Miracle-dz/UniFAR-FTUs huggingface dataset:
Renewable Carbon Nanomaterials: Novel Resources for Dental Tissue Engineering

Dental tissue engineering (TE) is undergoing significant modifications in dental treatments. TE is based on a triad of stem cells, signaling molecules, and scaffolds that must be understood and calibrated with particular attention to specific dental sectors. Renewable and eco-friendly carbon-based nanomaterials (CBMs), including graphene (G), graphene oxide (GO), reduced graphene oxide (rGO), graphene quantum dots (GQD), carbon nanotube (CNT), MXenes and carbide, have extraordinary physical, chemical, and biological properties. In addition to having high surface area and mechanical strength, CBMs have greatly influenced dental and biomedical applications. The current study aims to explore the application of CBMs for dental tissue engineering. CBMs are generally shown to have remarkable properties, due to various functional groups that make them ideal materials for biomedical applications, such as dental tissue engineering.
Learn collision-free self-driving skills at urban intersections with model-based reinforcement learning

Intersection is one of the most complex and accident-prone urban traffic scenarios for autonomous driving wherein making safe and computationally efficient decisions with high-density traffic flow is usually non-trivial. Current rule-based methods decompose the decision-making task into several serial sub-modules, resulting in long computation time at complex scenarios for on-board computing devices. In this paper, we formulate the decision-making and control problem under intersections as a process of optimal path selection and tracking, where the former selects a path with the best safety measure from a set generated only considering static information, while the latter then considers dynamic obstacles and solve a tracking problem with safety constraints using the chosen path. To avoid the heavy computation introduced by that, we develop a reinforcement learning algorithm called generalized exterior point (GEP) to find a neural network (NN) solution offline. It first constructs a multi-task problem involving all the candidate paths and transforms it into an unconstrained problem with a penalty on safety violations. Afterward, the approximate feasible optimal control policy is obtained by alternatively performing gradient descent and enlarging the penalty. As an exterior point type method, GEP permits control policy to violate inequality constraints during the iterations. To verify the effectiveness of our method, we carried out experiments both in simulation and in a real road test. Results demonstrate that the learned policy can realize collision-free driving under different traffic conditions while reducing the computation time by a large margin.
Adherence to COVID-19 measures: The critical role of autonomous motivation on a short- and long-term basis.

To limit the spreading of the SARS-CoV-2-virus, governments worldwide have introduced behavioral measures that require considerable effort from their citizens to adhere to. Grounded in Self-Determination Theory, the present research sought to examine in a study among Belgian citizens the cross-sectional (total N = 45975, Mage = 50.42), week-to-week (subsample 1;N = 981, Mage = 41.32), and long-term (subsample 2;N = 5643, Mage = 53.09) associations between various individual motives to follow government guidelines and their self-reported adherence to these measures. Controlling for COVID-specific concerns and perceived risk for infection, autonomous motivation related positively to citizens' concurrent adherence (total sample), their increased week-to-week adherence during the lockdown (subsample 1), and their persistent adherence during an exit phase (subsample 2). Introjected regulation and external regulation were positively and negatively associated with concurrent adherence, respectively, but had no long-term predictive value. The findings indicate that citizens' autonomous motivation is a robust predictor of adherence, suggesting that politicians, policymakers, and experts do well to adopt a communication style that ongoingly fosters autonomous motivation. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
Extracellular Vesicles in Airway Homeostasis and Pathophysiology

The epithelial–mesenchymal trophic unit (EMTU) is a morphofunctional entity involved in the maintenance of the homeostasis of airways as well as in the pathogenesis of several diseases, including asthma and chronic obstructive pulmonary disease (COPD). The “muco-microbiotic layer” (MML) is the innermost layer of airways made by microbiota elements (bacteria, viruses, archaea and fungi) and the surrounding mucous matrix. The MML homeostasis is also crucial for maintaining the healthy status of organs and its alteration is at the basis of airway disorders. Nanovesicles produced by EMTU and MML elements are probably the most important tool of communication among the different cell types, including inflammatory ones. How nanovesicles produced by EMTU and MML may affect the airway integrity, leading to the onset of asthma and COPD, as well as their putative use in therapy will be discussed here.
Smart criminal justice: Phenomena and normative requirements

Advanced technology is not only transforming the public sector in general but is also increasingly transmuting the criminal justice system. Smart applications are designed to predict crimes, automate legal proceedings, and predict recidivism. We argue that such use of intelligent technology to optimize police work, criminal justice, and law enforcement is ultimately serving to establish a “smart criminal justice. ” However, not every use of technology is intelligent or “smart,” per se. Based on a literature analysis, we explore the meaning of “smartness” from a descriptive and normative perspective, in order to develop a catalog of decisive criteria. We identify the use of technology as a basic prerequisite, and efficiency, effectiveness, and participation as normative criteria for any smart initiative in the public sector. Considering the specifics of the criminal justice system, these criteria can be expanded by claims related to legality, equality, and transparency. In sum, this article presents and discusses guidelines for assessing the usefulness and legitimacy of technical innovations in the criminal justice system. The catalog developed here will facilitate practitioners and policy-makers in determining when the use of technology in criminal justice is actually smart, and more importantly, when it is not. Points for practitioners Advanced technology is increasingly being used in the public sector in general and the criminal justice system in particular, bringing opportunities as well as substantial risk. The use of technology has to adhere to normative principles to be considered as “smart criminal justice. ” This article presents a list of normative criteria that can be used in practice to assess whether technology in criminal justice is used smartly. Practitioners are encouraged to use this catalog as a guide for procurement, implementation, and evaluation processes.
COMPARATIVE DICLOFENAC DETECTION FOR CHRONIC TOXICITY LEVELS USING WATER SOLUBLE Zn-METALLOPORPHYRIN, GOLD NANOPARTICLES AND THEIR HYBRID

The optical detection of chronic toxicity levels of diclofenac were comparatively investigated using novel nanostructured materials, such as: a water soluble metalloporphyrin: Zn(II)-tetra-(4-N-methyl-4-pyridyl)porphyrin tetrachloride (ZnTMePyP), tailored size spherical gold nanoparticles (n-Au) of 15 20 nm, and the hybrid nanomaterial (ZnTMePyP/nAu) formed between them. After spectroscopic and microscopic analyses, it can be stated that the water soluble Zn-porphyrin alone can detect diclofenac in a larger domain of concentration (2.57 x 10 14.15 x 10 mol/L) than the hybrid formed between the metalloporphyrin and n-Au and that the gold nanoparticles alone are not capable of diclofenac detection.
Contribution of F0 Contour Level, F0 Contour Shape and Durations Towards Perception of Lombard Speech

Lombard speech, that is speech spoken in noise, has considerable changes in the speech production mechanism both at source level and system level compared to speech produced in normal/neutral speech. At source level, previous studies have shown that there is increase in fundamental frequency ( F 0 ), but it is unclear that the importance of F 0 contour shape and F 0 contour level in lombard speech. These perceptual studies carried out in this paper highlight the role of F 0 contour shape and F 0 contour level along with the durations. A set of six different experiments were conducted by modifying durations and both F 0 contour shape and level. These experiments are performed on samples from the Hurricane Challenge-2013 database. Perceptual results show that the contribution of F 0 contour level is more than F 0 contour shape and durations.
Shared-Nothing Distributed Enumeration of 2-Plexes

We present a novel approach for the detection of 2-plexes, a popular relaxation of cliques used for modeling network communities. Specifically, with the purpose of identifying theoretically sound methods for community detection on a large scale, we introduce the first shared-nothing distributed algorithm for this problem. This result opens a new research direction for scalable community detection. Our proposal has three main ingredients: (i) we reduce the problem of finding 2-plexes to that of finding cliques; (ii) we leverage known algorithms for fast computation of cliques; (iii) we exploit a decomposition technique for a distributed shared-nothing computation. Preliminary experiments on a 10-nodes cluster running Spark confirm the effectiveness of our approach.
Intentionality-related Deep Learning Method in Web Prefetching

Many prediction models have been proposed to improve the effectiveness of web prefetching for reducing the response time perceived by users when browsing the web. Most of these models are based on structure learning and are applied at the client side. Currently, considerable attention is being paid to proxy-based prefetching because it is more effective and accurate in predicting the correlated pages of many websites of similar interest for more homogeneous users. Compared with client-based prefetching, more complex prediction tasks must run in the proxy, which implies that a more powerful prediction model is required. Thus, based on the time-series characteristics of browsing records, we proposed the intentionality-related long short-term memory (Ir-LSTM) model, which combines both the Skip-Gram embedding method and the LSTM model while expanding the input features with user information. We also propose a novel dynamic allocation module for detecting real-time traffic bursts and correspondingly adjusting the correlation coefficient of the model’s output to achieve higher server-side resource utilization while fully maximizing hit ratio.
Calibration of the JUICE RWI Antennas by Numerical Simulation

The reception properties of the Radio Wave Instrument (RWI) onboard JUICE (Jupiter Icy Moons Explorer) have been determined using numerical methods applied to a mesh‐grid model of the spacecraft. The RWI is part of the RPWI (Radio and Plasma Wave Investigation) and consists of three perpendicular dipoles mounted on a long boom. We determined their effective lengths vectors and capacitive impedances of 8–9 pF. We also investigated the change in effective antenna angles as a function of solar panel rotation and calculated the directivity of the antennas at higher frequencies up to the maximum frequency of 45 MHz of the receiver. We found that the RWI dipoles can be used for direction‐finding with an accuracy of 2° up to a frequency of 1.5 MHz. Additionally we calculated the influence of strong pulses from the JUICE active radar on RPWI and found that they should do no harm to its sensors and receivers.
Microwave Imaging of the Neck by Means of Artificial Neural Networks for Tumor Detection

In this paper, a microwave imaging approach based on artificial neural networks (ANNs) for neck tumor detection is presented. The aim of this technique is to retrieve the geometric and dielectric properties of the neck to identify the possible presence of tumors, starting from scattered electric field data. A fully-connected neural network is developed to test the feasibility of the proposed approach. Moreover, a numerical model including the main features of a cross section of the neck is specifically designed in order to create a suitable training dataset. Subsequently, for the optimization of the ANN architecture and performance evaluation, a numerical analysis is conducted. A set of simulated cases, based on realistic neck phantoms, is tested to evaluate the robustness of the network. Preliminary results show the possibility to identify and locate neck tumors.
Liquid and Solid Functional Bio-Based Coatings

The development of new bio-based coating materials to be applied on cellulosic and plastic based substrates, with improved performances compared to currently available products and at the same time with improved sustainable end of life options, is a challenge of our times. Enabling cellulose or bioplastics with proper functional coatings, based on biopolymer and functional materials deriving from agro-food waste streams, will improve their performance, allowing them to effectively replace fossil products in the personal care, tableware and food packaging sectors. To achieve these challenging objectives some molecules can be used in wet or solid coating formulations, e.g., cutin as a hydrophobic water- and grease-repellent coating, polysaccharides such as chitosan-chitin as an antimicrobial coating, and proteins as a gas barrier. This review collects the available knowledge on functional coatings with a focus on the raw materials used and methods of dispersion/application. It considers, in addition, the correlation with the desired final properties of the applied coatings, thus discussing their potential.
Preparation of Ti3C2Tx MXene‐Derived Quantum Dots with White/Blue‐Emitting Photoluminescence and Electrochemiluminescence

MXene‐derived quantum dots (MQDs) have begun to attract much attention recently, but their most attractive features (i.e., optical properties) are still explored less than expected. In the present work, Ti3C2Tx MQDs with solvent‐determined optical characteristics and size distributions are prepared through solvothermal treatment of Ti3C2Tx nanosheets in various solvents. White‐emitting photoluminescence (PL) under 365 nm UV irradiation is obtained in dimethyl sulfoxide (DMSO), while blue in dimethylformamide (DMF) and ethanol, respectively. Moreover, the average diameters and quantum yields of the MQDs are 3.3 ± 0.2 nm and 10.7%; 2.5 ± 0.2 nm and 6.9%; as well as 1.8 ± 0.1 nm and 4.1% in DMF, ethanol, and DMSO, respectively. Interestingly, among a variety of metal ions, Fe3+ shows specific quenching effect toward the PL of the MQDs in DMF rather than in the other two. Moreover, electrochemiluminescence of the MQDs is observed using persulfate as coreactant for the first time. The possible mechanisms are discussed based on the combined influences of polarity, oxidation effect, and boiling point of the different solvents and the various characterizations.
On the Proof of Fixed-Point Convergence for Plug-and-Play ADMM

In most state-of-the-art image restoration methods, the sum of a data-fidelity and a regularization term is optimized using an iterative algorithm such as ADMM (alternating direction method of multipliers). In recent years, the possibility of using denoisers for regularization has been explored in several works. A popular approach is to formally replace the proximal operator within the ADMM framework with some powerful denoiser. However, since most state-of-the-art denoisers cannot be posed as a proximal operator, one cannot guarantee the convergence of these so-called plug-and-play (PnP) algorithms. In fact, the theoretical convergence of PnP algorithms is an active research topic. In this letter, we consider the result of Chan et al. (IEEE TCI, 2017), where fixed-point convergence of an ADMM-based PnP algorithm was established for a class of denoisers. We argue that the original proof is incomplete, since convergence is not analyzed for one of the three possible cases outlined in the letter. Moreover, we explain why the argument for the other cases does not apply in this case. We give a different analysis to fill this gap, which firmly establishes the original convergence theorem.
From Primary MSC Culture of Adipose Tissue to Immortalized Cell Line Producing Cytokines for Potential Use in Regenerative Medicine Therapy or Immunotherapy

For twenty-five years, attempts have been made to use MSCs in the treatment of various diseases due to their regenerative and immunomodulatory properties. However, the results are not satisfactory. Assuming that MSCs can be replaced in some therapies by the active factors they produce, the immortalized MSCs line was established from human adipose tissue (HATMSC1) to produce conditioned media and test its regenerative potential in vitro in terms of possible clinical application. The production of biologically active factors by primary MSCs was lower compared to the HATMSC1 cell line and several factors were produced only by the cell line. It has been shown that an HATMSC1-conditioned medium increases the proliferation of various cell types, augments the adhesion of cells and improves endothelial cell function. It was found that hypoxia during culture resulted in an augmentation in the pro-angiogenic factors production, such as VEGF, IL-8, Angiogenin and MCP-1. The immunomodulatory factors caused an increase in the production of GM-CSF, IL-5, IL-6, MCP-1, RANTES and IL-8. These data suggest that these factors, produced under different culture conditions, could be used for different medical conditions, such as in regenerative medicine, when an increased concentration of pro-angiogenic factors may be beneficial, or in inflammatory diseases with conditioned media with a high concentration of immunomodulatory factors.
Wear Analysis of an Advanced Al–Al2O3 Composite Infiltrated with a Tin-Based Alloy

In this study, a hybrid material is produced, and the effect of different loads varying from 40 to 60 N against an EN-31 steel counter disk on its wear behavior under dry sliding conditions at room temperature is studied. The tribological behavior is studied via the pin-on-disk method and analyzed using primary wear parameters, such as the coefficient of friction (COF), mass wear, and specific wear rate. The obtained results are compared with the results for B83 babbitt under the same wear test conditions. Microstructural observation with scanning electron microscopy (SEM) is performed along with X-ray energy dispersive spectroscopy (EDX) for chemical analysis conduction. The results from the wear experiments indicate that the hybrid material possesses a lower COF, mass wear, and specific wear rate as well as a higher wear resistance in comparison to the B83 babbitt specimen when subjected to the same test conditions. The results from the wear experiments indicate that by applying different loads of 40, 50, and 60 N, the hybrid material possesses a lower mass wear, specific wear rate, and COF specifically at a load of 40 N in comparison to the B83 babbitt specimen under the same test conditions. It was also observed that by increasing the load under dry sliding friction, the hybrid material increases its mass wear and specific wear rate.
A Photo-Based Mobile Crowdsourcing Framework for Event Reporting

Mobile Crowdsourcing (MCS) photo-based is an arising field of interest and a trending topic in the domain of ubiquitous computing. It has recently drawn substantial attention of the smart cities and urban computing communities. In fact, the built-in cameras of mobile devices are becoming the most common way for visual logging techniques in our daily lives. MCS photo-based frameworks collect photos in a distributed way in which a large number of contributors upload photos whenever and wherever it is suitable. This inevitably leads to evolving picture streams which possibly contain misleading and redundant information that affects the task result. In order to overcome these issues, we develop, in this paper, a solution for selecting highly relevant data from an evolving picture stream and ensuring correct submission. The proposed photo-based MCS framework for event reporting incorporates (i) a deep learning model to eliminate false submissions and ensure photos credibility and (ii) an A-Tree shape data structure model for clustering streaming pictures to reduce information redundancy and provide maximum event coverage. Simulation results indicate that the implemented framework can effectively reduce false submissions and select a subset with high utility coverage with low redundancy ratio from the streaming data.
Physical Activity, Screen Time, Sedentary and Sleeping Habits of Polish Preschoolers during the COVID-19 Pandemic and WHO’s Recommendations: An Observational Cohort Study

Background: Restrictions related to the COVID-19 pandemic may lead to a significant decrease in physical activity, an increase in sedentary behavior, and thus also such things as screen time or a change in health behavior patterns. The survey aimed to compare levels of physical activity, screen time, hours spent sitting and sleeping time among Polish children aged 3–5 years of age before and during the COVID-19 pandemic. Methods: We identified 3000 respondents under five years of age, at Polish kindergartens. The questionnaire consists of 62 questions according to the recommendations of health behavior in school-aged children. The questionnaire was completed by the parents of these children. Results: Only 30.77% of children complied with WHO criteria before the pandemic. During the pandemic, the percentage of children meeting the recommendations for physical activity decreased even more. Children spent much more time in a sitting position before the restrictions. The children slept as recommended 10–13 h a day, and the pandemic caused an increase in sleep duration of 10–18%. Most children had a limited time allowed for the use of electronic devices already before the pandemic, but during the pandemic the results negatively decreased by 71.54%. Conclusions: The results clearly indicate decreased physical activity and increased screen time. It is also crucial to develop recommendations for prevention management strategies of sedentary lifestyles in the youngest group.