Miracle-dz/UniFAR-FTUs huggingface dataset:
Equilibrium properties of inhomogeneous partially-magnetized plasma containing negative ions

This paper presents an analytical model for inhomogeneous, partially-magnetized electronegative plasma. It is shown that the radial potential of the plasma can be related to the electronegativity parameter α=nv/ne , the positive ion density, and the electron temperature distribution inside the plasma system. Using this expression, the electronegativity parameter has been determined in an oxygen plasma produced by a 13.56 MHz capacitive coupled radio-frequency discharge, and it is shown that the negative ion parameter obtained using this model is in good qualitative agreement with the saturation current ratio technique based on the cylindrical Langmuir probe.
Polymyxin Induces Significant Transcriptomic Perturbations of Cellular Signalling Networks in Human Lung Epithelial Cells

Inhaled polymyxins are increasingly used to treat pulmonary infections caused by multidrug-resistant Gram-negative pathogens. We have previously shown that apoptotic pathways, autophagy and oxidative stress are involved in polymyxin-induced toxicity in human lung epithelial cells. In the present study, we employed human lung epithelial cells A549 treated with polymyxin B as a model to elucidate the complex interplay of multiple signalling networks underpinning cellular responses to polymyxin toxicity. Polymyxin B induced toxicity (1.0 mM, 24 h) in A549 cells was assessed by flow cytometry and transcriptomics was performed using microarray. Polymyxin B induced cell death was 19.0 ± 4.2% at 24 h. Differentially expressed genes (DEGs) between the control and polymyxin B treated cells were identified with Student’s t-test. Pathway analysis was conducted with KEGG and Reactome and key hub genes related to polymyxin B induced toxicity were examined using the STRING database. In total we identified 899 DEGs (FDR < 0.01), KEGG and Reactome pathway analyses revealed significantly up-regulated genes related to cell cycle, DNA repair and DNA replication. NF-κB and nucleotide-binding oligomerization domain-like receptor (NOD) signalling pathways were identified as markedly down-regulated genes. Network analysis revealed the top 5 hub genes (i.e., degree) affected by polymyxin B treatment were PLK1 (48), CDK20 (46), CCNA2 (42), BUB1 (40) and BUB1B (37). Overall, perturbations of cell cycle, DNA damage and pro-inflammatory NF-κB and NOD-like receptor signalling pathways play key roles in polymyxin-induced toxicity in human lung epithelial cells. Noting that NOD-like receptor signalling represents a group of key sensors for microorganisms and damage in the lung, understanding the mechanism of polymyxin-induced pulmonary toxicity will facilitate the optimisation of polymyxin inhalation therapy in patients.
A novel 5-DOF exoskeletal rehabilitation robot system for upper limbs

A novel 5-DOF exoskeletal rehabilitation robot for upper limbs of hemiplegic patients caused by stroke is proposed in this paper. Its hardware structure is introduced and the control methods are analyzed. To implement intelligent and interactive rehabilitation exercises, motion intention of patients' upper limb is introduced into control methods of rehabilitation exercises. In passive motions, according to the character of unilateral impaired, multi-channels surface electromyogram (sEMG) signals of patients' healthy arm muscles are acquired and analyzed to recognize the upper limb motions, then drive the robot and assist paralysis arm's rehabilitation exercises. In active-resistant motions, because patients are recovered with some muscle forces and active motion ability after a rehabilitation period, the terminal force loaded on the robot by an impaired arm are estimated with multi-channel joint torque sensors, according to which, the terminal velocity of the robot is controlled to drive the joint motions with a damp controller.
Further Evidence of the Melatonin Calmodulin Interaction: Effect on CaMKII Activity

Melatonin (MEL) is a pleiotropic indolamine that reaches multiple intracellular targets. Among these, MEL binds to calmodulin (CaM) with high affinity. In presence of Ca2+, CaM binds to CaM-dependent kinase II (CaMKII). The Ca2+-CaM/CaMKII pathway regulates a myriad of brain functions in different cellular compartments. Evidence showing the regulation of this cellular pathway by MEL is scarce. Thus, our main objective was to study the interaction of MEL with CaM and its effects on CaMKII activity in two microenvironments (aqueous and lipidic) naturally occurring within the cell. In addition, colocalization of MEL with CaM in vivo was explored in mice brain hippocampus. In vitro CaM-MEL interaction and the structural conformations of CaM in the presence of this indoleamine were assessed through electrophoretic mobility and isoelectric point. The functional consequence of this interaction was evaluated by measuring CaMKII activity. Ca2+-CaM-MEL increased the activity of CaMKII in aqueous buffer but reduced the kinase activity in lipid buffer. Importantly, MEL colocalizes in vivo with Ca2+-CaM in the hippocampus. Our evidence suggests that MEL regulates the key cellular Ca2+-CaM/CaMKII pathway and might explain why physiological MEL concentrations reduce CaMKII activity in some experimental conditions, while in others it drives biological processes through activation of this kinase.
A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper proposes a lightweight CNN classification model based on transfer learning. Use the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing power. In order to alleviate the problem of model overfitting caused by insufficient data set, transfer learning is used to train the model. The study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network, and then retrain the model based on the CT image data set provided by Kaggle. Experimental results on a computer equipped only with the Central Processing Unit (CPU) show that it consumes only 1.06 s on average to diagnose a chest CT image. Compared to other lightweight models, the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters, which can be easily applied to computers without GPU acceleration. Code:github.com/ZhouJie-520/paper-codes. © 2022 Tech Science Press. All rights reserved.
Towards a large scale indoor localization service with crowdsensing indoor map generation

Knowing self location matters a lot in people’s daily life. While Global Positioning System (GPS) provides almost perfect solution for outdoor area, it would not work in indoor areas because of no line-of-sight to satellites. However, since human tend to spend more and more time in complexly constructed buildings, helping people localize themselves in indoor space become a critical problem. Raising localization accuracy and reducing deployment cost are two main objects in indoor localization problem. High localization accuracy ensures usability of service, while low deployment cost lessens the effort people must take to use localization service. Numerous technologies have been proposed to tackle the problem. However, practical indoor localization that can provide high localization accuracy with minimum cost is still a vacancy. In the first part of this thesis, we devote ourself to explore the possibility of fingerprint based localization. Although a large number of fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. Motivated by the obtained insights from field studies with GMI, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on Ambient Magnetic Field fingerprints, which is formed by “twisted” geomagnetic field by building structures, that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience. In our experiments with ambient magnetic field fingerprint, we see that scalability of ambient magnetic field based approach is not satisfactory comparing to WiFi based approaches. By further exploration, we find that dual properties naturally existed in ambient magnetic field fingerprint and WiFi fingerprint. Therefore based on GROPING, we present MaWi a
Enhancement of Physiological Stress Classification using Psychometric Features

Psychological data features are underutilized in many acute stress studies since they are challenging to replicate and validate due to their inherent subjectivity. However, psychology and perception play essential roles in stress research according to the well-established allostatic load model. Therefore, we demonstrate the importance of accounting for psychological data in acute stress research in an ambulatory setting through a joint analysis. We enhanced stress classification by combining psychometric features with standard physiological signal features. We used the publicly available Wearable Stress and Affect Database (WESAD), from which we obtained physiological signals and psychological self-assessments from 15 participants. For each participant, a set of physiologically relevant features were extracted from each signal type. In parallel, we adapted psychometric features, positive emotion (PEscore) and negative emotion (NEscores) scores, by calculating the weighted average of self-evaluation scores. Using a stepwise feature selection and a lineardiscriminant-analysis-based classifier, we found that PEscores, along with select physiological signal features, could enhance cross-validated stress classification accuracy by 8%, higher than a previous benchmark study using the same dataset. More importantly, we found that such a classification accuracy could be achieved with significantly fewer physiological signal features (by 20 times) with the aid of a psychometric feature. Finally, we found that psychometric features could indicate the type of perceived stress relating to an individual’s mood descriptor scores. Thus, a combination of psychometric and physiological data could be beneficial towards improving the detection and management of stress and support the development of holistic stress models.
A Deep Multitask Convolutional Neural Network for Remote Sensing Image Super-Resolution and Colorization

Remote sensing data have become increasingly vital in target detection, disaster monitoring, and military surveillance. Abundant pan-sharpening and super-resolution (SR) methods based on deep learning have been proposed and have achieved remarkable performance. However, pan-sharpening requires paired panchromatic (PAN) and multispectral (MS) images, and SR cannot increase the spectral resolution of PAN. Thus, we introduce a computational imaging-based method to recover or produce the incomplete data of single PAN or MS. This work also explores the integration of multiple tasks by a single neural network. We start with SR and colorization, study the feasibility of simultaneously finishing SR colorization, and use a model trained in SR colorization to finish pan-sharpening without MS. A generic neural network, remote sensing image improvement network (RSI-Net), is designed for remote sensing image SR, colorization, simultaneous SR colorization, and pan-sharpening. To verify its performance, RSI-Net is compared with the state-of-the-art SR and colorization methods. Experiments show that RSI-Net can be competitive in visual effects and evaluation indexes, and it performs well at simultaneous SR colorization, and RSI-Net finishes pan-sharpening and only needs to input PAN. Our experiments confirm the effect of integrating multiple tasks.
Hypo-angiogenesis: a possible pathological factor in the development of dry age-related macular degeneration and a novel therapeutic target

Background: Angiogenesis causes severe vision loss in patients with exudative or wet forms of age-related macular degeneration (AMD). The pathogenesis involves upregulation of several proangiogenic factors, particularly the vascular endothelial growth factor (VEGF). Contrary to the pathogenesis of exudative AMD, molecular events leading to the development of dry AMD remain unclear. Dry AMD is characterized by loss of the retinal pigment epithelium (RPE). The mechanism that triggers RPE cell loss remains unclear. Choriocapillaris development is absent in mice with RPE-specific deletion of VEGF. Moreover, in later life, background VEGF secretion promotes the survival of the RPE and maintains choriocapillaris integrity. Hypothesis: We hypothesized that reduced synthesis of VEGF (hypo-angiogenesis) or abnormalities in its receptors, VEGF receptor-1 (VEGFR1) and VEGFR2, may be involved in the pathogenesis of non-exudative AMD or dry AMD. If the concept of hypo-angiogenesis as a driver for dry AMD is proven, treatment with VEGF or induction of angiogenesis could be considered. Similar attempts at therapeutic angiogenesis have been actively investigated in cardiac and limb ischemia. Conclusions: The reasons for a patient developing exudative AMD or dry AMD remain poorly understood. Nevertheless, targeting increased VEGF production in patients with exudative AMD using anti-VEGF drugs is highly efficacious in preserving vision. Similarly, dry AMD may be a manifestation of reduced VEGF synthesis (hypo-angiogenesis) and subsequent decreased RPE cell survival. Experimental studies exploring the possibility of reduced VEGF secretion and/or increased receptor resistance/abnormality could pave the way for clinical trials of angiogenesis to treat dry AMD.
Actively Learning Gaussian Process Dynamical Systems Through Global and Local Explorations

Usually learning dynamical systems by data-driven methods requires large amount of training data, which may be time consuming and expensive. Active learning, which aims at choosing the most informative samples to make learning more efficient is a promising way to solve this issue. However, actively learning dynamical systems is difficult since it is not possible to arbitrarily sample the state-action space under the constraint of system dynamics. The state-of-the-art methods for actively learning dynamical systems iteratively search for an informative state-action pair by maximizing the differential entropy of the predictive distribution, or iteratively search for a long informative trajectory by maximizing the sum of predictive variances along the trajectory. These methods suffer from low efficiency or high computational complexity and memory demand. To solve these problems, this paper proposes novel and more sample-efficient methods which combine global and local explorations. As the global exploration, the agent searches for a relatively short informative trajectory in the whole state-action space of the dynamical system. Then, as the local exploration, an action sequence is optimized to drive the system’s state towards the initial state of the local informative trajectory found by the global exploration and the agent explores this local informative trajectory. Compared to the state-of-the-art methods, the proposed methods are capable of exploring the state-action space more efficiently, and have much lower computational complexity and memory demand. With the state-of-the-art methods as baselines, the advantages of the proposed methods are verified via various numerical examples.
Damage Detection in Bridge Structure Using Vibration Data under Random Travelling Vehicle Loads

Due to the random nature of the road excitation and the inherent uncertainties in bridge-vehicle system, damage identification of bridge structure through continuous monitoring under operating situation become a challenge problem. Methods for system identification and damage detection of a continuous two-span concrete bridge structure in time domain is presented using interaction forces from random moving vehicles as excitation. The signals recorded in different locations of the instrumented bridge are mixed with signals from different internal and external (road roughness) vibration sources. The damage structure is also modelled as the stiffness reduction in one of the beam element. For the purpose of system identification and damage detection three different output-only modal analysis techniques are proposed: The covariance-driven stochastic subspace identification (SSI-COV), the blind source separation algorithms (called Second Order Blind Identification) and the multivariate AR model. The advantages and disadvantages of the three algorithms are discussed. Finally, the null-space damage index, subspace damage indices and mode shape slope change are used to detect and locate the damage. The proposed approaches has been tested in simulation and proved to be effective for structural health monitoring.
A Multimodal Sensory Apparatus for Robotic Prosthetic Feet Combining Optoelectronic Pressure Transducers and IMU

Timely and reliable identification of control phases is functional to the control of a powered robotic lower-limb prosthesis. This study presents a commercial energy-store-and-release foot prosthesis instrumented with a multimodal sensory system comprising optoelectronic pressure sensors (PS) and IMU. The performance was verified with eight healthy participants, comparing signals processed by two different algorithms, based on PS and IMU, respectively, for real-time detection of heel strike (HS) and toe-off (TO) events and an estimate of relevant biomechanical variables such as vertical ground reaction force (vGRF) and center of pressure along the sagittal axis (CoPy). The performance of both algorithms was benchmarked against a force platform and a marker-based stereophotogrammetric motion capture system. HS and TO were estimated with a time error lower than 0.100 s for both the algorithms, sufficient for the control of a lower-limb robotic prosthesis. Finally, the CoPy computed from the PS showed a Pearson correlation coefficient of 0.97 (0.02) with the same variable computed through the force platform.
The socio-economic approach to management revisited. The Evolving Nature of SEAM in the 21st Century

This volume is part of the ongoing collaboration between the RMC series and the Socio-Economic Institute for Firms and Organization (ISEOR), a French intervention-research think tank codirected by Henri Savall and Veronique Zardet. Building on an earlier collaboration on the ISEOR approach - Socio-Economic Intervention in Organizations: The Intervener-Researcher and the SEAM Approach to Organizational Analysis (IAP, 2007) – Buono and Savall bring together over 30 talented intervener-researchers to explore and examine the ongoing evolution of the Socio-Economic Approach to Management (SEAM). This volume revisits the application of SEAM in the context of intervention challenges in the wake of the recent economic crisis and the disruptive change that has taken hold across the world. The basic foundation of SEAM – built on the idea of strategic patience, the need to undertake holistic intervention in organizations, and the challenge to get organizational members to listen to themselves (through what they refer to as the mirror effect) – has remained the same. In response to economic and organizational pressures in the current environment, however, there has been a concomitant emphasis on helping client organizations achieve short-term results while still maintaining focus on the long term. Many ideas that have become part of the current discourse within ISEOR today were not as explicitly addressed in the initial volume – from the destructive effect of the Taylorism-Fayolism-Weberism (TFW) virus, to the need to focus on ways to ensure the sustainability of a SEAM intervention, the growing importance of collaborative interactions between external and internal consultants, and the growing importance of cocreating knowledge with client firms and organizations.
Development of an Information System for the Modern Business Environment, Adapted to SMEs

The paper presents the development of special information systems for the modern business environment by including new functions and modules developed based on last generation technical means. The research results are of great value especially for SMEs, which have the capability of best adapting to such an evolution of the information system, and, by their particularities, are the most motivated in approaching such an improvement strategy of their own information system. The research proved the possibility to develop a completely new integrated information system, at the highest technological level, aiming to completely support all the information processing in a SME.
Historical Prevalence of Infectious Diseases and Entrepreneurship: The Role of Institutions in 125 Countries

This study examines the effects of the historical prevalence of infectious diseases on contemporary entrepreneurship. Previous studies reveal the persistence of the effects of historical diseases on innovation, through the channel of culture. Drawing on the epidemiological origin of institutions, we propose a framework which argues that the impact of infectious disease prevalence on contemporary entrepreneurship is mediated by property rights. The central hypothesis posits that a guarantee of property rights reduces the effect of past diseases on entrepreneurship. Using data from 125 countries, we find strong and robust evidence on the proposed hypothesis and other results. Property rights are higher in countries where the prevalence of diseases was low, which leads to good entrepreneurship scores. In contrast, countries with high disease prevalence did not have time to develop strong institutions to secure property rights. This explains their low level of entrepreneurship today. These results are robust to alternative methods and measures of property rights. Furthermore, our results also confirm the level of development, culture and the digitalization of economies as transmission channels between past diseases and the current level of entrepreneurship.
Theoretical Model Study of Radioisotope Microbattery Based on β-Radio-Voltaic Effect

As the developing of MEMS(micro electromechanical system),micropower becomes a very key problem in MEMS application gradually.The paper accounts simultaneously for the choice and decay type of isotope,particle energy spectrum,the energy loss rate of emitted particle in the mass,semiconductor characteristic and generation and recombination of carriers and sets up a theoretical model of radioisotope microbattery based on β-radio-voltaic effect.The model can calculate short circuit current and open circuit voltage of microbattery.The simulated result is slightly bigger than literature results;doped density of diffu-sion layer,the diffusion depth and radiation particle activity respectively influences export parameters of bat-tery.The model is considerably instructional and useful for developing microbattery.