Miracle-dz/UniFAR-FTUs huggingface dataset:
NON-CONTACT PULSE RATE MEASUREMENT USING FACIAL VIDEOS

Pulse rate (PR) is one of the vital physiological parameters which indicates the physiological state of individuals thus proving to be an important parameter to be monitored. In the last decade, more emphasis is given to non-contact based systems that are low-cost and are easy to use. Despite these advancements, most of these systems are suitable for a lab environment in offline situations. This project presents an effective system for the estimation of a pulse rate from facial videos. A dataset of 160 videos with pulse rate has been introduced. The dataset is obtained from 20 subjects performing 4 activities in 2 lighting conditions. Each activity is captured by a smartphone camera placed on a tripod. This dataset with facial videos and pulse rate is trained on different Convolutional Neural Network (CNN) models to predict the pulse rate. Their performances were compared to obtain better results. Another method called Eulerian video magnification (EVM) was also implemented with the same dataset and the results were compared with the CNN results for better accuracy. This technology possesses a high potential in advancing personal health care and in the field of telemedicine. Additional improvements to the proposed system with regards to movement and illumination can prove to be useful in many real-time applications.
Dissomic disorders associated with juvenile rheumatoid arthritis: impact on quality of life

Dyssomnic disorders (DD) associated with juvenile rheumatoid arthritis (JRA) are some of the most common conditions that are difficult to endure and that lead to deconditioning. This study aimed to assess prevalence and structure of DD, their relationship with clinical picture peculiarities and contribution to deterioration of the quality of lives of JRA patients. At the 1st stage, we assessed prevalence of DD in a continuous sample of JRA patients and healthy children aged 8–16 years. At the 2nd stage, we assessed DD structure, features associated with gender and age, connections to the key clinical characteristics of JRA and quality of life of the patients. In the context of the study, we used the SDSC sleep quality scale, the PedsQL 4.0 quality of life model, and the Ritchie index. DD develop in JRA patients 3.3 times more often than in healthy children (in 178 (72.3%) and 93 (22.2%) children, respectively). The DD registered were sleep initiation and maintenance disorders (54 cases, 22.0%), respiratory disorders (32 cases, 13.0%), sleep-to-wakefulness transition disorders (31 cases, 12.6%), excessive sleepiness disorders (38 cases, 15.4%), combinations thereof (23, 9.3%). Girls had sleep initiation and maintenance disorders more pronounced (p = 0.003), boys were more prone to excessive sleepiness (p = 0.008). The severity of DD increases with patients' age (r = 0.69; p = 0.001) and JRA onset age (r = 0.71; p = 0.001); they are also more severe in polyarticular JRA patients (r = 0.32; p = 0.048). We have clarified the relationship between DD and indicators of inflammatory (r = 0.56; p = 0.001) and Ritchie indices (r = 0.44; p = 0.005), duration of morning stiffness (r = 0.49; p = 0.029). The proven connection between DD and JRA entails the need for routine checks for DD in such patients, and, when discovered, DD should call for personalized therapeutic and diagnostic approach rather than be regarded as one of the JRA syndromes.
Theorising the ‘Security Influencer’: Speaking security, terror and Muslims on social media during the Manchester bombings

Security studies literature neglects social media’s potential for lay actors to become influential within security debates. This article develops the concept of ‘security influencers’, bringing literature from marketing into the security debate to understand how social media enables individuals to ‘speak’ and contest security and how lay actors exert influence. Methodologically, this article applies a multi-methods approach to 27,367 tweets to identify and analyse the top four most influential actors in 48 hours following the 2017 bombings by keywords ‘Manchester’ and ‘Muslims’. This article builds a typology of security influencers nuancing definitions of the passive ‘security broadcaster’ and the active ‘security engager’, both of which emerge from obscurity or influence within non-security domains. Furthermore, a dichotomy emerges within influential messages and contestation; messages discussing Muslims in banal terms as diverse individuals register high levels of agreement, whereas those discussing Islam as a world religion receive more hostility and contestation.
Hiring and Intra-occupational Gender Segregation in Software Engineering

Women tend to be segregated into different subspecialties than men within male-dominated occupations, but the mechanisms contributing to such intra-occupational gender segregation remain obscure. In this study, I use data from an online recruiting platform and a survey to examine the hiring mechanisms leading to gender segregation within software engineering and development. I find that women are much more prevalent among workers hired in software quality assurance than in other software subspecialties. Importantly, jobs in software quality assurance are lower-paying and perceived as lower status than jobs in other software subspecialties. In examining the origins of this pattern, I find that it stems largely from women being more likely than men to apply for jobs in software quality assurance. Further, such gender differences in job applications are attenuated among candidates with stronger educational credentials, consistent with the idea that relevant accomplishments help mitigate gender differences in self-assessments of competence and belonging in these fields. Demand-side selection processes further contribute to gender segregation, as employers penalize candidates with quality assurance backgrounds, a subspecialty where women are overrepresented, when they apply for jobs in other, higher-status software subspecialties.
Dissociation Rate of THF-methane Hydrates

Abstract A number of papers and research projects suggest that stranded natural gas can be transported in a solid hydrate state at higher temperatures or lower pressures compared to conventional transportation systems (LNG and CNG). The self-preservation effect of methane hydrate can probably be improved by the use of a third component besides CH4 and water. Tetrahydrofuran (THF) is a promoter that greatly reduces the required formation pressures. In the present work the influence of THF on the decomposition kinetics of mixed THF-CH4 hydrates was studied to evaluate the THF stabilization effect. The experimental work, carried out with the help of a reaction calorimeter, has revealed that the dissociation rate of mixed THF hydrates is lower (on average by one order of magnitude) than that of simple methane hydrates. Mixed hydrates can also be stored for short periods at temperatures over 0°C. However, the best preservation conditions (among the experimented ones) are realized at −1°C and 3 MPa. (about 66 days required for complete dissociation).
End-group characterization of poly(methylphenylsilane) by alkali metal salts doped MALDI-TOF mass spectra

The end groups of a sample of poly(methylphenylsilane) (PMPS) obtained by the Wurtz coupling reaction of diorganodichlorosilane were characterized by MALDI-TOF mass spectrometry. Three fractions were obtained by fractionation of a PMPS sample with methanol, in order to get information on the distribution of end groups as a function of the molecular weight. The MALDI mass spectra of these fractions showed well-resolved signals up to 2 500 Da corresponding to open chain oligomers of PMPS bearing four different types of end groups :-Si-(CH 3 ) 3 , -O-Si-(CH 3 ) 3 , -O-CH 3 , -H. Six different types of linear oligomers were identified together with a sizeable amount of cyclic oligomers, most likely generated during the polymerization step. The problem of isobar mass series in the MALDI spectra due to clusters of oligomers with Li + , Na + , K + was solved by comparing the MALDI spectra of PMPS fractions with and without the addition of a specific alkaline salt (KCl or NaI) to the solution containing the sample and matrix. The intensity of the oligomer clusters with the specific cation were enhanced with respect to the others. The mass shifts observed in the MALDI-TOF doped spectra with respect to the undoped ones allowed the unambiguous assignments of the isobar peaks corresponding to PMPS oligomers.
Oxygen‐dependent stomatal opening in Zea mays leaves. Effect of Light and carbon dioxide

The oxygen requirement for stomatal opening in maize plants (Zea mays L. hybrid INRA 508) was studied at different CO2 concentrations and light intensities. In the absence of CO2, stomatal opening always required O2, but this requirement decreased with increasing light intensity. In darkness, the lowest O2 partial pressure needed to obtain a weak stomatal movement was about 50 Pa. This value was lowered to ca 10 Pa in light (320 μmol m−2 s−1). On the other hand. in the absence of O2, CO2enabled stomatal opening to occur in the light, presumably due to the evolved photosynthetic O2. Thus, CO2, which generally reduced stomatal aperture, could induce stomatal movement in anoxia and light. The effect of CO2 on stomatal opening was closely dependent on O2 concentration and light intensity. Stomatal aperture appeared CO2-independent at an O2 partial pressure which was dependent on light intensity and was about 25 Pa at 320 umol m−2 s−1. The presence of a plasmalemma oxidase, in addition to mitochondrial oxidase, might explain the differences in the O2 requirement at various light intensities. The possible involvement of such a system in relation to the effect of CO2 is discussed.
Effect of Barrier Layer on InAs/GaSb Type-II Superlattice nBn Detector

In a barrier infrared detector (BIRD), the design of the barrier layer plays an important role in device performance. In InAs/GaSb Type-II superlattice (T2SL) detector with the $n$ Bn structure, AlGaSb is generally used as a barrier layer material. However, AlGaSb material has strain due to lattice mismatch with T2SL absorption layer, and it has a valence band offset (VBO) with the absorption layer in terms of band alignment. The feature of T2SL material is that it has flexible in the bandgap engineering. Therefore, T2SL material can be designed as barrier layer material. In this study, the electronic band structure of InAs/GaSb T2SL is calculated using 8-band k·p envelope method. In the nBn detector with a T2SL absorption layer, it compares the performance of the designed T2SL barrier layer and the AlGaSb barrier layer. Our study shows that T2SL barrier detectors reduced turn-on voltage by bringing the VBO close to zero. And since the lattice constant of the absorption layer and barrier layer are similar, the strain is decreased, improving the dark current density characteristic.
Design and Assembly of a Thin-Plate Mechatronic Atomizer by 3D Printing

Microfluidic structures and devices have been studied over decades for the transport of liquid through internal channels using versatile microfabrication schemes such as surface and bulk micromachining technologies. One challenge in consideration of the device design involves the breakthrough of microfluidic reservoir and channels being substantially limited in two-dimensional (2D) geometry. However, recent progress of the emerging 3D printing technologies has showed great potential to overcome this problem in a simple manner. This paper comprehensively reports an additive manufacturing of polylactic acid (PLA) layers to significantly improve the complexity in the formation of the 3D microfluidic structures as compared to conventional micro-manufacturing techniques. Moreover, a handheld mechatronic device with a small height of ~10 mm, assembled with a thin planar atomizer and a micro controller, was produced and demonstrated for generation of droplets (~6 μm in diameter). Both the analytical and experimental results indicated that the grids of channel microstructures were simply varied by different line widths (300–500 μm) and spacing (250–400 μm) 3D printed within the device, thereby providing the design capability for capillary flow. In this regard, a variety of complex micro devices fabricated via computer-aided design (CAD) and the 3D printing method could be applied for more applications than ever, such as microfluidic delivery of biomedical materials and health care devices of a small size.
Structural dynamics of acetylcholinesterase and its implications in reactivators design

Acetylcholinesterase (AChE), one of nature fastest enzyme, is the target of multiple toxics,including organophosphate nerve agents (OP). In the first part of this thesis I present thestructure-based development of a new uncharged reactivator, which showed characteristicsbetter than any molecule commercially available to date. The molecule has been rationallydesigned to present both affinity to the inhibited enzyme and good reactivation capabilities.The interactions between the lead molecule KM297 and AChE has been characterizedby means of flexible docking, molecular dynamics simulations and X-ray protein crystallography.The deeper understanding of its binding modes to both native and OP-inhibitedAChE has helped in developing a derivative, JDS207, whose binding mode at the peripheralsite of AChE is optimized. This derivative has also been studied by flexible docking and Xraycrystallography. The design of this family of reactivators taught us that a deep insightof the AChE dynamics is necessary to optimize ligands. The second part of the thesis isdevoted to the analysis of molecular dynamics simulations of AChE. At first, we assessedthat combining multiple short simulations is a fast and reliable method to characterizethe dynamics of the amino-acids side-chains. By comparing dynamics of the side-chainsfrom hAChE and TcAChE, we confirm that some key dynamical differences exist betweenthe two enzyme. The knowledge of the rotamers issued of MD simulation has lead us todevelop a new method to generate flexible receptors for docking, which is specific to eachsingle residue in the enzyme. This method has been validated by comparing its outputstructures with the ones found on the PDB database.
Synthesis of 5H-imidazo[2,1-c]pyrrolo[1,2-a][1,4]benzodiazepine

We have synthesized 5H-imidazo[2,1-c]pyrrolo[1,2-a][1,4]benzodiazepine 1 in five steps from 1-(2-amino-methylphenyl) pyrrole 4. Amidino derivatives 11-12 have also been prepared.
Gold/Platinum nanosponges for electrocatalytic oxidation of methanol

We have prepared Aun/Pt nanosponges that are highly electroactive for the oxidation of MeOH, from Pt nanosponges and AuCl4− ions through galvanic reactions at ambient temperature and pressure. At AuCl4− concentrations of 0, 0.1, 0.3, 0.5, 1.0, and 2.0 mM and a constant content of Pt nanosponges, we obtained Aun/Pt nanosponges having values of n of 0, 0.1, 0.2, 0.3, 0.5, and 0.8, respectively. The average size of the Au nanoparticles deposited on the surfaces of the Pt nanosponges increased upon increasing the AuCl4− concentration (at a constant Pt nanosponge content). We used UV–Vis absorption spectroscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, inductively coupled plasma mass spectrometry, and transmission electron microscopy to characterize the as-prepared Aun/Pt nanosponges. These techniques revealed that the Aun/Pt nanosponges comprised spherical Au nanoparticles (diameter: 6–17 nm) and assemblies of Pt nanorods (width: 3 nm; length: 17 nm). Cyclic voltammetry indicated that the electrocatalytic oxidation of MeOH was dependent on the content of Au nanoparticles on the surfaces of the Pt nanosponges. The Au0.5/Pt nanosponges provided the highest electrocatalytic efficiency—indeed, it was superior to those of commercial Pt nanoparticles and other previously reported Pt nanosponges. The Au0.5/Pt nanosponges exhibited a mass activity of 231 mA mg−1 and a forward-to-reverse anodic peak current ratio of 2.2—i.e., excellent electrocatalytic activity for the oxidation of MeOH.
Phytopathogenic fungi in forest nurseries of Middle Siberia

Abstract: The paper presents the results of phytopathogenic fungi determination in bare-root forest nurseries of Middle Siberia. Genetic analysis of pathogenic microflora of Pinus sylvestris L. , Pinus sibirica Du Tour and Picea obovata Ledeb. seedlings allowed identification of 17 genera of micromycetes: Phoma Sacc. , Lophodermium Chevall. , Sclerophoma Hohn. (teleomorph Sydowia Bres.), Cladosporium Link, Alternaria Nees, Typhula (Pers.) Fr. etc. Most frequently detected fungi represented genera Phoma (23.7 %) and Lophodermium (23.6 %). Pinus sylvestris L. seedlings harboured the highest diversity of fungal taxa. Seven genera of microscopic fungi (Phoma sp., Didymella sp., Alternaria sp., Cladosporium sp., Lophodermium sp., Gremmenia sp., Sclerophoma sp.) were detected in all studied forest zones: taiga, forest-steppe and Southern-Siberian mountain. The obtained results demonstrate the usefulness of DNA analysis for the identification of phytopathogenic fungi in forest nurseries of Middle Siberia with several implications for increasing the efficacy of forest management.
Resilience Analysis of Multichord Peer to Peer IoT Satellite Networks

The world is witnessing a dramatic shift in usage of social networks from being a passive network to an active one. This will incorporate all types of information and devices that require scalability and resilience in network operations. With Internet of Things (IoT), the challenge is doubled with the limited capabilities of such devices in addition to being self-organizing and self-configuring topologies. Some research started proposing the use of satellite networks as a carrying medium of the information in IoT networks. In this paper, we focus on analyzing the resilience of such networks in the presence of frequent disconnections that may lead to a significant delay in discovering, connecting and organizing IoT devices. This facilitates mechanisms to collect, aggregate, filter, process, store and retrieve data. In particular, we present the mathematical model of the proposed novel peer to peer multichord-based protocol for IoT over LEO satellite networks. Preliminary results show that multichord IoT satellite networks can provide the required highspeed connectivity with low latency and high robustness to failure.
Detecting Influential Users in a Trending Topic Community Using Link Analysis Approach

Social media has become a popular platform to post or share personal information, opinion, photos, videos, etc. Detection of influential users is a significant problem in information diffusion or propagation. Previous researches find influential users based on follower or retweet relationships and centrality measurement approaches. In this paper, influential users are detected by network topology that was obtained from communication relationships among users, link analysis approach, and user’s profile features. The proposed approach aims to detect trending topic influencers. Firstly, communication relationships namely retweet, mention, and reply between users in a trending topic are extracted and a trending topic graph is constructed. Secondly, influential users are detected using a link analysis approach combined with the ability of users’ profile features. The performance of the system can be proved to compare with influencer detection methods. The experimental result shows that the trending topic influencers can detect using the interaction relationships and user’s features.
Incorporating Multi-Context Into the Traversability Map for Urban Autonomous Driving Using Deep Inverse Reinforcement Learning

Autonomous driving in an urban environment with surrounding agents remains challenging. One of the key challenges is to accurately predict the traversability map that probabilistically represents future trajectories considering multiple contexts: inertial, environmental, and social. To address this, various approaches have been proposed; however, they mainly focus on considering the individual context. In addition, most studies utilize expensive prior information (such as HD maps) of the driving environment, which is not a scalable approach. In this study, we extend a deep inverse reinforcement learning-based approach that can predict the traversability map while incorporating multiple contexts for autonomous driving in a dynamic environment. Instead of using expensive prior information of the driving scene, we propose a novel deep neural network to extract contextual cues from sensing data and effectively incorporate them in the output, i.e., the reward map. Based on the reward map, our method predicts the ego-centric traversability map that represents the probability distribution of the plausible and socially acceptable future trajectories. The proposed method is qualitatively and quantitatively evaluated in real-world traffic scenarios with various baselines. The experimental results show that our method improves the prediction accuracy compared to other baseline methods and can predict future trajectories similar to those followed by a human driver.
Climate and Landscape Controls of Regional Patterns of Flow Duration Curves Across the Continental United States: Statistical Approach

The flow duration curve (FDC) is a hydrologically meaningful representation of the statistical distribution of daily streamflows. The complexity of processes contributing to the FDC introduces challenges for the direct exploration of physical controls on FDC. In this paper, the controls of climate and catchment characteristics on FDC are explored using a stochastic framework that enables construction of the FDC from three components of streamflow: fast and slow flow (during wet days) and slow flow during dry days. The FDC during wet days (FDCw) is computed as the statistical sum of the fast flow duration curve (FFDC) and the slow flow duration curve (SFDCw), considering their dependency. FDC is modeled as the mixture distribution of FDCw and the slow flow duration curve during dry days (SFDCd), by considering the fraction of wet days (δ) for perennial streams and both δ and the fraction of days of zero streamflow for ephemeral streams. The Kappa distribution is employed to fit the FFDC, SFDCw, and SFDCd for 300 catchments from Model Parameter Estimation Experiment (MOPEX) across the United States. Results show that the 0–20th percentile of FDC is controlled by FFDC and SFDCw, the 90–100th percentile of FDC is controlled by SFDCd, and the 20–90th percentile of FDC is controlled by three components. The relationships between estimated Kappa distribution parameters and climate and catchment characteristics reveal that the aridity index, the coefficient of variation of daily precipitation, timing of precipitation, time interval between storms, snow, topographic slope, and slope of recession slope curve are dominant controlling factors.