Miracle-dz/UniFAR-FTUs huggingface dataset:
Lessons from COVID-19 for GCR governance: a research agenda

The Lessons from Covid-19 Research Agenda offers a structure to study the COVID-19 pandemic and the pandemic response from a Global Catastrophic Risk (GCR) perspective. The agenda sets out the aims of our study, which is to investigate the key decisions and actions (or failures to decide or to act) that significantly altered the course of the pandemic, with the aim of improving disaster preparedness and response in the future. It also asks how we can transfer these lessons to other areas of (potential) global catastrophic risk management such as extreme climate change, radical loss of biodiversity and the governance of extreme risks posed by new technologies. Our study aims to identify key moments- ‘inflection points’- that significantly shaped the catastrophic trajectory of COVID-19. To that end this Research Agenda has identified four broad clusters where such inflection points are likely to exist: pandemic preparedness, early action, vaccines and non-pharmaceutical interventions. The aim is to drill down into each of these clusters to ascertain whether and how the course of the pandemic might have gone differently, both at the national and the global level, using counterfactual analysis. Four aspects are used to assess candidate inflection points within each cluster: 1. the information available at the time; 2. the decision-making processes used; 3. the capacity and ability to implement different courses of action, and 4. the communication of information and decisions to different publics. The Research Agenda identifies crucial questions in each cluster for all four aspects that should enable the identification of the key lessons from COVID-19 and the pandemic response.
Neutrophil Elastase and Cathepsin G Receptor/CD87 on Monocytic Cells by Proteolytic Regulation of the Urokinase

The urokinase receptor (CD87) participates to the pericellular proteolytic potential of migrating cells and to the recruitment of leukocytes during inﬂammation. It consists of three structurally homologous domains, with the C-terminal domain D3 attached to cell membranes through a GPI anchor. CD87 is susceptible to an endoproteolytic processing removing the N-terminal domain D1 and generating truncated D2D3 membrane species, thus modulating CD87-associated functions. Full-length or truncated CD87 can be also released from cells via juxtamembrane cleavage by phospholipases and/or by yet unidentiﬁed proteinases. Using a recombinant CD87 and the CD87-positive monocytic U937 cell line and isolated blood monocytes, we show by protein immunoblotting and ﬂow immunocytometry that the human neutrophil serine-proteinases elastase and cathepsin G cleave CD87 within the D1-D2 linker sequence, while in addition cathepsin G is highly efﬁcient in cleaving the C terminus of D3. The combination of cathepsin G and elastase provided by degranulated neutrophils results in enzymatic cooperation leading to the release from monocytic cells of a truncated D2D3 species resembling that previously described in pathological body ﬂuids. Using mass spectrometry analysis, the proteolytic fragmentation of synthetic peptides mapping the D1-D2 linker and D3 C-terminal domains identiﬁes potential cleavage sites for each enzyme and suggests the existence of a mechanism regulating the CD87(D1-D2)-asso-ciated chemotactic activity. Finally, isolated or combined elastase (cid:6) : 1672.9175; adrenocorticotropic hor- mone fragment 18–39, [M (cid:6) H] (cid:6) : 2465.1989). Identiﬁcation of the truncated peptides was conducted with GPMAW software, version 6.0 (Lighthouse Data, Odense, Denmark), in regard to the D1-D2 and D3 sequences. Search was performed using monoisotopic masses with a mass tolerance of 0.1%.
Sex Differences in Pre-Season Anthropometric, Balance and Range-of-Motion Characteristics in Elite Youth Soccer Players

In soccer, injury epidemiology differs between males and females. It is clinically useful to know whether there are between-sex differences in selected characteristics relevant to primary injury risk and injury prevention practices. The purpose of this study was to explore between-sex differences in anthropometric, balance, and range-of-motion characteristics in Spanish elite male and female youth soccer players. This was a pre-season cross-sectional study. Sixty-nine males (age 16.8 ± 0.9 yr; height 175.9 ± 6.8 cm; mass 67.9 ± 6.3 kg) and thirty-seven females (age 17.2 ± 1.7 yr; height 164.0 ± 6.3 cm; mass 59.0 ± 5.8 kg) participated. Anthropometrics (standing/sitting height, bodymass, right/left leg length) and right/left anterior reach test (ART), hip internal/external active range of motion, active knee extension (AKE), and weightbearing lunge test (WBLT) were measured. Between-sex differences were assessed with Bonferroni-corrected Mann–Whitney U tests and Cliff’s delta (d). Between-sex significant differences (p < 0.003, d ≥ 0.50) were observed for anthropometric data and for hip internal rotation. No between-sex significant differences were observed for ART/AKE/WBLT measures. Between-sex significant differences with large effect sizes were identified for anthropometric data and right/left hip internal rotation. The present study adds new data to the literature for young Spanish male and female soccer players. The present findings will help inform clinical reasoning processes and future injury prevention research for elite male and female youth soccer players.
DIGITAL IMAGES AND TOTAL STATION MEASUREMENTS FUSION FOR 3D BUILDINGS MODELS CREATION

In recent years, 3D buildings models have known a great evolution, being used in many scientific domains of activity. In this paper is presented a method for 3D building model creation, using digital images and total station measurements. For this case study, the historical monument "Vovidenia church" from Iasi City was chosen. Due to the fact that the north part of the church is occluded, also by trees and the property limit, digital images couldn't be taken in ideal conditions. So, this part of the church, was modeled in 3D using total station measurements, made for building characteristic points: corners of windows, doors, etc. The south part was modelled based on digital images, acquired with the Nikon Coolpix L810 digital camera, calibrated with a 3D calibration target. Finally, the two results were fused into AutoCAD software and the final 3D model was created. The 3D building model, was evaluated by comparing the coordinates of 20 building characteristic points, as resulted from the total station measurements and by digital images processing respectively.
Differential Strategy-Based Multi-Level Dense Network for Pansharpening

Due to the discrepancy in spatial structure between multispectral (MS) and panchromatic (PAN) images, the general fusion scheme will lead to image error in the fused result. To solve this issue, a differential strategy-based multi-level dense network is proposed, and it regards the image pairs at different scales as the input of the network at different levels and is able to map the spatial information in PAN images to each band of MS images well by learning the differential information of different levels, which effectively solves the scale effect of remote sensing images. An improved dense network with the same hierarchical structure is used to obtain richer spatial features to enhance the spatial information of the fused result. Meanwhile, a hybrid loss strategy is used to constrain the network at different levels for obtaining better results. Qualitative and quantitative analyses show that the result has a uniform spectral distribution, a complete spatial structure, and optimal evaluation criteria, which fully demonstrate the superior performance of the proposed method.
Query Answering and Scaling Extensions of Konclude

Konclude is a well-performing reasoner for ontologies formulated via the Web Ontology Language. In this paper, we give an overview of new or extended optimizations that lead to additional improvements, such as an individual derivations cache for handling large amounts of assertions more efficiently. We further describe new (functional) extensions (e
Genomics in precision medicine

The term Precision Medicine (PM) gained significant attention in the field of drug discovery. The roots of PM can be traced back even before the start of Human Genome Project (in the early 2000s) but came into existence only by 2010. PM is a preventative strategy that focuses on improved patient care by taking account on variability in genes, environment, and lifestyle for each individual. PM is a long-term endeavour, including the National Institute of Health (NIH) and multiple other research centres were pioneers in funding and conducting cutting edge research related to PM. Knowing the genetic makeup of an individual will help us better understand and predict the risk of disease, this will further aid in personalised diagnosis and treatment strategies. Although there has been significant number of research articles available on PM, however to the best of my knowledge they do not emphasise much on the reasons why PM has not been the choice for disease diagnosis and patient care. Therefore, this article will explain how PM will transform the future of heath care as we know it today. In addition, this article also discusses on factors that will aid in the development of PM.
Autonomous Drone Control for Visual Search Based on Deep Reinforcement Learning

: Autonomous flight of drone using Deep Reinforcement Learning is an attractive area of research in recent years that gives excellent results. Autonomous drone flight is defined through a set of complex tasks for understanding the environment and navigating independently through it. Understanding the environment means that the drone knows its location in respect to other objects and that it can easily reach the desired location without collision. Extending the problem with a target search task increases the complexity and the necessity for using new tools and algorithms. In this paper, we present an approach in which a drone, in addition to learning to navigate in an unknown environment, learns how to find and approach an object a priori assigned to it as a target. In our approach, the drone uses RGB and RGB-D cameras as the only source of information about environment. Our proposed solution incorporates, into the framework of deep reinforcement learning, appropriate fast object detection, feature extraction, as well as efficient existing algorithms for avoiding obstacles. The proposed model uses the sensed RGB-D image of the drone as the main factor for estimating the distance to the obstacles, while, on the other hand, our model also requires two RGB images for a Siamese network as feature extractor used to identify the target in the environment, group of these images represents the current general state, based on which drone performs the action for which it can potentially receive the highest reward. We used a 3D simulator (MS AirSim) to validate the performance of our approach. Based on the simulation results, we conclude that the proposed method exhibits promising performance in terms of the rate of successful approach to the
Mobile crowdsourcing: Intrinsic and extrinsic motivational factors influencingonline communities in China

The ubiquity of crowdsourcing has become a new tool for creativity activities of online communities by participating on social platforms in marketing spheres. Mobile crowdsourcing is emerging as new approach to attract mobile crowd in online creativity tasks. The aim of this research is to contribute to the current body of knowledge on intrinsic and extrinsic motivation to crowdsourcing via social networks by analyzing various motivational factors influencing users to perform tasks in online activities. This paper provides exploration of several motivational factors in online communities and we develop a research model to explain various motives in crowdsourcing activities. We then proceed to identify the driving factors to attract online users to participate in contribution through social platforms. Using a survey based research design, involving mobile users in China, we analyzed the impacts of intrinsic motivation and incentive factors to use Chinese social networks linked with online creativities. We find that intrinsic motivations are linked to higher level of online users’ to contribute in crowdsourcing rather than extrinsic motivations in Chinese context. The findings of the study have significant practical implications for online creativities in crowdsourcing for crowds in China.
Secure Multifactor Authentication and Access Control Mechanism for Electronic Bill Service in a 5G Cloud-Fog Hybrid Architecture

The electronic bill service was greatly encouraged by electronic commerce and paperless bills. However, the massive authentication and authorization access requests from users in the electronic bill services cannot be efficiently processed by a conventional client/server-based scheme during the 5G era. This paper proposes a secure multifactor authentication and access control scheme tailored for electronic bill service in a 5G cloud-fog hybrid architecture. The proposed scheme can realize many security properties, including mutual authentication, privacy protection, batch authentication, authorization and revocation of authority, and resistance to multiple protocol attacks. Moreover, the scheme has the desired performance compared to similar schemes. Finally, we used BAN logic and Scyther to analyze and verify the proposed protocol, and the result shows that the proposed scheme is stable and can achieve the previous security goals.
DSW: One-Shot Learning Scheme for Device-Free Acoustic Gesture Signals

In this paper, we propose a Dynamic Speed Warping (DSW) algorithm to enable one-shot learning for device-free acoustic gesture signals performed by different users. The design of DSW is based on the observation that the gesture type is determined by the trajectory of hand components rather than the movement speed. By dynamically scaling the speed distribution and tracking the movement distance along the trajectory, DSW can effectively match gesture signals from different domains with a ten-fold difference in speeds. Our experimental results show that DSW can achieve a recognition accuracy of 97% for gestures performed by unknown users while only using one training sample of each gesture type from four training users.
Translation and Cross-Cultural Adaptation of the System Usability Scale to Brazilian Portuguese

Objective: To adapt culturally the System Usability Scale to Brazilian Portuguese and assess its internal consistency and structural construct validity. Materials and methods: This methodological study of a measurement instrument’s cultural adaptation included the initial translation, the translations’ synthesis, the back-translation, the evaluation by a committee of eight experts, testinh with a sample of 100 students, who evaluated the usability of the WhatsApp application, and the evaluation of structural construct validity by exploratory factor analysis. Results: The judges validated the Brazilian version of the System Usability Scale in the second round. It was revealed that, following the structural construct validation, the version had a unidimensional structure and an acceptable level of reliability (Cronbach’s alpha of 0.76). Besides, in the usability test, no suggestions for change were made. Conclusions: It is worth noting that the Brazilian version of this scale was semantically, idiomatically, conceptually, and culturally equivalent to the original English version and showed adequate reliability and structural construct validity.
Registration of facial liposculpture protocols: technique article

In this scenario, optimizations have been made in the techniques of suspending the superficial musculoaponeurotic system and the adjacent fat pads [2,3]. Minimally invasive procedures in facial LE are expanding, resulting in optimal esthetic effects [3-5]. However, as it is technically challenging to remove subcutaneous AT deposits evenly with open techniques, these attempts often produce uneven skin contours. In this regard, lipo contouring techniques provide a means to shape the AT deposits of the neck and face to better obtain the desired facial profile [6].
Precalculated arrays-based algorithms for the calculation of the Riemann zeta-function

In this paper, we continue the study of efficient algorithms for the computation of the Riemann zeta function on the complex plane. We introduce two precalculated arrays-based modifications of MB-method. We perform numerical experiments with these algorithms using Zetafast as a benchmark and apply the algorithms for the visualizations of fractal structures associated with the Riemann zeta function.
RoFDT: Identification of Drug–Target Interactions from Protein Sequence and Drug Molecular Structure Using Rotation Forest

Simple Summary Determining the drug–target relationships is the key to modern drug development, and it plays a crucial role in drug side effects research and individual treatment. However, traditional drug target identification by bio-experimental methods is often difficult to develop due to limitations of precision, flux and cost. With the rapid development of bioinformatics and computational biology, the computer-assisted drug–target interaction (DTIs) prediction approach has attracted great attention by researchers as an accurate and quick mean of drug target recognition. In this study, combined with the protein sequence information and drug molecular structure information, a prediction method of DTIs based on machine learning is developed to achieve the purpose of locking targets and saving costs for new drug research. Abstract As the basis for screening drug candidates, the identification of drug–target interactions (DTIs) plays a crucial role in the innovative drugs research. However, due to the inherent constraints of small-scale and time-consuming wet experiments, DTI recognition is usually difficult to carry out. In the present study, we developed a computational approach called RoFDT to predict DTIs by combining feature-weighted Rotation Forest (FwRF) with a protein sequence. In particular, we first encode protein sequences as numerical matrices by Position-Specific Score Matrix (PSSM), then extract their features utilize Pseudo Position-Specific Score Matrix (PsePSSM) and combine them with drug structure information-molecular fingerprints and finally feed them into the FwRF classifier and validate the performance of RoFDT on Enzyme, GPCR, Ion Channel and Nuclear Receptor datasets. In the above dataset, RoFDT achieved 91.68%, 84.72%, 88.11% and 78.33% accuracy, respectively. RoFDT shows excellent performance in comparison with support vector machine models and previous superior approaches. Furthermore, 7 of the top 10 DTIs with RoFDT estimate scores were proven by the relevant database. These results demonstrate that RoFDT can be employed to a powerful predictive approach for DTIs to provide theoretical support for innovative drug discovery.
Data Mining For Dummies

Delve into your data for the key to successData mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome.Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including:Model creation, validity testing, and interpretationEffective communication of findingsAvailable tools, both paid and open-sourceData selection, transformation, and evaluationData Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining.
Experimental evaluation on the use of capillary tube and thermostatic expansion valve with heat recovery hot spot water heater in air source refrigeration system

The present experimental evaluation has been carried out to investigate the use of capillary tube (CT) and thermostatic expansion valve (TEV) with heat recovery hot spot water heater (HRHSWH) in air source compression refrigeration system. CT and TEV are the two basic types of refrigerant expansion devices that most frequently used in compression refrigeration system, but the identified about HRHSWH in extant literature are limited. The HRHSWH is modified from residential refrigeration system. The heat exchanger coil as HRHSWH was bonded with the compressor discharge pipe line as counter-flow heat exchanger, then insulated and placed them into transparent acrylic box. Water from storage tank with 50L capacity is pumped using circulation pump to the HRHSWH and heating the water in storage tank. The system performance of those two expansion devices in compression refrigeration system are evaluated and compared between with or without modified using HRHSWH. Results show that refrigeration system using TEV pe.
Plant Classification Using Firefly Algorithm and Support Vector Machine

Plant identification plays an important role in crop cultivation and agriculture. Plants are traditionally distinguished based on their fruit, flowers, and leaves. However, relying on human experience quickly becomes tedious and unmanageable, so a need for an automated approach that can assist farmers in crop management presents itself. This research proposes an approach based on swarm intelligence (SI) algorithms and support vector machine (SVM) to extract features and classify plant images. The nature-inspired firefly algorithm (FA) models behavior patterns of fireflies and adapts them to optimization problems for which it excels at resolving. Combined with the SVM model, which is often used for solving classification problems with great accuracy gives a novel approach used to handle plant identification. The resulting FA-SVM algorithm has been thoroughly tested on an extensive plant image dataset, and obtained results indicate that it outperforms competing algorithms in terms of accuracy, as well as other performance metrics, when applied to the same dataset.
Weight Status Change in Chinese American Children over a Ten-Year Period: Retrospective Study of a Primary Care Pediatric Population

Weight change from childhood to adolescence has been understudied in Asian Americans. Known studies lack disaggregation by Asian subgroups. This retrospective study assessed the weight status change in 1500 Chinese American children aged 5–11 years from an urban primary care health center between 2007 and 2017. Weight status was categorized using the 2000 CDC growth charts into “underweight/normal weight” and “overweight/obese. ” The overweight/obesity prevalence in 2007 and 2017 were determined. McNemar’s test and logistic regression were performed. The prevalence of overweight/obesity decreased from 29.9% in 2007 to 18.6% in 2017. Children who were overweight/obese at 5–11 years had 10.3 increased odds of staying overweight/obese over time (95% CI = 7.6–14.0, p < 0.001) compared to their underweight/normal weight counterparts. Of the children who were overweight/obese in 2007, 45.7% remained overweight/obese ten years later. Childhood overweight/obesity strongly predicts adult overweight/obesity in Chinese Americans. Targeted education and intervention are warranted to prevent adult obesity.