Miracle-dz/UniFAR-FTUs huggingface dataset:
3D/4D-printed bending-type soft pneumatic actuators: fabrication, modelling, and control

ABSTRACT This article reviews soft pneumatic actuators (SPAs) that are manufactured entirely via additive manufacturing methods. These actuators are known as four-dimensional (4D)-printed SPAs and can generate bending motions in response to either pressurised or vacuum (negative pressure) air stimulus after fabrication. They are characterised by geometrical and material factors that determine their motion trajectory, and the force they exert on manipulated soft objects in delicate applications such as food handling and non-invasive surgery. Here, we review various 3D printers and materials used for the fabrication of the pressurised air bending-type SPAs. The reported approaches for modelling and control of these actuators are presented and compared. General discussions, as well as future directions and challenges of these actuators, are given.
Thinking Before Doing: A Pilot Study on the Application of Motor Imagery as a Learning Method During Physical Education Lesson in High School

Motor imagery (MI), i. e. , the mental simulation of an action without its actual execution, is a promising technique to boost motor learning via physical practice in rehabilitation, sport, and educational fields. The purpose of the present pilot study was to test the feasibility and the effectiveness of the application of MI as learning methodology place alongside conventional teaching technique as employed for physical education lessons. Thirty-three high school students from two classes were enrolled for instruction in the underhand serve in volleyball. One group, the motor imagery group (MIG) carried out the physical exercise along with the kinesthetic MI of the action, while the other group (the control group) was limited to the merely physical exercise. The training period lasted 8 weeks. MI duration and the duration of real movement (ME), the isochrony index (differences between real and imagined movements duration), and the number of balls which passed over the net (NBN) were evaluated before and after training. Results showed a significant improvement in the isochrony index for the MIG group exclusively; namely, MI duration became more similar to ME duration. Moreover, in MIG a significantly negative relationship appeared between the percentage change in the isochrony index and the difference between NBN before and after training. These findings suggest improvement in sensorimotor representation of the action, which lies at the basis of enhanced motor performance. The present study constitutes initial proof of concept on the application of MI as learning technique applicable to physical education lesson at high school.
Feasibility Study and Porting of the Damped Least Square Algorithm on FPGA

Modern embedded computing platforms used within Cyber-Physical Systems (CPS) are nowadays leveraging more and more often on heterogeneous computing substrates, such as newest Field Programmable Gate Array (FPGA) devices. Compared to general purpose platforms, which have a fixed datapath, FPGAs provide designers the possibility of customizing part of the computing infrastructure, to better shape the execution on the application needs/features, and offer high efficiency in terms of timing and power performance, while naturally featuring parallelism. In the context of FPGA-based CPSs, this article has a two fold mission. On the one hand, it presents an analysis of the Damped Least Square (DLS) algorithm for a perspective hardware implementation. On the other hand, it describes the implementation of a robotic arm controller based on the DLS to numerically solve Inverse Kinematics problems over a heterogeneous FPGA. Assessments involve a Trossen Robotics WidowX robotic arm controlled by a Digilent ZedBoard provided with a Xilinx Zynq FPGA that computes the Inverse Kinematic.
Discriminative Multiple Kernel Concept Factorization for Data Representation

Concept Factorization (CF) improves Nonnegative matrix factorization (NMF), which can be only performed in the original data space, by conducting factorization within proper kernel space where the structure of data become much clear than the original data space. CF-based methods have been widely applied and yielded impressive results in optimal data representation and clustering tasks. However, CF methods still face with the problem of proper kernel function design or selection in practice. Most existing Multiple Kernel Clustering (MKC) algorithms do not sufficiently consider the intrinsic neighborhood structure of base kernels. In this paper, we propose a novel Discriminative Multiple Kernel Concept Factorization method for data representation and clustering. We first extend the original kernel concept factorization with the integration of multiple kernel clustering framework to alleviate the problem of kernel selection. For each base kernel, we extract the local discriminant structure of data via the local discriminant models with global integration. Moreover, we further linearly combine all these kernel-level local discriminant models to obtain an integrated consensus characterization of the intrinsic structure across base kernels. In this way, it is expected that our method can achieve better results by more compact data reconstruction and more faithful local structure preserving. An iterative algorithm with convergence guarantee is also developed to find the optimal solution. Extensive experiments on benchmark datasets further show that the proposed method outperforms many state-of-the-art algorithms.
Edge Based Mission Critical Session Control

Fifth generation (5G) mobile networks possess capabilities to address the strong requirements of mission critical communications for low latency, very high reliability and availability. Further improvement of network performance can be achieved by applying Multi-access Edge Computing (MEC) paradigm for distributing the computing and storage capabilities at the network edge. In this paper, we propose a new mobile edge service which provides notifications about session related events to applications and enables applications to determine the further session handling. The service, which follows Representational State Transfer style, is described by its resources and supported methods. The service performance is evaluated by emulation.
Smart Home Equipment Control System with Raspberry Pi and Yocto

Yocto is a customizable lightweight Linux distribution for embedded devices with any kind of hardware architecture. With the Yocto, it is possible to compile an operating system with the same feature for the different types of hardware in a very short period of time. This paper proposed to use the Yocto with the Raspberry Pi to form a high-speed Internet of Things (IoT) hardware cum software solution with the only specific features that we need. The very common Linux distribution for the Raspberry Pi has a number of extra features that are not necessary for some dedicated applications like IoT application, which needs to run only some specific set of tasks. Here in this project, we designed a prototype of a smart home equipment control system with the two different models of Raspberry Pi and this embedded Linux operating system called Yocto. This system is able to control all the home equipment from the control interface display or from the specific website world wise.
Different Head-Sway Responses to Optic Flow in Sitting and Standing With a Head-Mounted Display

We investigated postural responses (head displacements) and self-motion perception (vection) to radial and lateral optic flows while sitting and standing by using a head-mounted display. We found that head displacement directions varied across postures. In the standing posture, radial optic flow generally produced the opposed head displacement against the perceived vection direction, consistent with the literature; however, in the sitting posture, the optic flow generally produced the following head displacement in the vection direction. In the standing posture, responses were evident soon after the onset of the optic flow presentation but became less clear in the latter half of a trial. The results, while less clear for lateral flows, were similar for both flow types. Our findings suggest partially distinct processes underlying vection and postural control.
A Systematic Mapping Study on the Use of Motivational Theories in the Design of Motivational Software

Background: Motivation is a key issue in people’s lives. Motivated people are more likely to accomplish the goals they set out to achieve. For this reason, it is essential to motivate people in their lives. At present, the world of information and communications technologies (ICT) is evolving rapidly, and humans are used to interacting with a large number of software applications. Therefore, it would be valuable to endow these apps with a motivating function. Objective: To identify the main characteristics of motivational software products that are based on motivational strategies and how they are used in order to pinpoint patterns and relationships. Method: A systematic mapping study of software applications that motivate users to achieve their goals. Results: We have retrieved large number of studies applying and combining multiple motivational strategies, leading to different ways of motivating users. Conclusion: We have built a knowledge base based on the connections/relationships between the employed motivational strategies, participant characteristics, the target behaviour to be encouraged or discouraged, and the devices on which motivational software runs.
Towards a Retrospective One-Class Oriented Approach to Parents Detection in Social Media

Social media is the source of data for different purposes: advertisement, social study, human recruiting. However, usually, we are limited to readily available, structured information: age, gender, education, occupation. We have to work with unstructured data such as texts related to a user if we want to extract more complex, implicit features. We show the case of complex user analysis in social media using textual data. The task we solve is detecting parents on social networks. Our approach works with content that is not generated by a user, but with the content, the user was interested in implicitly - the user liked, or explicitly - the user subscribed to a group, where the content was published. In this paper, we compare classification methods for the task of parents detection on social media. Using mentioned above user’s likes and other information it is required to estimate chances if a user has got a child or children already or not. This task is an example of positive-unlabeled learning: data from social networks and media may contain explicit signals about users’ parenthood but there is no ground to make a backward conclusion. It can be considered as a case of look-a-like modelling or in other words a one-class classification problem. We propose a retrospective approach that can exploit data from social media to allow building a binary classifier. We compare both these approaches and conclude that the retrospective approach albeit requiring more efforts to be implemented may yield better results. This approach may be useful in similar tasks having look-a-like problem statement.
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In this paper, a novel benchmark is introduced for evaluating local image descriptors. We demonstrate limitations of the commonly used datasets and evaluation protocols, that lead to ambiguities and contradictory results in the literature. Furthermore, these benchmarks are nearly saturated due to the recent improvements in local descriptors obtained by learning from large annotated datasets. To address these issues, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and verification. This allows for more realistic, thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors is able to boost their performance to the level of deep learning based descriptors once realistic benchmarks are considered. Additionally we specify a protocol for learning and evaluating using cross validation. We show that when training state-of-the-art descriptors on this dataset, the traditional verification task is almost entirely saturated.
Índice de sinantropía, crecimiento y distribución de especies del género Physalis, Subgénero Rydbergis, sección Angulatae.

Synanthropy or weedy plants have been studied from taxonomic, floristic, ecological and biological points of view. In order to improve understanding of these species, we need a metric to measure their association with the habitats strongly modified by humans, that is, answer the question of “How weedy is the weed? ”. In this work we propose an index that combines characteristics of two previously published indices: it uses the simple calculus of Hart (1976), but combines it with the integration of various levels of synanthropy by Nuorteva (1963), in this case, four levels. We calculated the three indices for the 10 species of Physalis (Solanaceae), subgenus Rydbergis, section Angulatae, all of which are weedy to some degree, two are cultivated with weedy populations and all are native to Mexico. The data were provided by a well-curated dataset of herbarium specimens. Physalis philadelphica (one of the cultivated species) and Physalis acutifolia had the highest levels of synanthropy, and Physalis solanaceus, Physalis microcarpa and Physalis crassifolia had the lowest levels. Also, the slight differences in the results of the three indices mentioned were compared and discussed. In order to show an application of the index, we calculated the correlation of the index with various measures of frequency or commonness of the species. The data showed that weediness is not necessarily associated with being widespread or common, at least in its area of origin.
Role of Different Regions of α-synuclein in the Interaction with the Brain Fatty Acid DHA

α-Synuclein is a protein involved in Parkinson’s disease. Its interaction with brain polyunsaturated fatty acids is attracting a strong interest. Previously, we investigated the interaction ofiÂ€ α-syn with docosahexaenoic acid (DHA), and a mutual effect between the monomeric protein and the fatty acid was observed. α-Syn acquires an α-helical secondary structure with a rapid equilibrium between its free and the lipid bound form. In the α-helical form the protein segment 73-102 is flexible and is not characterized by a persistent structure. DHA forms oil droplets in the presence of the protein. To investigate at molecular level the involvement of different regions of the protein in the interaction with DHA, truncated protein species were prepared (syn1-99, syn1-52, syn57-102 and syn108-140) and their ability to interact with DHA was analyzed by circular dichroism and proteolytic mapping. CD data showed that all the peptides, except that corresponding to the C-terminal region, are able to acquire alpha-helical structure in the presence of DHA. The different molar ratio polypeptide/DHA necessary to reach the maximum folding and the different final helix content between the two N-terminal peptides (syn1-99, syn1-52) and the NAC peptide (syn57- 102) suggest that the sequence repeats plays an important role in the α-helix transition and thereby in the interaction with DHA. The proteolysis experiments showed that all the truncated species are resistant to proteolysis in the presence of saturating concentrations of DHA, indicating that almost all the residues are engaged in the interaction with the lipid. The C-terminal region, at variance, seems only to modulate the portion of the molecule buried into the lipid compartment. Moreover the ability of the peptides to affect the self-assembly of the fatty acid was also analyzed showing that the peptides that interact also change the physical state of DHA, by the positive charged N-terminus.
Research on Fault Detection for ZPW-2000A Jointless Track Circuit Based on Deep Belief Network Optimized by Improved Particle Swarm Optimization Algorithm

With the rapid development of railway traffic, traffic safety has become a focus. The ZPW-2000A jointless track circuit is an important part of train control systems. Currently, the fault detection of the ZPW-2000A jointless track circuit still relies on the experience of maintenance personnel, which can introduce several problems, such as a low fault detection efficiency and large amounts of required labor. Although some artificial intelligence fault detection algorithms for the ZPW-2000A track circuit have been developed, their detection accuracy is not high enough to meet the needs of large-scale applications, and due to security requirements, the actual ZPW-2000A track circuit fault data cannot be directly obtained in large quantities. To solve these problems, an equivalent theoretical model of the Chinese ZPW-2000A jointless track circuit is proposed by using four-terminal network theory. Through this equivalent theoretical model, the original fault data were collected. Considering that the relationship between fault data and fault types of the ZPW-2000A jointless track circuit is not obvious, a deep belief network was designed to detect the fault modes of the ZPW-2000A jointless track circuit. In order to optimize the deep belief network performance, the particle swarm optimization algorithm optimized by the genetic algorithm (GAPSO) was selected to optimize the deep belief network. The simulation experiments indicated that the optimized deep belief network could achieve a 98.5% fault detection accuracy and a 98.6% F1 Score rate, which showed that the deep belief network optimization by the particle swarm optimization algorithm which was optimized by the genetic algorithm (GAPSO-DBN) model proposed in this paper, had high accuracy and robustness. The results show that it had higher accuracy and robustness than other fault detection methods, and it can greatly improve the level of ZPW-2000A track circuit fault detection in the future.
Pulmonary Nodule Detection Using V-Net and High-Level Descriptor Based SVM Classifier

Early detection of the pulmonary nodule is critical to increase the five-year survival rate of lung cancer. Many computer-aided diagnosis (CAD) systems have been proposed for nodule detection to assist radiologists in diagnosis. Along this direction, this paper proposes a novel automated pulmonary nodule detection model using the modified V-Nets and a high-level descriptor based support vector machine (SVM) classifier. The former is for nodule candidate detection and the latter is for false positive (FP) reduction. A hard mining scheme for retraining is devised to improve the FP reduction performance. The proposed SVM classifier, which employs more critical features of CT images, performs superior in FP reduction than other SVM based classifiers and CNN classifiers. Experimental results using the LIDC-IDRI dataset are presented to demonstrate the effectiveness of the proposed CAD model.
Characterization of an Avian Protease Inhibitor

Background: Ovomucoid is a serine proteinase inhibitor in the egg whites of all avian species at a concentration of about 10 mg/ml. The involvement of proteinases in a multitude of control functions in an organism has created an interest in their physiological inhibitors. Regulation of proteolytic activity in tissues is a critical requirement in the maintenance of homeostasis. Egg white proteins possess ACE-inhibitory activity, & also high radical-scavenging activity. The combined antioxidant and ACE-inhibitory properties of egg white hydrolysates, or the corresponding peptides, would make a useful multifunctional preparation for the control of cardiovascular diseases. Proteases play key roles in several physiological processes, including intracellular protein degradation, bone remodeling, and antigen presentation, and their activities are increased in pathophysiological conditions such as cancer metastasis and inflammation. They are also required for invasion by microorganism. Four protease inhibitors have been identified in egg white: cystatin, ovomucoid, ovomacroglobulin (also known as ovostatin), and ovoinhibitor. Results & Conclusion: The fluorescence emission spectrum was 302 nm, comparable to that reported earlier. Stokes radius was found to be 2.91nm the value was comparable with white leghorn hen (stokes’ radius 3.15nm). The extraordinary large value of stokes radius can be attributed to its high carbohydrate content which increases the hydration of the molecule. The inhibitor had the molecular weight of 29,300 and the carbohydrate content was 22%, the specific extinction coefficient of duck ovomucoid was found to be 5.82 at 279 nm and the stokes radius was 2.91nm.
Helpless Priming Sends CD8+ T Cells on the Road to Exhaustion

Persistent antigen exposure in chronic infection and cancer has been proposed to lead to cytotoxic T lymphocyte (CTL) “exhaustion”, i.e., loss of effector function and disease control. Recent work identifies a population of poorly differentiated TCF-1+PD-1+ CD8+ T cells as precursors of the terminally exhausted CTL pool. These “predysfunctional” CTLs are suggested to respond to PD-1 targeted therapy by giving rise to a pool of functional CTLs. Supported by gene expression analyses, we present a model in which lack of CD4+ T cell help during CD8+ T cell priming results in the formation of predysfunctional CTLs. Our model implies that predysfunctional CTLs are formed during priming and that the remedy for CTL dysfunction is to provide “help” signals for generation of optimal CTL effectors. We substantiate that this may be achieved by engaging CD4+ T cells in new CD8+ T cell priming, or by combined PD-1 blocking and CD27 agonism with available immunotherapeutic antibodies.
Visible Light Communication (VLC) for 6G Technology: The Potency and Research Challenges

This paper aims to envision the potential use of visible light communications (VLC) for a 6G technological platform. We have identified the technical aspect of VLC as well as its research and regulatory challenges. It is argued that VLC is being considered for 6G is because it operates on a large bandwidth at Terahertz frequencies. Such an extra-wide bandwidth is expected to achieve a much higher data rate than the current 5G platform. However, methods of increasing data rates and reducing interference are among the research topics that need to be further investigated. In addition, a human-safe standard, an automotive standard, considerations for spurious RF emissions, and interference prevention on other RF spectrum bands are some of the VLC regulatory topics proposed for future study.