Paper materials are natural composite materials where fibers are almost randomly distributed in a fiber network. Mechanical properties of fiber networks are known to be strongly controlled by fiberfiber interactions and single fiber properties. A fiber network is often modeled as a beam network where beam-to-beam interactions are treated as cohesive zones and single beams stretch indefinitely without breaking. The latter assumption is not physically correct and leads to an overprediction of the mechanical response of the beam network. In this work, we present a computational modeling framework for simulating beam failures and thereby closing the gap to physically based micromechanical modeling of paper and packaging products. Modeling beam failure is a challenging engineering problem. At the onset of failure, the tangent stiffness tensor projected in a direction normal to the surface of discontinuity (commonly referred to as the localization tensor) is singular, i.e. we have a bifurcation point and the problem is ill-posed. Another implication of ill-posedness for the numerical simulation after a spatial discretization is a pathological mesh dependency of the computed result. We use the ED-FEM where a failure process zone (FPZ) is introduced into a multi-scale continuum mechanics formulation (i.e. the material is split into a small scale and a large scale defining the FPZ and the bulk material, respectively), making the computed result mesh independent. The multi-scale nature of the ED-FEM enables an operator splitting implementation method as opposed to carrying out the computations of the nodal displacement vector and the displacement discontinuity vector simultaneously with the global loop where the global stiffness matrix would be singular at the onset of failure. We show that fiber failures and fiber-fiber bond failures can contribute to the observed elastoplastic stress-strain response of paper.

We develop a thermodynamically consistent continuum damage micromechanics model for the compressive failure of flax fiber composites. We used a micromechanics-based constitutive model reported recently [1]. It describes the microstructure of a unidirectional composite and captures the material behavior of the fiber and matrix constituents, respectively. The description has been formulated in the reference configuration (i.e. the undeformed state of the composite) and is therefore independent of fiber rotations that may appear during the deformation of the composite. A hyperelastic finite deformation plasticity with power law hardening [3] mimics the compressive elastic-plastic stress-strain response of the fiber (reported in [2]) and the matrix. The model has been extended to account for fiber damage, resulting in a thermodynamically consistent continuum damage micromechanics model. Our results indicate that fiber damage plays an utmost role in the compressive failure of flax fiber composites – it is a major determinant of the material’s compressive stress-strain response. X-ray Computed Tomography and Scanning Electron Microscopy show that fiber damage can be attributed to intra-fiber splitting and elementary fiber crushing.

In recent years IPTV (Internet Protocol Television) platforms are becoming one of the most popular entertainment multimedia services which are used to serve movies, tv-series and other video and audio attractive content using the Internet Protocol. VoD (Video on Demand) is the most popular multimedia IPTV service, which provides content without the need for the old traditional way of using video playback devices. Except that it is necessary to have high-quality VoD content, IPTV platforms must provide the best end-user experience. Moreover, it is imperative to provide new features to attract new customers and keep the existing ones. We confirmed the efficacy of this classifier thru simple trial and error. When we searched for movies that have sequels, our engine recommended those sequels. Since Cosine Similarity Classifier considers multiple factors, such as actor, genre, year, etc. Even if the movie does not have prequels or sequels this algorithm was able to provide us with movies that share other common characteristics.

COVID-19 pandemic brought many changes in people’s lifestyles. Some of those changes hurt people's mental health in different age groups. This research is done to investigate which factors contributed most to the occurrence of depressive and anxiety symptoms during COVID-19 lockdown, and what type of people in terms of age, sex, level of education, place of living, was the most exposed to the appearance of mental health disorders. 1115 people (18-85 years old) from Poland joined the research process. They fulfilled online questionnaires which were used as a basis for further research of lockdown impact on mental health. Responses are evaluated by using ML tools predicting the group of participants with signs of depression and anxiety, based on their answers to the questionnaires, and the attributes of the participants. Based on the results given by the studies, the youngest population (age 18-29), which participated in the surveys, experienced more intense depression and anxiety symptoms than participants from other age groups.

K-means and hierarchical clustering algorithms are employed to cluster genes according to the gene expression to determine the harming level of the genes in brain cancer. The gene expression data with a control group from The Cancer Genome Atlas database were used. The optimal cluster number for each clustering technique was obtained using the elbow method and dendrogram for K-means and hierarchical clustering methods respectively. We identified the ideal number of clusters as three and further classified them into seven groups. We observed that the second cluster contains over half the genes in healthy people and the cluster distribution of a healthy patient and a patient who died six months after being diagnosed with brain cancer is similar. Further analysis indicated that of all the time spent by patients after being diagnosed with brain cancer, group 0 has the highest percentage in one month after the diagnosis, while group -2 has the lowest percentage. Most genes shift their clusters when Kmeans and hierarchical clustering techniques we compared with the genes from the control and disease groups. The result of the measure of dissimilarity between the genes expression patterns indicates that the K-means technique outperforms the hierarchical technique with a higher rate of change in the cluster.

Homicide rates are still high in the world and they are the worst crime in human existence. Despite all the technological advances and usage of information by various agencies, the number of homicides is not decreasing. Homicide prediction in certain countries should notably be the number one priority, which can help the government to easily identify the kind of profile they are looking for, or even help them prevent those cases. This paper compares different Machine Learning Techniques classifications of homicide prediction. Random Forest (RF), Random Tree, J48, Naive Bayes and k-Nearest-Neighbor (KNN) were tested to determine which method provides the best results in homicide prediction classification. The results of sample accuracy for all algorithms were around 99%, which clearly shows that all algorithms give great results. However, J48 is the best technique applied on the dataset, as it classified all instances correctly.

Since the early beginnings of education systems, attendance has always played a crucial role in student success, as well as in the overall interest of the matter. The most productive way of increasing the student attendance rate is to understand why it decreases, try to predict when it is going to happen, and act on causing factors in order to prevent it. Many benefits of predicted and increased attendance rate can be achieved, including better lecture organization (i.e. lecture time and duration, lecture class choice, etc). This paper describes the steps in the extraction of knowledge from the university's student database and making a model that predicts whether the student will attend the class or not. Results show that the attendance patterns are best reflected when employing a decision tree algorithm, a C4.5 model that is interpretable and able to predict the attendance with 0.81 AUC performance measure

It can be confidently stated that access to education is one of the most prized possessions available to us today. Although there are underlying factors such as the discrepancies in the education being provided worldwide, it is imperative that data scientists and all those interested take advantage of the data publicly available to draw necessary insights into how to better the education sector in our respective countries. The purpose of this research is to showcase various analytical insights into the 2020 New York State (NYS) high school graduation rate data using various advanced database systems techniques, specifically using SQL. With these analyses, further studies and conclusions can be drawn for local governments to implement into their plans to increase the quality of the schooling system, to aim for equality for all without reg

With the increasing number of users and data on the Internet, especially social media sites, sentiment analysis topic became one of the important and essential fields for most. Collection of people's feelings and sentiment and classifying the data attracted most businesses and companies. Recently, twitter sentiment analysis has attracted much attention, because of Twitter's growth and popularity. The solution for handling enormous amounts of data from social media is a new term called Big data. Big data is not just for having a large amount of data, but also the importance of processing and the usage of the data.