Miracle-dz/UniFAR-FTUs huggingface dataset:
Augmenting Dummies for Training Central Venous Catheterization

Medical simulation based on augmented reality (AR) is a promising tool to improve medical training, particularly for needle-based interventions. However, limited research has been conducted in new solutions for training central venous catheterization (CVC). The purpose of this work is to compare the AR application developed with the conventional method to train CVC, using a dummy of the upper torso and neck. The proposed system (named PIÑATA) was implemented using AR targets placed in the needle and the dummy to display internal anatomical structures and additional information about the position and orientation of the needle. Observation and co-design sessions were conducted to collect the requirements, obtain feedback from the users and improve the prototype. A total of 18 potential users attending specialists and medical residents performed needle insertion tasks for CVC with PIÑATA and with the conventional training system. The performance was objectively measured by the task completion time and the number of errors. A correlation between the task completion time of the two training methods was found, suggesting the concurrent validity of PIÑATA. An inherent difference in the task completion time (p = 0.040) and in the number of errors (p = 0.036) between novices and experts proved the construct validity of the new tool. The qualitative answers of the participants also suggest its face and content validity, a high acceptability rate and a medium perceived workload. The overall results show that the AR tool proposed can complement the conventional training of CVC. Keywords—Medical training, augmented reality, central venous catheterization, needle placement, professional assessment.
Fluid-Structure Interaction in Coronary Stents: a Discrete Multiphysics Approach

Stenting is a common method for treating atherosclerosis. A metal or polymer stent is deployed to open the stenosed artery or vein. After the stent is deployed, the blood flow dynamics influence the mechanics by compressing and expanding the structure. If the stent does not respond properly to the resulting stress, vascular wall injury or re-stenosis can occur. In this work, Discrete Multiphysics is used to study the mechanical deformation of the coronary stent and its relationship with the blood flow dynamics. The major parameters responsible for deforming the stent are sort in terms of dimensionless numbers and a relationship between the elastic forces in the stent and pressure forces in the fluid is established. The blood flow and the stiffness of the stent material contribute significantly to the stent deformation and affect the rate of deformation. The stress distribution in the stent is not uniform with the higher stresses occurring at the nodes of the structure.
Tackling Pilot Contamination in Cell-Free Massive MIMO by Joint Channel Estimation and Linear Multi-User Detection

In this paper we consider cell-free (CF) massive MIMO (MaMIMO) systems, which comprise a very large number of geographically distributed access points (APs) serving a much smaller number of users. We exploit channel sparsity to tackle pilot contamination, which originates from the reuse of pilot sequences. Specifically, we consider semi-blind methods for joint channel estimation and data detection. Under the challenging assumption of deterministic parameters, we determine sufficient conditions and necessary conditions for semi-blind identifiability, which guarantee the non-singularity of the Fisher Information Matrix (FIM) and the existence of the Cramer-Rao bound (CRB). We propose a message passing (MP) algorithm which determines the exact channel coefficients in the case of semiblind identifiability. We show that the system is identifiable if the Karp-Sipser algorithm yields an empty core. Additionally, we propose a Bayesian semi-blind approach which results in an effective algorithm for joint channel estimation and multi-user detection. This algorithm alternates between channel estimation and linear multi-user detection. Numerical simulations verify the analytical derivations.
IMPROVING THE SENTIMENT ANALYSIS OF SARCASTIC TWEETS USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING

With the developing technology, the number of comments made on the internet is increasing day by day. It is difficult to make a manual sentiment analysis on these comments. Therefore, new algorithms should be developed to automatically perform sentiment analysis on these texts for companies. In this study, a sentiment analysis model using machine learning and NLP (Natural Language Processing) algorithms are compared. While developing this model, CNN (Convolutional Neural Network) methods and machine learning algorithms were used together. As a naïve method of sentiment analysis, the root of each word in a sentence takes a score from a dictionary and the final polarity score of the relevant sentence is calculated by using additive score-based models. Machine learning models and Natural Language Processing models were trained to perform accurate sentiment annotations by using features based on polarity scores of texts. This analysis was conducted on 10,000 tweets using publicly available data from Kaggle. The results showed that NLP outperforms machine learning algorithms and gives better accuracy.
The Implementation of Deep Reinforcement Learning in E-Learning and Distance Learning: Remote Practical Work

The world has seen major developments in the field of e-learning and distance learning, especially during the COVID-19 crisis, which revealed the importance of these two types of education and the fruitful benefits they have offered in a group of countries, especially those that have excellent infrastructure. At the Faculty of Sciences Semlalia, Cadi Ayyad University Marrakech, Morocco, we have created a simple electronic platform for remote practical work (RPW), and its results have been good in terms of student interaction and even facilitating the employment of a professor. The objective of this work is to propose a recommendation system based on deep quality-learning networks (DQNs) to recommend and direct students in advance of doing the RPW according to their skills of each mouse or keyboard click per student. We are focusing on this technology because it has strong, tremendous visibility and problem-solving ability that we will demonstrate in the result section. Our platform has enabled us to collect a range of students' and teachers' information and their interactions with the learning content we will rely on as inputs (a large number of images per second for each mouse or keyboard click per student) into our new system for output (doing the RPW). This technique is reflected in an attempt to embody the virtual teacher's image within the platform and then adequately trained with DQN technology to perform the RPW. © 2021 Abdelali El Gourari et al.
Interlayer and interfacial stress transfer in hBN nanosheets

Stress transfer has been investigated for exfoliated hexagonal boron nitride (hBN) nanosheets (BNNSs) through the use of Raman spectroscopy. Single BNNSs of different thicknesses of up to 100 nm (300 layers) were deposited upon a poly(methyl methacrylate) (PMMA) substrate and deformed in unixial tension. The Raman spectra from the BNNSs were relatively weak compared to graphene, but the in-plane E2g Raman mode (the G band) could be distinguished from the spectrum of the PMMA substrate. It was found that G band down-shifted during tensile deformation and that the rate of band shift per unit strain decreased as the thickness of the BNNSs increased, as is found for multi-layer graphene. The efficiency of internal stress transfer between the different hBN layers was found to be of the order of 99% compared to 60%–80% for graphene, as a result of the stronger bonding between the hBN layers in the BNNSs. The reduction in bandshift rate can be related to the effective Young’s modulus of the 2D material in a nanocomposites and the findings show that it would be expected that even 100 layer BNNSs should have a Young’s modulus of more than half that of hBN monolayer. Interfacial stress transfer between a single hBN nanosheet and the PMMA substrate has been evaluated using shear lag theory. It is found that the interfacial shear stress between the BNNS and the substrate is of the order of 10 MPa, a factor of around 4 higher than that for a graphene monolayer. These findings imply that BNNSs should give better mechanical reinforcement than graphene in polymer-based nanocomposites as a result of good internal interlayer stress transfer within the nanosheets and better interfacial stress transfer to the polymer matrix.
Spatiotemporal Distributions and Land-Use Regression Models of Ambient Bacteria and Endotoxins in the Greater Taipei Area

ABSTRACTAmbient bacteria and endotoxins are components of bioaerosols, which are abundant in ambient air. Exposure to ambient bacteria and endotoxins has been associated with respiratory symptoms. We monitored the spatiotemporal distributions of ambient bioaerosols in the Greater Taipei area by using multisite sampling and developed regression models for ambient bacterial and endotoxin concentrations. Forty-four representative sampling sites were selected from the Greater Taipei area. Samples were collected in 4 seasons throughout a 1-y study period. Ambient bacteria were quantified using acridine orange staining, and endotoxins were analyzed using Limulus amebocyte lysate assays. Land-use types and major determinants of the bioaerosols were used to develop regression models. Ambient bacteria ranged from < limit of detection (LOD) to 1.68 × 105 cells/m3, whereas endotoxins ranged from < LOD to 20.62 EU/m3. Significant seasonal variations were observed for both bioaerosols, with the highest concentrations observed in spring. Regression analyses revealed temperature, relative humidity, and particulate matter as the major predictors of ambient bacteria and endotoxins. No land-use type was correlated with any of the bioaerosols. The number of schools and gas stations was positively associated with both bioaerosols. The leave-one-out cross-validation R2 of the final models for ambient bacteria and endotoxins were 0.11 and 0.31, respectively. The results of this study revealed high spatiotemporal variations in the distributions of ambient bacteria and endotoxins in the Greater Taipei area. Additional potential predictors should be included in future studies to develop better predictive models for ambient bacteria and endotoxins.
Physics of turbulence spreading and explicit nonlocality

In this paper, we systematically derive a model for turbulence spreading from the basic kinetic equation. The model contains explicit nonlocal nonlinear diffusion and nonlocal growth. When the nonlocality scale parameter δ b (banana width) vanishes, this model reduces to the usual turbulence spreading model. We elucidate the mechanisms of nonlinear saturation and nonlocal growth. Results show that nonlocal effects, especially the nonlocal growth, thicken the turbulence spreading front and increase the speed of front propagation. More turbulence intensity penetrates the stable region when δ b increases. The penetration depth Δ p is proportional to δb/LT , therefore the fraction of turbulence in the unstable region scales as 1 − δ b*. The transport scales the same way.
Engineering Extracellular Matrix-derived Constructs for Human Cardiac Tissue Models

Difficulties in maturing human induced pluripotent stem cells derived cardiomyocytes (hiPSC-CMs) to an adult-like state is still an obstacle hindering their clinical use. Cardiac tissues grown from hiPSC-CMs are still subjected to a microenvironment distinct from physiological conditions. In Nature, the extracellular matrix (ECM) serves as a template to build tissues, being a key regulator in tissue development, homeostasis and injury. Exposing hiPSC-CMs to an ECM-mimicking microenvironment may guide their maturation. In this work, human ECM was extracted from cardiac tissue through physical and chemical-based decellularization approaches. Tissue milling and freeze-drying resulted in tissue cellularity reduction and maintenance of ECM composition, even though tissue architecture is not preserved. By using detergents, consistent reductions in cellularity amongst different donors was only achieved with exposure times comparable with organ perfusion decellularization. ECM composition and tissue architecture preservation were also donor-dependent. ECM was successfully incorporated in a 3D hiPSC-based cardiac model, and contributed to enrich the microenvironment in ECM, promoted increase in the expression of genes related with ultrastructural cardiac maturation, resulted in hiPSC-CMs with larger sarcomeres, and improved calcium handling. ECM was also successfully used to functionalize alginate scaffolds with a myocardium biomimetic honeycomb architecture. Optimization of cell retention within the biomaterial will be necessary to unravel ECM’s role in maturing more structurally complex hiPSC-based tissues.
Potential of Virtual Worlds for Purchasing

A previous study concluded that T-shirt choices in 3D virtual worlds are good indicators of consumer preferences when exposed to T-shirts in physical forms. This allows a rapid and cheaper test of the market’s potential on new models without the time and cost of manufacturing and shipping physical samples. However, 3D virtual worlds have far fewer users than traditional web platforms, which can limit and bias their potential on this area. This paradigm leads to an increase of the need to analyse and compare the quality of this indicator to other similar indicators in a more usual 2D virtual space, with more users (Web platform). This study compares the differences on the purchasing records between 2D and 3D, with the actual receipt of the physical product, to identify the quality factor of each method as an indicator of consumer preferences of physical products. For this, physical products were created and offered for sale in 2D virtual spaces and 3D virtual world, where users made purchase. Second Life platform was used to the 3D virtual world whilst Webnode platform was used for 2D virtual world. The physical products created were t-shirts, and then recreated on both 3D and 2D. Subsequently, users were invited to choose and buy, for their own money, a t-shirt in the 3D virtual world and group of different users proceed the same way on to the 2D virtual space. In the final step users were introduced to the purchased physical t-shirt for delivery and given the option to modify their choice of t-shirt, previously purchased online. The results conclude that the 3D virtual world choices are more similar to consumers actual choices when experiencing the physical product than the choices made when buying 2D t-shirts online.
NEWLY CREATED BLENDS AND COMPOUNDS IN THE ENGLISH LANGUAGE RELATED TO THE COVID-19 PANDEMIC

This paper aims to investigate the possible interpretations of the new expressions in the English language emerging during the COVID-19 pandemic. The current pandemic and the numerous subsequent profound changes in everyday life have proved to be a fertile ground for the creation of various expressions being increasingly used to describe these different social circumstances. The corpus of the study comprises more than 50 expressions that were collected using different internet tools and platforms. Blending and compounding seem to be the dominant processes for creating the recorded neologisms. Interpretation processes of the expressions with more than one meaning were examined within the framework of the Relevance theory. Since the aim of this paper is to examine the interpretation process of expressions that have specific morphological structure (blends and compounds). Since decomposition is an important step in the interpretation of these expressions, an additional goal is imposed – the analysis of morphological processes involved in their creation. The morphological transparency of neologisms (the level or degree of identifiability of the splinters and bases) is related to our ability to interpret them in the intended way. Furthermore, the interpretation is undoubtedly easier in an appropriate/available/specific context, but this study points to the possibility of the context being as important in the interpretation of compounds (more morphologically transparent) as it is with blends (less transparent). Namely, the neologisms analyzed in this paper comprise at least two concepts and in order to arrive at the intended meaning of an expression, the hearer needs to construct an ad hoc concept, and therefore access the encyclopedic entries of the concepts utilized in creating the neologism. Choosing the correct (intended) properties from the encyclopedic entry, now featuring in a newly created concept, is strongly influenced by the context. This could be one of the reasons for the fact that numerous novel expressions in the English language have more than one meaning (hence interpretation) attested by their recorded use.
Applying Machine Learning Techniques to Implement a Decision Support Mechanism for Human Resources Recruitment

— This work describes a new approach based on machine learning techniques to automatically evaluate candidates to open positions in IT companies and its integration on the recruitment processes. This approach comprises four main layers: a processing layer, a clustering layer, a classification layer and an output layer. The processing layer receives CVs, extracting their features in order to be used in the latter layers. The clustering layer studies the underlying structure of the company while the classification layer is responsible for the evaluation of an incoming candidate, relying on the structured data obtained from the previous layer. The first implements the K-Means clustering algorithm while the latter implements the KNN classifier. At last, the final layer generates several outputs displayed in a web application, describing the evaluated candidate and its adequacy for the company. This work shows promising results regarding its usefulness as a tool to guide the recruitment process, standing out from similar systems by the deeper analysis performed on each candidate and simpler user interfaces.
Verification of a Fabless Device Model Using TCAD Tools: from Bipolar Transistor Formation to I-V Characteristics Extraction

This paper describes the analysis of processes used in microand nano-electronic device manufacturing. It also presents an exemplary and novel laboratory exercise in which an epitaxial planar n + pn bipolar transistor with junction isolation is illustrated and analyzed step-by-step. Only seven photolithography steps are used to obtain this bipolar transistor structure: for buried layer formation, for junction transistor isolation and collectors regions formation, for base region formation, for emitter and collector n+ region formation, for contact windows, for first aluminum metallization, and, finally, for passivation. Silvaco TCAD software tools are used to implement all of these manufacturing processes and to simulate the resulting I-V characteristics of all presented semiconductor structures. This type of laboratory work provides students with basic knowledge and a consistent understanding of bipolar transistor manufacturing, as well as facilitating theoretical understanding, analysis, and simulation of various semiconductor manufacturing processes without the need for costly and lengthy technological manufacturing experiments. This article also presents the conclusions and other benefits of such laboratory work, as well as possible recommendations for further improvement or expansion.
Propolis ve Sağlık Üzerine Etkileri

Propolis, bal arilari tarafindan bitkilerden ozellikle de cicek ve tomurcuklardan toplanan belirli miktarda balmumu ve recine karisimi ve esansiyel yaglari iceren ve kovan icerisinde bircok amaca uygun olarak kullanilan dogal bir ari urunudur. Insan sagligi uzerinde yapilan calismalarda antioksidan, anti-mikrobiyal, anti-tumor, anti- inflamatuvar basta olmak uzere bircok biyolojik aktiviteye sahiptir. Son bes yila donuk calismalara bakildiginda, propolis ve yapisinda bulunan kafeik asit bileseninin antioksidan ve antiinflamatuvar etkiye sahip oldugu, bagisiklik sistemi ve diger saglik durumlari uzerine etki gosterdigi gozlenmistir. Ozellikle stres ve inflamatuvar sitokin artisinin oldugu mekanizmalarda kafeik asit bileseninin mekanizmadaki etkin enzimlerin inhibisyonunu sagladigi gozlenmistir. Bagisiklik sistemi uzerine etkili olabilmesi icin alinmasi gereken doz konusunda tam netlik olmasa bile yapilan randomize kontrollu calismalar sonucunda belirlenen gunluk alimda guvenilir olan doz 70 mg/kg olup, 15 g ustu alim toksik doz olarak belirlenmistir. Propolisin bagisiklik sistemi uzerine etkisi, yan etkileri ve guvenilir dozu ile ilgili calismalar gun gectikce artmaktadir. Onumuzdeki donemlerde yapilan calismalarla birlikte bagisiklik sistemi uzerindeki etkinlik mekanizmalari ve doz konusundaki bilgiler kanit duzeyinde anlamlilik kazanacaktir. Buna bagli olarak propolisin basta bagisiklik sistemi ve butuncul olarak insan sagligina etkilerinin tanimlanmasi onemlidir. Bu calismanin amaci; propolisin ozellikleri, tuketim dozu ve insan sagligi uzerine etkileri konusunda yapilan calismalarin ozetlenerek, aktarilmasidir.
The Role of Interventional Endoscopic Ultrasound in Liver Diseases: What Have We Learnt?

Chronic liver disease (CLD) is still a major problem, where the disease progression will lead to liver cirrhosis (LC) or hepatocellular carcinoma (HCC). Portal hypertension (PH) management and loco-regional therapy for HCC have become the cornerstones in advanced liver disease management. Recently, there are studies looking at the potential role of interventional endoscopic ultrasound (EUS) in liver diseases. EUS may be useful in vascular changes of the digestive wall evaluation, performing dynamic assessment of hemodynamic changes, predicting variceal bleeding and rebleeding risk, and assessing the pharmacological effects. In PH management, EUS-guided vascular therapy—which revolves around glue injection, endovascular coil placement/embolization, and combination of both—has shown promising results. As a diagnostic modality for liver cancer, the implementation of EUS in liver diseases is currently not only limited to liver biopsy (EUS-LB) but also in shear-wave elastography (SWE) and portal pressure gradient measurement, as well as portal vein sampling. The application of EUS-guided radiofrequency ablation (EUS-RFA) and tumor injection can also overcome the limitations shown by both modalities without EUS. Nevertheless, establishing EUS as a firm diagnostic and therapeutic modality is still challenging since the performance of interventional EUS requires high expertise and adequate facilities.