analysis through multiple washing steps result in the loss of proteins. To address these limitations, this study introduced a simple micro-centrifugation-based MP extraction (MME) method from rice leaves, with the efficacy of this approach being compared with a commercially available plasma membrane extraction kit (PME). Moreover, this study assessed the subsequent solubilization of isolated MPs in an MS-compatible surfactant, namely, 4-hexylphenylazosulfonate (Azo) and SDS using a label-free proteomic approach. The results validated the effectiveness of the MME method, specifically in the enrichment of plasma membrane proteins as compared with the PME method. Furthermore, the findings showed that Azo demonstrated several advantages over SDS in solubilizing the MPs, which was reflected through a label-free quantitative proteome analysis. Altogether, this study provided a relatively simple and rapid workflow for the efficient extraction of MPs with an Azo-integrated MME approach for bottom-up proteomics.
Low Diastolic Blood Pressure Predicts Good Clinical Outcome in Patients With Cerebral Venous Thrombosis

Background: Cerebral venous thrombosis (CVT) refers to a stroke subtype characterized by the disturbance of cerebral venous outflow caused by venous thrombosis. Previous studies have reported a range of factors that predict the prognosis of CVT. This study is aimed to find out whether systolic blood pressure (SBP) and diastolic blood pressure (DBP) are suitable as potential indicators of the severity and clinical outcome in CVT patients. Methods: The CVT patients admitted to Xuanwu Hospital from January 2014 to December 2019 were enrolled. The severity of CVT was assessed by the National Institute of Health Stroke Scale (NIHSS) and intracranial pressure (ICP) at the time of admission. The modified Rankin score (mRS) was assessed at 6 months of follow-up. Results: One hundred fifty-six CVT patients were enrolled with a mean age of 35.8 ± 12.8 years. A percentage of 55.8% of the CVT patients recruited were female, and 17.3% were either pregnant or in perinatal period. Headache was the most common symptom. SBP and DBP were not correlated with NIHSS at admission. Furthermore, SBP and DBP had no impact on the disturbance of consciousness, epilepsy, intracranial hemorrhage, and mental disorders. However, SBP and DBP were positively correlated with ICP at admission. SBP > 129.5 mmHg and/or DBP > 77.5 mmHg suggested the presence of intracranial hypertension (IH). Based on current results, SBP was not correlated with mRS at 6 months of follow-up. However, DBP was found to be positively correlated with mRS at 6 months of follow-up. DBP in CVT patients with good prognosis was significantly lower than in those with poor prognosis. DBP > 79.5 mmHg was identified as a cutoff value to predict a poor clinical outcome. A higher mRS and a higher rate of poor clinical outcome were found in CVT patients with SBP > 146 mmHg or DBP > 79.5 mmHg compared to those with SBP ≤ 146 mmHg or DBP ≤ 79.5 mmHg. Conclusion: SBP > 129.5 mmHg and DBP > 77.5 mmHg suggested the presence of IH in CVT patients. DBP > 79.5 mmHg predicted a poor clinical outcome.
The Relationships Among Structural Social Support, Functional Social Support, and Loneliness in Older Adults: Analysis of Regional Differences Based on a Multigroup Structural Equation Model

Objective: This study investigated the relationship between structural social support and loneliness and explored whether functional social support had an intermediate role therein. It also employed a multigroup structural equation model to compare mediation models among older adults living in cities, towns, and rural areas. Methods: Using a self-made demographics questionnaire, the structural-functional social support scale, and the 3-item UCLA loneliness scale, this study collected information from 1,325 older adults identified via convenient sampling. Results: The results showed that as: (1) compared with older adults living in towns, older urban, and rural adults had higher structural social support and experienced less loneliness, while older adults’ functional social support showed no difference among the three regions (2) An analysis of the models of regional differences indicated that functional social support served as a full mediator in the relationship between structural social support and loneliness in urban older adults, and a partial mediator for older adults living in towns and rural areas. Conclusion: The relationship between structural social support and loneliness is mediated by functional social support, and this mediation model varies between older adults in cities and towns/rural areas. This study helps us understand possible mechanisms through which structural social support impacts loneliness. It suggests that nursing strategies for older adults should be adjusted according to the region and direct greater focus on the function (or quality) of the social support network and older adults living in towns.