Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel dataset and evaluation of state-of-the-art vessel segmentation methods

Introduction: Vascular diseases, such as intracranial aneurysms, are one of the top causes of death in the world. Due to the constantly increasing number of angiographic imaging examinations and their use in population screening there is a need for accurate and robust methods for vessel segmentation. Methods & Materials: We used a publicly available dataset of 570 cerebral TOF-MRA angiograms (IXI dataset) and manually created reference segmentations using interactive thresholding of the raw and vesselness filter enhanced angiograms. The obtained segmentations were visually verified by a skilled radiologist and then used to objectively and comparatively evaluate six approaches based on recent convolutional neural network (CNN) segmentation models. Results: Model training on raw images (without preprocessing) resulted in Dice similarity coefficient (DSC) value of 0.91, while preprocessing with specialized filters produced inferior DSC values. Spatially affixed model training on the Circle of Willis (CoW) region yielded a significantly better result (DSC=0.95; p-value < 0.001) as compared to the training on whole images (DSC=0.91). Conclusion: On the MRA scans of IXI dataset we created reference vessel segmentations to serve as a new benchmark for vessel segmentation studies. The reference segmentations are publicly available**. Among six state-of-the-art approaches evaluated on this dataset, we found that raw input images with spatially affixed CNN model training with respect to CoW achieved the best vessel segmentation.
Guaranteed cost fault-tolerant control of networked control systems with sensor failure

Based on the network transmission with network-induced delay and packet dropout,guaranteed cost fault-tolerant control for closed-loop networked control systems(NCSs) with sensor failure is studied,aiming at the NCSs with the uncertainty of the parameters matrix,based on Lyapunov stability theory and linear matrix inequalities(LMIs),the sufficient conditions of guaranteed cost control with certain upper bound and fault-tolerant control for the NCS with sensor failure are deduced.The corresponding design method of guaranteed cost fault-tolerant controller is presented by using LMIs.The proposed control algorithm,which improves the reliability of the system,is propitious to optimize the integrative capability of the system.Numerical simulation demonstrates the effectiveness and feasibility of the proposed method.
Acoustic Emission, Chapter 7/10

Acoustic emission (AE) is one of the most promising methods for structural health monitoring (SHM) of materials and structures. Because of its passive and non-invasive nature, it can be used during the operation of a structure and supply information that cannot be collected in real time through other techniques. It is based on the recording and study of the elastic waves that are excited by irreversible processes, such as crack nucleation and propagation. These signals are sensed by transducers and are transformed into electric waveforms that offer information on the location and the type of the source. This chapter intends to present the basic principles, the equipment, and the recent trends and applications in aeronautics, highlighting the role of AE in modern non-destructive testing and SHM. The literature in the field is vast; therefore, although the included references provide an idea of the basics and the contemporary interest and level of research and practice, they are just a fraction of the total possible list of worthy studies published in the recent years. D. G. Aggelis (*) Vrije Universiteit Brussel, Brussels, Belgium e-mail: Dimitrios.Aggelis@vub.be M. G. R. Sause Institute of Materials Resource Management, University of Augsburg, Augsburg, Germany P. Packo AGH University of Science and Technology, Krakow, Poland R. Pullin · S. Grigg · Y. -K. Lai Cardiff University, Cardiff, UK T. Kek University of Ljubljana, Ljubljana, Slovenia © The Author(s) 2021 M. G. R. Sause, E. Jasiūnienė (eds.), Structural Health Monitoring Damage Detection Systems for Aerospace, Springer Aerospace Technology, https://doi.org/10.1007/978-3-030-72192-3_7 175 M or e in fo a bo ut th is a rt ic le : ht tp :// w w w . n dt . n et /? id = 26 47 0
International Journal of Management Sciences and Business Research, Nov-2021 ISSN (2226-8235) Vol-10, Issue 11

This paper examines the influence of information technology, foreign direct investment, and environmental regulations on economic growth in 13 African countries in the Franc zone during the period 1990-2020. It contributes to the literature on the information technology, foreign direct investment and environmental regulations. In this research, results have confirmed theoretical and empirical arguments for a linear growth relationship for both the West African and Central African monetary union countries. The results indicate a positive and significant influence of information technology, foreign direct investment, and environmental regulations on economic growth. These findings could have policy implications for the Franc zone countries' development prospects, particularly in terms of post-COVID-19 recovery plans. The findings of this study emphasize the financial assistance and role of information technology adoption in assisting economic recovery while meeting its objectives on domestic and external stability and incorporation of environmental policies in the post-COVID-19 era.
Influences on User Trust in Artificial Intelligence in Healthcare: A Systematic Review Protocol

Artificial Intelligence (AI) broadly refers to a “set of advanced technologies that enable machines to carry out highly complex tasks effectively [. ] tasks that would require intelligence if a person were to perform them” (Harwich & Laycock, 2018, p.11). AI has the potential to transform healthcare by altering the way in which we use data, treat patients and develop diagnostic tools. Accordingly, it is often perceived as part of a solution to tackling healthcare issues such as increasing costs and staff shortages. The promises of AI have resulted in large investments for its development. For example, the UK Government recently announced it planned to spend £250m on integrating AI into health services (Gallagher, 2019).
NSS_A_348242 83..92

1Department of Humanities, Social Sciences and Cultural Industries, University of Parma, Parma, Italy; 2Department of Health Sciences, University of Florence, Florence, Italy; 3Department of Medicine and Surgery, University of Parma, Parma, Italy; 4Department of Human Sciences, Lumsa University of Rome, Rome, Italy; 5Department of History, Cultural Heritage, Education and Society, University of Rome Tor Vergata, Rome, Italy; 6Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; 7IRCCS Institute of Neurological Sciences of Bologna (ISNB), Bologna, Italy
ANALYSIS OF EDUCATIONAL NEEDS OF MYANMAR HEALTH PROFESSIONALS

The collaboration between the University of Parma, the University of Medicine 1 of Yangon and the General Practitioners’ Society of Myanmar, aimed at improving healthcare services in rural areas of Myanmar, has led to the programming of an advanced training course for Myanmar health professionals. In order to organise the course, it was necessary to conduct an in-depth context analysis, realized through the collaboration of students and professors of both contexts. The synergic collaboration between students and professors proved to be particularly effective, allowing to conduct a comprehensive and original study, and successfully planning the course. A first key factor of the collaboration lied in the interdisciplinary profiles of students and professors involved, which proves to be an extremely important driver in the development and conduction of a cooperation project. A second key factor lied in the constructive exchange between students and professors both vertical exchange, as it concerns the transmission from the professors to the students of knowledge and professional skills, and horizontal exchange, as the meeting between students and professors answered to the transversal objective of the research of producing new competences from an educational, didactic and experiencing point of view. La collaborazione tra l'Università di Parma, l'Università di Medicina 1 di Yangon e la General Practitioners Society del Myanmar, finalizzata al miglioramento dei servizi sanitari nelle aree rurali del Myanmar, ha portato alla programmazione di un corso di alta formazione per gli operatori sanitari del Myanmar. Per organizzare il corso è stato necessario condurre un'approfondita analisi di contesto, realizzata attraverso la collaborazione di studenti e docenti di entrambi i paesi. La collaborazione sinergica tra studenti e docenti si è rivelata particolarmente efficace, consentendo di condurre uno studio completo e originale e di programmare con successo il corso. Un primo fattore chiave della collaborazione risiede nei profili interdisciplinari di studenti e docenti coinvolti, che si rivela un driver estremamente importante per lo sviluppo e la conduzione di un progetto di cooperazione. Un secondo fattore chiave risiede nello scambio costruttivo tra studenti e professori, sia scambio verticale, in quanto riguarda la trasmissione dai docenti agli studenti di conoscenze e competenze professionali, sia scambio orizzontale, in quanto l'incontro tra studenti e docenti rispondeva all'obiettivo trasversale della ricerca di produrre nuove competenze dal punto di vista educativo, didattico e sperimentale.
University of Groningen Genomic organization and evolution of double minutes/homogeneously staining regions with MYC amplification in human cancer L'Abbate,

The mechanism for generating double minutes chromosomes (dmin) and homogeneously staining regions (hsr) in cancer is still poorly understood. Through an integrated approach combining nextgeneration sequencing, single nucleotide polymorphism array, fluorescent in situ hybridization and polymerase chain reaction-based techniques, we inferred the fine structure of MYC-containing dmin/hsr amplicons harboring sequences from several different chromosomes in seven tumor cell lines, and characterized an unprecedented number of hsr insertion sites. Local chromosome shattering involving a single-step catastrophic event (chromothripsis) was recently proposed to explain clustered chromosomal rearrangements and genomic amplifications in cancer. Our bioinformatics analyses based on the listed criteria to define chromothripsis led us to exclude it as the driving force underlying amplicon genesis in our samples. Instead, the finding of coexisting heterogeneous amplicons, differing in their complexity and chromosome content, in cell lines derived from the same tumor indicated the occurrence of a multistep evolutionary process in the genesis of dmin/hsr. Our integrated approach allowed us to gather a complete view of the complex chromosome rearrangements occurring within MYC amplicons, suggesting that more than one model may be invoked to explain the origin of dmin/hsr in cancer. Finally, we identified PVT1 as a target of fusion events, confirming its role as breakpoint hotspot in MYC amplification.
GRAIN STARCH ESTIMATION USING HYPERSPECTRAL DATA AND ITS RELATIONSHIP WITH LEAF WATER CONTENTS FOR BROOMCORN MILLET (PANICUM MILIACEUM L.)

The analysis of grain starch content is one of the main indices to determine crop quality. The experiment was performed using two broomcorn millet varieties (Neimi 2 and Ningmi 14) in northern China, having 5 different sowing dates. The prediction models for grain starch fraction were constructed by integrating the spectral models and grain starch content with leaf water content using the intersection approach. We have identified 468, 630, 806, and 1488 nm as characteristic bands of the first derivative spectrum having a positive correlation. The correlation coefficient between the vegetation index of 608 nm and 1488 nm was significantly higher than other characteristic bands regarding leaf water contents. It is therefore identified that RDVI (1488, 806) could be used effectively to monitor leaf water content of broomcorn millet under different sowing date treatments, R = 0.7075, RMSE = 0.073, RPD = 1.430 in the calibration set, R = 0.8458, RMSE = 0.042, RPD = 2.546 in the validation set. In addition, the monitoring models of RVI (1488, 806), RDVI (1488, 806), and PVI (1488, 806) were superior to other models, and RPD was 0.020, 1.978, and 1.904, respectively. Overall, the RDVI (1488, 806) monitoring model, due to its stability and higher accuracy provided precise data on the grain starch fraction of broomcorn millet.
Quinoa and Chia Seed: Protein Isolates, Properties, Nutrition and Health benefits

The recent increase in the use of quinoa and chia seeds as a superfood has been seen not only just due to its popularity but also due to its various other nutritional health benefits. Quinoa and chia seeds are widely used food in a vegan diet because of its high protein content and well-balanced amino acid content present in it. This vegetable protein acts as the best replacement for animal protein for human health, animal welfare or religious region. The present review is focused on recent research on vegetable protein. The seeds protein classification and isolation, as well as its various physicochemical and functional properties of protein isolates, have been reported. Quinoa and chia seeds are gluten-free and contain a low concentration of prolamins present in it which makes it suitable for a celiac patient. The consumption of quinoa and chia seeds has been associated with a wide spectrum of benefits due to its high nutritional value which helps to prevent various diseases like immune regulation including prevention of intestinal imbalances and inflammatory disorders.
DECISION-MAKERS’ UNDERLYING VALUE-BELIEF SYSTEMS

This paper investigates how a consumer’s utility and consequent optimization are determined by his/her natural endowments – self-awareness, imagination, conscience and free will. It focuses on such general utility that is a function in the dollar value of consumption, the number of hours spent on waged work and a particular value-belief system. For the third variable, we examine that it encourages the consumption of commodities and devalues waged job; it reinforces minimal commodity consumption; and it demands a non-standard optimization. While uncovering how an individual’s marginal utility from commodity consumption or waged work varies respectively with different variables, such as non-waged incomes, etc., this paper demonstrates that when an individual decides on how much commodity is to be consumed and how much labor output is to be supplied to waged work by maximizing the corresponding utility, the person’s utility and his/her method of optimization are exclusively defined by his/her value-belief system.
Understanding Views on Sentience and Conservation: A Study of Emotional Experiences with Wildlife Authors

with Wildlife Authors Anna Reilly, Annie Lynch, Katie Richards, Nelle Douglas, and Lily Jeffery Environmental Studies Program, Boston College, Boston, MA, 02467, USA Corresponding Author Jennifer Rebecca Schauer, 427 McGuinn, Department of Sociology, Boston College, Boston, MA 02467; kellyajf@bc.edu Authors’ Contributions AR, AL, KR, ND, and LJ designed the project and co-wrote the manuscript. Running Title Views on Sentience and Conservation
Using psychotools for Evaluating the Performance of Score-Based Measurement Invariance Tests in IRT Models

The detection of diﬀerential item functioning (DIF) is a central topic in psychometrics and educational measurement. In the past few years, a new family of score-based tests of measurement invariance has been proposed, which allows the detection of DIF along arbitrary person covariates in a variety of item response theory (IRT) models. Schneider, Strobl, Zeileis, and Debelak (2020) illustrate the application of these tests within the R system for statistical computing. This vignette targets more advanced users and provides a tutorial on how to conduct simulation studies investigating the performance of score-based tests of measurement invariance.
Bachelor Thesis

End-to-end approaches for autonomous driving gain popularity since NVIDIA demonstrated its capabilities with their PilotNet architecture in 2016. Although many research projects are subject to the eld of end-to-end architecures, less e ort is taken into the issue that arises when a vehicle with di erent physical properties uses an architecture that it was not trained on speci cally. This results in something that we refer to as `system discrepancies' and can be understood as a sim-to-real problem. In this thesis we implement and evaluate NVIDIA's popular PilotNet against a system discrepancy which is an o set in the steering. This architecture maps an input image of the front facing camera to an absolute steering wheel angle. We use a custom data set including images with minimal complexity in regard to image features that we generated in the autonomous driving simulator CARLA. In the context of CARLA, we demonstrate that a steering o set negatively impact the driving performance and o sets the vehicle's position to an amount that is not acceptable in a real world scenario. We propose, implement and evaluate a prototype architecture called PilotNet∆ (PilotNet Delta) that has increased robustness against the steering o set and leads to improved results when comsidering the lateral o set on the road. PilotNet∆ uses a convolutional LSTM layer to map a sequence of images to a relative steering angle, which is the di erence to the prevoius steering prediction.
The benefits of general-purpose on-NIC memory

We propose to use the small, newly available on-NIC memory ("nicmem") to keep pace with the rapidly increasing performance of NICs. We motivate our proposal by accelerating two types of workload classes: NFV and key-value stores. As NFV workloads frequently operate on headers---rather than data---of incoming packets, we introduce a new packet-processing architecture that splits between the two, keeping the data on nicmem when possible and thus reducing PCIe traffic, memory bandwidth, and CPU processing time. Our approach consequently shortens NFV latency by up to 23% and increases its throughput by up to 19%. Similarly, because key-value stores commonly exhibit skewed distributions, we introduce a new network stack mechanism that lets applications keep frequently accessed items on nicmem. Our design shortens memcached latency by up to 43% and increases its throughput by up to 80%.
Applying Natural Language Processing Models to Create Recommendations for Professional Skills Development

The article examines the problem of applying intellectual methods and models for the automatic extraction of professional skills and competencies from the descriptions of academic disciplines in Russian. The accuracy of pre-trained multilingual models of distributed vector representation LASER and SBERT is investigated. The process of collecting and preprocessing a dataset for creating and training skills classification models is described. A neural network model of skills classification based on a bidirectional recurrent neural network with long-short-term memory is described and trained. It was trained on data prepared two different ways: 1) skills were separated on “soft skills” and “hard skills”, the last one was not translated into Russian; 2) “soft skills” and “hard skills” were treated as one class and were translated into Russian. Learning curves on validation data are presented. They show high accuracy of trained models up to 99%. The test accuracy of the models was analyzed employing confusion matrices and the results show a fairly high performance of the model. an example of skills extraction from an academic discipline using trained models is given.
GS-02-2020-0022_proof 372..393

Purpose – The purpose of this paper is to examine the effectiveness of an improved dummy variables control grey model (DVCGM) considering the hysteresis effect of government policies in China’s energy intensity (EI) forecasting. Design/methodology/approach – Energy consumption is considered as an important driver of economic development. China has introduced policies those aim at the optimization of energy structure and EI. In this study, EI is forecasted by an improved DVCGM, considering the hysteresis effect of energy-saving policies of the government. A nonlinear optimization method based on particle swarm optimization (PSO) algorithm is constructed to calculate the hysteresis parameter. A one-step rolling mechanism is applied to provide input data of the prediction model. Grey model (GM) (1, N), DVCGM (1, N) and ARIMAmodel are applied to test the accuracy of the improved DVCGM (1, N) model prediction. Findings –The results show that the improvedDVCGMprovides reliable results andworkswell in simulation and predictions using multivariable data in small sample size and time-lag virtual variable. Accordingly, the improvedDVCGM notes the hysteresis effect of government policies and significantly improves the prediction accuracy of China’s EI than the other three models. Originality/value – This study estimates the EI considering the hysteresis effect of energy-saving policies in China by using an improved DVCGM. The main contribution of this paper is to propose a model to estimate EI, considering the hysteresis effect of energy-saving policies and improve forecasting accuracy.
Determination of Genetic Mutation Profile of drpr Gene in Drosophila melanogaster using Coupled real time PCR-High Resolution Melting Analysis

Key word: Draper High-resolution melting Genetic mutation Drosophila melanogaster Genotype determination of experimental animals is generally conducted using sequencing methods that require expensive cost as well as experience and special equipment. This study aimed to determine the presence of mutation in the drpr gene of Drosophila melanogaster using coupled real time PCR-High Resolution Melting (real time PCR-HRM) as an alternative method. Two types of fly samples, w and drpr were used as wildtype control and mutant genotype, respectively. The DNA from twenty of each w and mutant drpr flies were isolated and amplified using real time PCR. The generated amplicons were then further processed by HRM method at the temperature of 60-95°C. This study demonstrated that the real time PCRHRM method could distinguish wildtype control w and mutant drpr based on the HRM data with the confidence level was more than 90%. Therefore, this study provides an evidence that real time PCR-HRM method might be beneficial to screen the mutant genotype from its wildtype counterpart based on differences in the melting temperatures due to changes at nucleotide base level. Article history: Received: 12/01/2021 Revised: 08/08/2021 Accepted: 16/10/2021
K2-GNN: Multiple Users' Comments Integration with Probabilistic K-Hop Knowledge Graph Neural Networks

Integrating multiple comments into a concise statement for any online products or web services requires a non-trivial understanding of the input. Recently, graph neural networks (GNN) has been successfully applied to learn from highly-structured graph representations to mitigate the relationship between entities, such as co-references. However, current inter-sentence relation extraction cannot leverage discrete reasoning chains over multiple comments. To address this issue, in this paper, we propose a probabilistic K -hop knowledge graph (KKG) to extend existing knowledge graphs with inferred relations via discrete intra-sentence and inter-sentence reasoning chains. KKG associates each inferred relation with a conﬁdence value through Bayesian inference. We further answer how a knowledge graph with inferred relations can help the multiple comments integration through integrating KKG with GNN (K 2 -GNN). Our extensive experimental results show that our K 2 -GNN outperforms all baseline graph models on multiple comments integration