Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of UV and Gamma Irradiation Sterilization Processes in the Properties of Different Polymeric Nanoparticles for Biomedical Applications

The sterilization processes of nanoparticles (NP) by autoclaving and filtration are two of the most utilized methods in the pharmaceutical industry but are not always a viable option. For this reason, the search for alternative options such as UV and gamma radiation is of interest. In this work, we evaluated both types of sterilization on two types of NP in solid state widely employed in the literature for biomedical applications, poly-(ε-caprolactone) and poly(d,l-lactide-co-glycolide) acid NP stabilized with polyvinyl alcohol. Physicochemical properties and cell viability were studied pre- and post-sterilization. The efficiency of irradiation sterilization was performed by a test of sterility using 1 × 108 CFU/mL of Escherichia coli, Staphylococcus aureus, and Candida albicans. Microbiological monitoring revealed that both methods were sufficient for sterilization. After the UV irradiation sterilization (100 µJ/cm2), no substantial changes were observed in the physicochemical properties of the NP or in the interaction or morphology of human glial cells, though 5 and 10 kGy of gamma irradiation showed slight changes of NP size as well as a decrease in cell viability (from 100 µg/mL of NP). At 5 kGy of radiation doses, the presence of trehalose as cryoprotectant reduces the cell damage with high concentrations of NP, but this did not occur at 10 kGy. Therefore, these methods could be highly effective and low-processing-time options for sterilizing NP for medical purposes. However, we suggest validating each NP system because these generally are of different polymer-composition systems.
Maintenance of Alveolar Ridge Dimensions Utilizing an Extracted Tooth Dentin Particulate Autograft and Platelet-Rich fibrin: A Retrospective Radiographic Cone-Beam Computed Tomography Study

This study utilized radiographic comparative analysis in order to evaluate dimensional ridge changes four months after tooth extraction and immediate grafting with mineralized dentin particulate autograft and chopped platelet-rich fibrin. Fifty-eight extraction sockets with up to 2 mm of missing buccal bone in the coronal aspect compared to the lingual bone were included. Graft material was covered with either a platelet-rich fibrin membrane or collagen sponge with no effort to achieve primary closure. The dimensional changes of the ridge were assessed on cone-beam computed tomography (CBCT) images acquired prior to extraction and four months later. The reduction in the buccal bone plate thickness 1 mm, 3 mm, and 5 mm below the buccal crest was −0.87 ± 0.84 mm, −0.60 ± 0.70 mm, and −0.41 ± 0.55 mm, respectively. The mean ridge width changes 1 mm, 3 mm, and 5 mm below the crest were −1.38 ± 1.24 mm, −0.82 ± 1.13 mm, and −0.43 ± 0.89 mm, respectively. The average mid-buccal bone height gain was +1.1%, while the mid-lingual height gain was 5.6%. A mineralized dentin autograft with platelet-rich fibrin is effective in preserving post-extraction alveolar ridge dimensions.
In situ healing of dendrites in a potassium metal battery

Significance Historically, battery self-heating has been viewed negatively as an undesirable attribute. However, we report that battery self-heat, if properly controlled, can smoothen dendritic features in potassium metal batteries. This could open the door to high gravimetric and volumetric energy density potassium-ion batteries that could offer a sustainable and low-cost alternative to the incumbent lithium-ion technology. The use of potassium (K) metal anodes could result in high-performance K-ion batteries that offer a sustainable and low-cost alternative to lithium (Li)-ion technology. However, formation of dendrites on such K-metal surfaces is inevitable, which prevents their utilization. Here, we report that K dendrites can be healed in situ in a K-metal battery. The healing is triggered by current-controlled, self-heating at the electrolyte/dendrite interface, which causes migration of surface atoms away from the dendrite tips, thereby smoothening the dendritic surface. We discover that this process is strikingly more efficient for K as compared to Li metal. We show that the reason for this is the far greater mobility of surface atoms in K relative to Li metal, which enables dendrite healing to take place at an order-of-magnitude lower current density. We demonstrate that the K-metal anode can be coupled with a potassium cobalt oxide cathode to achieve dendrite healing in a practical full-cell device.
EVALUATION OF CORROSION OF ENGINEERING CERAMICS BY ATOMIC EMISSION SPECTROMETRY IN INDUCTIVELY COUPLED PLASMA

An analytical method has been developed and verified, facilitating chemical analysis of saline aqueous solutions from corrosion tests of two types of engineering ceramics, i.e. polycrystalline alumina, and silicon nitride. The method is capable of providing complementary information related to mechanisms of corrosion and kinetics of dissolution of the two main components of the ceramics, i.e. Al in α-Al2O3, and Si in Si3N4. A radially viewed inductively coupled plasma atomic emission spectroscopy was used, and the operating conditions for the analysis were optimised. The method was validated. Internal standardisation, matrix matching, standard addition technique and direct measurement without matrix correction were applied, and the results were critically discussed. The technique of internal standard was shown to be the most sensitive. The method exhibited satisfactory precision (relative standard deviation up to 5 %), analytical recoveries from 95 to 100 %, and acceptable limits of detection based on 3σ criterion of 0.095 mg l for Al (measured at 308.215 nm) and 0.099 mg∙l for Si (at 251.611 nm).
Data Compression Algorithms in FPGAs

Data compression is increasingly important as more and more data is produced, transferred and stored on a daily basis. It allows to reduce storage costs, speed up data transfers over limited bandwidth, reduce network congestion and even improve energy efficiency in wireless devices. In this work we propose a stream-based LZ77 hardware accelerator that implements the most computationally intensive part of many compression algorithms. The stream-based interface of this IP makes it easy to integrate with other hardware or software components to implement different compression applications. This IP was integrated into a hardware/software architecture that implements the widely used Deflate lossless compression algorithm. This architecture targets embedded computing systems using software and hardware components. The hardware component accelerates the LZ77 algorithm, while the software selects the best matches and codes them with static Huffman. Executing the software part on a generic processor allows for greater flexibility of the encoding part. A prototype implementation targeting a Zynq device demonstrated that the hardware accelerator can process 123 MiB/s and can easily be scaled in order to enhance the compression ratio. Compared with a software-only implementation running on the Zynq ARM processor a speedup of 1.5 is achieved. The proposed IP can also be used to implement a system that surpasses the throughput of Gzip running on a Intel Core i-5 computer by a factor greater than 2, for typical files.
Modelling and Control of a Linear Actuated Transfemoral Knee Joint in Basic Daily Movements

This paper presents the modelling and control of an actuated prosthetic knee mechanism for transfemoral amputees. The mechanism consists of a linear actuation system that feeds the mechanism with the required moment and power at different movements. Physical simulation was utilized to simulate and identify the all physical parameters of the knee. Particular pattern of the knee angle was used as a reference to test the behavior of the knee mechanism in terms of the angle and speed. Movements such as sittostand, slope climbing, and stair ascent were tested at different time intervals. PID control parameters were tuned while the angle of the actuated knee mechanism could track the desired angle at time period of 1 s and 0.1 s at different movements. However the mechanism showed deviation from the desired input at time periods of 0.05 s and 0.0125 s. In addition, the estimated amount of torque and power at time period less than 0.1s were about 15 N and 800 W. The physical simulation presented a realistic simulation of the actuated mechanism in terms of the knee parameters. Further analysis may be carried out during the development stage of the knee mechanism. Also, more experiments could be conducted with the transfemoral amputees to improve the overall performance of the knee mechanism.
Exploring Feedforward and Long Short Term Memory Neural Networks to Stock Markets Forecasting

—Artiﬁcial Neural Networks as a part of Machine Learning have been gaining momentum in the context of ﬁnancial prediction. Deep Neural Networks developments allow an increasing level of abstraction over the problem’s application, without loss of effectiveness and allowing for state-of-the-art performances. This thesis has a framework for creating Deep Neural Networks models applied to ﬁnancial forecasting, using Feedforward Neural Networks and Long Short-Term Neural Networks. The framework is conﬁgured with parameters that deﬁne all aspects, ranging from the data inputs, data pre-processing, model training and backtesting, producing a predicting model. This model is used in association with a deﬁned strategy with the results presented by a backtest and compared against a Buy and Hold strategy. The results show that it is possible to create simple proﬁtable strategies from securities with different behaviors by incorporating past data and technical indicators along with the price data. The semi-automated framework to train models and apply an investment strategy is able to beat the benchmark by 20% in the selected model and able to generate a model that reaches a cumulative return in the end of the period of 40% for the IBM security. For the AAPL security, an accuracy of 66% for the selected model is registered. The FLKS shows consistent high returns of 200% with less opened positions.
Training soldiers to calibration procedures in Virtual Reality, the FELIN IR sight use case

The infantrymen of the French army are equipped today with a combat system called FELIN, which includes an infrared sighting device: the IR sight. One of the first manipulations learnt by the soldier is the IR sight calibration. Currently, calibration training is a two-step procedure. The first step consists in practicing on a 2D WIMP software, and virtually practicing until making no mistakes. Then, if the soldier succeeds without mistakes, he can apply his knowledge in real situation on the shooting range. In this paper, we present a learning method that includes a prototype in Virtual Reality for training on the FELIN IR sight calibration procedure. We experimented it on real infantrymen learners in an infantry school. Results showed interesting added value of Virtual Reality in this specific use case. It improved the learners' motivation, learning efficiency and helped to identify specific mistake types not detected by the traditional learning software.
Comparison of Analytical Modeling and Numerical modeling of the role of Reverse Parameter using Differential Geometry in Differential Rate Equations of Tm-doped Tellurite Material

This paper analytical and numerical investigates the reverse cross relaxation parameter in differential rate equations of Tmdoped tellurite material using differential geometry principles with different dopant concentrations. The comparison between analytical simulation and numerical simulation indicates that there is difference in calculations in case of neglect the reverse cross relaxation parameters in analytical modeling compared with the simulation which respects reverse cross relaxation parameter in numerical modeling. The difference is calculated for a set of samples with concentration level variations by a factor of 30 (0.36 mol% to 10 mol%). This paper shows also the impact of neglect the reverse cross relaxation parameter on the population of Tm-doped tellurite material. It indicates that the role of reverse crosses relaxation impacts according to pump intensity and energy level and it have a significant effect on the simulation of laser and amplifier devices. General Terms Reverse cross relaxation parameter, Cross relaxation, Thulium doped material. Differential rate equations.
Comparision of central corneal thickness in pseudoexfoliation syndrome and pseudoexfoliation glaucoma

Introduction: To measure the central corneal thickness(CCT) in patients with Pseudoexfoliation Syndrome without glaucoma (PXS) and Pseudoexfoliation Syndrome with glaucoma(PXG) using ultrasonic pachymetry and compare the two. Materials and Methods: The study was conducted on 210 patients. 70 patients were with pseudoexfoliation syndrome, 70 were pseudoexfoliation glaucoma and 70 were healthy individuals. Central corneal thickness was measured using ultrasonic pachymetry. Results: Central corneal thickness is significantly thinner in patients with PXG (51522.94mm) than in patients with PXS (52521.22mm) and control group (52823.35mm) with p value Conclusion: The study shows that corneas are thinner in patients with pseudoexfolaition glaucoma (PXG) as compared to pseudoexfoliation syndrome without glaucoma (PXS) and controls (CNT). Keywords: Central corneal thickness, Pseudoexfolaition syndrome, Pseudoexfoliation glaucoma, Ultrasonic pachymetry.
Temporal changes in bird communities of wind-affected coniferous mountain forest in differently disturbed stands (High Tatra Mts., Slovakia)

In November 2004 a large windstorm affected forests dominated by Norway spruce ( Picea abies (L.) H. Karst, 1881) in the High Tatras National Park, Slovakia, and was followed by a wildfire (2005) and bark beetle outbreak. In this study we investigate temporal changes of breeding bird community (their species richness, density and diversity) as well as different nest-position guilds, in relation to disturbance type. Bird species composition was surveyed in two periods (2006 and 2007, and 2017 and 2018) using the point count method on four plots differing by disturbance type – undisturbed forest, unlogged windthrow, salvage logged windthrow and burnt salvage logged windthrow. A total of three visits per point and breeding season were performed at forty point count sites. We also assessed the successional shift in habitat characteristics between the two monitored periods. In total, 55 bird species and 2059 individuals were recorded on the plots during the study. We observed no effect of disturbance type on mean species richness, abundance and Simpson’s diversity per point; however, those characteristics did significantly increase over the study duration. While differently disturbed points had previously differed in their (nest-position) guild number and diversity, the differences between them disappeared over time. We found different trajectories of temporal change in the composition of the species communities and guild diversity in relation to disturbance type, but in a similar direction. During the first period, the greater the initial differences in the severity of disturbance among the disturbance types, the greater the differences were in the composition of bird communities; however, the differences gradually diminished as the forests regenerated. Alterations in habitat structure caused by natural and anthropogenic disturbance and by subsequent succession were the most probable reasons for the altered bird communities and observed patterns of recovery.
Food Safety Practice and Associated Factors among Meat Handlers in Gondar Town: A Cross-Sectional Study

Introduction. Animal sources of foods (ASF), including meat, are a source of high-quality nutrients. However, meat composition makes it an ideal medium for the growth of a good number of microorganisms. Around 600 million foodborne illnesses and 420000 deaths occur each year due to poor food handling practice. Thus, probing into meat handling practice will be an insatiable input for the intervention. This study aims to investigate the level of meat handling practice and associated factors among meat handlers in butcheries in Gondar town, Ethiopia. Method The study was a community-based cross-sectional study among butcher shops in Gondar town from April 20 to 30, 2019. Data were collected using a pretested structured questionnaire by trained data collectors among 214 meat handlers from butcher shops. Multivariable logistic regression analysis with a 95% confidence interval (CI) was used to identify the factors significantly associated with a good level of meat handling practice. Result More than half of the meat handlers 66.4% (95% CI: (59.8, 72.4)) in butcher shops had a good level of meat handling practice. Level of attitude (AOR = 4.45; 95% CI, 2.09–9.43) and knowledge (AOR = 2.04; 95% CI, 1.09–3.82) were significantly associated with a good level of meat handling practice. The majority of respondents wash their hands after disposing garbage (91.6%) with less vigilance after smoking, sneezing, or coughing (64.0%). Conclusion The study revealed that the level of food handling practice was unsatisfactory among meat handlers. This result is a testimony to the prevailing potential risk faced by consumers due to the disregarding of hygienic behaviors by food handlers. Considering attitude and knowledge are associated with the outcome variable, investing time on behavioral change activities that will contribute to the improvement of meat handler's attitude & practice, primarily focusing on reducing working while ill is essential. Therefore, much supervisory and coaching work will be expected from local health departments & regulatory bodies.
Segmenting Images with a Deep Auto-encoder and K-Means Clustering

—The purpose of this research is to improve some of the current methods of image segmentation, thereby allowing for more accurate results when categorizing images. By speciﬁcally venturing into image segmentation and neural networks, we hope to ﬁnd a collaborative and beneﬁcial correlation between convolutional neural networks and categorizing images. This research combines various segmentation methods and evaluation methods in hopes of creating a robust algorithm. By performing a particular technique of image segmentation, the desired product is to be capable of classifying local, global, and multi class images. In turn, this will constitute for an improved and more accurate way of segmenting images.
A Longitudinal Analysis of YouTube's Promotion of Conspiracy Videos

Conspiracy theories have flourished on social media, raising concerns that such content is fueling the spread of disinformation, supporting extremist ideologies, and in some cases, leading to violence. Under increased scrutiny and pressure from legislators and the public, YouTube announced efforts to change their recommendation algorithms so that the most egregious conspiracy videos are demoted and demonetized. To verify this claim, we have developed a classifier for automatically determining if a video is conspiratorial (e.g., the moon landing was faked, the pyramids of Giza were built by aliens, end of the world prophecies, etc.). We coupled this classifier with an emulation of YouTube's watch-next algorithm on more than a thousand popular informational channels to obtain a year-long picture of the videos actively promoted by YouTube. We also obtained trends of the so-called filter-bubble effect for conspiracy theories.
Quadrotor Stabilization using Visual Servoing

The objective of this work is to develop a position stabilization controller based on Visual Servoing alternatives for an existing quadrotor platform. We first introduce the quadrotor concept along with a mathematical model for its motion and the essential theory concerning computer vision. The quadrotor characteristics are then presented, including some tools developed in order to improve its capabilities, and the platform-specific parameters are estimated in order to create a SimuLink R © model for numerical simulations. Some of the available Visual Tracking alternatives are then described. The estimation alternatives of velocity and relative motion are also investigated. A validation of the visual tracking alternatives and the homography reconstruction is performed, and a comparative study is developed. Possible PBVS methods are tested in SimuLink R © in order to chose the most suitable alternative. Finally, a realtime implementation of the chosen PBVS scheme installed onboard the quadrotor is described, followed by a camera calibration and an assessment of estimation results. Keywords—Quadrotor, Visual Servoing, Homography, PBVS, Optical Flow.
Lessons learnt from 288 COVID-19 international cases: importations over time, effect of interventions, underdetection of imported cases

288 cases have been confirmed out of China from January 3 to February 13, 2020. We collected and synthesized all available information on these cases from official sources and media. We analyzed importations that were successfully isolated and those leading to onward transmission. We modeled their number over time, in relation to the origin of travel (Hubei province, other Chinese provinces, other countries) and interventions. We characterized importations timeline to assess the rapidity of isolation, and epidemiologically linked clusters to estimate the rate of detection. We found a rapid exponential growth of importations from Hubei, combined with a slower growth from the other areas. We predicted a rebound of importations from South East Asia in the upcoming weeks. Time from travel to detection has considerably decreased since the first importation, however 6 cases out of 10 were estimated to go undetected. Countries outside China should be prepared for the possible emergence of several undetected clusters of chains of local transmissions.
Pedestrian Detection with Multichannel Convolutional Neural Networks

—This work proposes an innovative method to approach the PD problem, based on the combination of heterogeneous input channel features obtained with Convolutional Neural Networks (CNNs). More speciﬁcally, a pre-trained CNN model is ﬁne-tuned for each single input channel (e.g. RGB or LUV) and high level features are extracted from the penultimate layer (right before the classiﬁcation layer). Finally, a multichannel input CNN model is trained (partially or fully) with these high level features. During test, the full images are pre-processed with the Aggregated Channels Features (ACF) detector in order to generate pedestrian candidate windows (i.e., windows potentially containing pedestrians). Next, these candidate windows are entered in the multichannel input CNN model, being effectively classiﬁed as pedestrians or non pedestrians. The developed method is competitive with other state of the art approaches, when evaluated on the INRIA dataset, achieving improvements over the baseline ACF method. Two experimental setups were adopted, namely, the full INRIA dataset with higher resolution and the partial INRIA dataset with lower resolution. Furthermore, the devised methodology can be successfully applied to the low resolution PD problem, and promisingly extended to other areas by integrating information from several inputs.