cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
The Exploration on the Variety of Methods that Attempt to Reduce Barren Plateaus in Quantum Machine Learning, and the Proposal of a Noise-Reducing Ansatz as a Solution – A Literature Review
Creators: name: BRAHMJOT SINGH CHAWLA
name: CRISTIAN GERHARD JIMENEZ- EBERT
name: Boicu, Mihai
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: Barren plateaus have notably decreased the ease of trainability of Quantum Machine Learning models. Barren plateaus are formed where the variability decreases exponentially in the graph of the cost function. The purpose of this literature review was to provide an up-to-date overview of solutions to reduce barren plateaus, by analyzing the effects of Quantum Convolutional Neural Networks (QCNN), novel perturbations in Variational Quantum Algorithms, Variational Quantum State Diagonalization, and the Expressiveness of Parametrization on reducing barren plateaus. The novel perturbations in step sizes in Quantum Natural Gradient proved to be more effective than the QCNN, which has been widely recognized as one of the most efficient ways to maintain variability in a cost function to avoid barren plateaus. Additionally, the use of an ansatz (a circuit with a predetermined geometry and parameterized gates) reduces the hardware noise of a quantum computer leading to reduce noise-induced barren plateaus. These results serve to encourage further research into novel perturbations in the steps of Quantum Natural Gradient and the Noise-Reducing Ansatz.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
Detection Methods for Sockpuppet Accounts on Reddit – A Systematic Literature Review
Creators: name: ASHWIN TRIPATHY
name: Boicu, Mihai
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: Reddit, one of the largest and most diverse social media platforms, has witnessed a significant rise in the presence of malicious accounts, including trolls, spammers, and sockpuppets. The timely and accurate detection of these accounts is essential to maintain the platform's integrity and user experience. A large range of techniques is used to detect sockpuppet accounts, including machine learning algorithms such as Support Vector Machines (SVM), Random Forest, and Neural Networks, as well as rule-based approaches and natural language processing techniques. Optimized Feed Forward Neural Networks obtained best precision (92%) and recall. SVM's had the ability to handle imbalanced datasets, a common issue in troll detection. The generalizability of SVM also contributes to its impact, as it can be trained on diverse datasets from various online platforms and still yield consistent and high-performing results, making it the most reliable detection method. By understanding the details and effectiveness of various detection methods, this study contributes to the development of more reliable approaches to tackle the growing challenge of malicious accounts on Reddit.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
Predicting Procrastination in College Students Using Machine Learning: A Comparative Analysis of Models – A Preliminary Experiment
Creators: name: ARNAV MATHUR
name: Boicu, Mihai
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: Procrastination is a prevalent issue among college students that can negatively impact academic performance and overall well-being. Several machine learning techniques were explored to predict procrastination tendencies in college students based on daily behavioral patterns and task characteristics (assignment type, grades, and submission time for each attempt). A procrastination index was created based on late submission frequency and grade improvement frequency, with a higher index indicating increased procrastination behavior. Three machine learning models are compared: Random Forest, Support Vector Regression, and Neural Network, to identify the most effective approach in predicting the index. Using the Random Forest Regression model, an R-squared value (accuracy) of 0.873 was achieved, indicating a strong ability to predict procrastination tendencies based on the given features. The Support Vector Regression showed an R-squared value of 0.609, and the Neural Network achieved an R-squared value of 0.840. The findings demonstrate that machine learning models can predict procrastination tendencies in college students. This research is a preliminary step to eventually better understanding procrastination behaviors and providing early intervention and support for college students facing procrastination challenges.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
Heterogeneity-induced large deviations in activity and (in some cases) entropy production
Creators: name: Gingrich, Todd R.
name: Vaikuntanathan, Suriyanarayanan
name: Geissler, Phillip L.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: FOS: Physical sciences

description: We solve a simple model that supports a dynamic phase transition and show conditions for the existence of the transition. Using methods of large deviation theory we analytically compute the probability distribution for activity and entropy production rates of the trajectories on a large ring with a single heterogeneous link. The corresponding joint rate function demonstrates two dynamical phases - one localized and the other delocalized, but the marginal rate functions do not always exhibit the underlying transition. Symmetries in dynamic order parameters influence the observation of a transition, such that distributions for certain dynamic order parameters need not reveal an underlying dynamical bistability. Solution of our model system furthermore yields the form of the effective Markov transition matrices that generate dynamics in which the two dynamical phases are at coexistence. We discuss the implications of the transition for the response of bacterial cells to antibiotic treatment, arguing that even simple models of a cell cycle lacking an explicit bistability in configuration space will exhibit a bistability of dynamical phases.
description: 11 pages, 7 figures
20: Brain Tumor MRI Dataset
Creators: name: Mayer, Simone
name: Hartmann, Dennis
name: Auer, Florian
name: Krafft, Tamara
Publisher: name: Zenodo
Year: 2023

description: This model was trained to determine, if a patient suffers from glioma, meningioma, pituitary or no tumor. It was trained on a combination of the following three datasets: figshareSARTAJ dataset Br35H The resulting dataset contains 7022 images of human brain MRI images which are classified into 4 classes: gliomameningiomano tumorpituitaryNo tumor class images were taken from the Br35H dataset. The model was trained using this script; a summary of its performance can be found here.
Supplementary for Plach et al., 2022, ACP
Creators: name: Plach, Andreas
Year: 2022

description: Data collection used in the analysis presented in: "Analysis of high gas concentration and flux measurements at Swiss Beromünster tall tower" by Plach et al., 2022, ACP Andreas Plach, Rolf Rüfenacht, Simone Kotthaus, and Markus Leuenberger egusphere-2022-1019
Gurzadyan's Problem 5 and improvement of softenings for cosmological simulations using the PP method
Creators: name: Eingorn, Maxim
Publisher: name: arXiv
Year: 2014
Subjects: subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Physical sciences

description: This paper is devoted to different modifications of two standard softenings of the gravitational attraction (namely the Plummer and Hernquist softenings), which are commonly used in cosmological simulations based on the particle-particle (PP) method, and their comparison. It is demonstrated that some of the proposed alternatives lead to almost the same accuracy as in the case of the pure Newtonian interaction, even despite the fact that the force resolution is allowed to equal half the minimum interparticle distance. The revealed way of precision improvement gives an opportunity to succeed in solving Gurzadyan's Problem 5 and bring modern computer codes up to a higher standard.
description: 8 pages, 1 figure
Additional file 2 of Construction and description of a constitutive plipastatin mono-producing Bacillus subtilis
Creators: name: Maliheh Vahidinasab
name: Lilge, Lars
name: Reinfurt, Aline
name: Pfannstiel, Jens
name: Henkel, Marius
name: Heravi, Kambiz Morabbi
name: Hausmann, Rudolf
Publisher: name: figshare
Year: 2020
Subjects: subject: Biochemistry
subject: Microbiology
subject: FOS: Biological sciences
subject: Biotechnology
subject: Ecology
subject: Immunology
subject: FOS: Clinical medicine

description: Additional file 2. A: Plipastatin variants detected by MS analysis in B. subtilis standard. B: Detailed mass spectrometry data of extracted-ion chromatograms regarding to plipastatin standard produced by B. subtilis and the sample extracts of BMV12 strain from exponential phase and stationary phase.
Mental health research: misalignment between priorities and needs in the face of shifting societal demands
Creators: name: Yegros-Yegros, Alfredo
Publisher: name: Zenodo
Year: 2022

description: While we had previous experiences of using portfolio mapping to discuss research priorities, this is the first time that we have applied this mixed method methodology in the context of a specific policy need – in this case, the request by Vinnova to explore and compare research on prevention, rehabilitation and social determinants of mental health. Since we found serious challenges in terms of selection and classification of publication into relevant categories, we have used multiple visualisations in terms of disciplinary profile and topic article-level clusters. Triangulating evidence with these multiple mappings, the analysis supports the view of many stakeholders that there is relatively low level of research in prevention, social determinants, health care systems and that more research on these issues.
SCALABLE - D6.1 Report on identification and set-up of key development systems
Creators: name: Riha, Lubomir
name: Vysocky, Ondrej
name: Badwaik, Jayesh
name: Herten, Andreas
Publisher: name: Zenodo
Year: 2021

description: The goal of this deliverable is to report the status of the access to the experimental and cutting-edge computing systems that will be used for code development and optimization, benchmarking, and porting to new architectures by all members of the consortium. The key development systems at IT4I and FZJ were identified, and through the Director Discretion project (IT4I) and PRACE Preparatory Access project (FZJ) all project partners have now access to the identified development systems for early work on both WaLBerla and ProLB. Access to petascale/pre-exascale systems Karolina and LUMI will be enabled during the upcoming months when the systems become generally available for users. Both WaLBerla and ProLB were installed and tested using a basic test case at Barbora and JUWELS Booster, so that WP2 can perform benchmarking. A compute project proposal with several million core hours has been submitted to provide resources for development at IT4I systems, other similar-size projects are planned to propose resources of JUWELS Booster and LUMI systems.
The Impact of Online Learning During Covid-19 on Student Procrastination – A Systematic Literature Review
Creators: name: KALLAKURU SAMPADA
name: RYAN YI
name: ARJUN BANGALORE
name: Boicu, Mihai
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: During the Covid-19 pandemic, classes were taken online to allow teaching to continue while keeping up with self-isolation guidelines. This research studied the impacts and provided clarification on how the online environment has affected future generations’ procrastination as they return to regular learning. Initially, papers were discovered using ad-hoc searches in various repositories, followed by a systematic search conducted through identified journals and conferences using keywords such as “covid”, “procrastination”, and “online learning”. Thirty-five relevant articles were identified and sorted into three categories: mental health, academic performance, and social environment. An analysis performed for each category revealed that online learning was significantly impacted and led to further procrastination. Not only did the mental health of students greatly suffer through an increase in depression and anxiety, but their academic performance greatly fell as well. Students experienced decreased grades and burnout leading to a steady increase in procrastination. Classroom environments also fostered this procrastination, promoting students to continue these unhealthy habits. Overall, online learning increased students’ tendency to procrastinate and it continues to linger even after students have returned to in-person learning.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
A preliminary analysis of deep learning and quantum machine learning approaches for stock prediction
Creators: name: ANDY QIN
name: Boicu, Mihai
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: Predicting stocks has always been an attractive prospect for researchers as financial stock markets have been shown to have a significant economic impact on numerous sectors such as business, education, and technology. Techniques such as moving average models (e.g., autoregressive integrated moving average) and deep learning models (e.g., recurrent neural network) have been used to model financial markets. However, the stock market is incredibly volatile and there are no existing models that consistently model stocks. This study aims to reveal possible benefits of using quantum machine learning to model markets by proposing two models: a long short-term memory (LSTM) and a quantum neural network (QNN) consisting of two parametrized quantum circuits. The models were tested on an intraday dataset of Agilent Technologies, Inc., one of the index stocks on the S&P 500, with 1-minute intervals from 9-10-2017 to 2-12-2018. Each of these methods are evaluated using root mean squared error (RMSE) with the LSTM performing significantly better with an RMSE of 1.8695 (vs 4.4813 for QNN). Overall, quantum machine learning models still seem to be underdeveloped compared to classical models, and despite their faster training speeds, do not always perform better.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
Using A Machine Learning Approach to Identify Predictive Factors for Early Detection of Procrastination – An Experimental Design
Creators: name: VINEEL KANDALA
name: ALEEM SIDDIQUI
name: Boicu, Mihai
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: Procrastination is a prevalent issue affecting individuals in various domains, including education and personal life. Recognizing the significance of early detection to prevent its negative consequences, this research project aims to identify indicators for early signs of procrastination by conducting a literature review. Previous research identified predictor variables such as self-efficacy, conscientiousness, growth mindset, academic entitlement, and demographic factors. The most important results from the reviewed literature suggest that these predictor variables play significant roles in predicting procrastination tendencies, and integrating these factors into machine learning algorithms can enhance the accuracy of early procrastination detection. Specifically, the incorporation of self-efficacy, personality data, and motivational factors such as academic entitlement and growth mindset were variables that predicted procrastination in machine learning algorithms more accurately than demographic factors. These predictors also had higher levels of correlation with procrastination compared with demographic ones. An experimental design is proposed to study various machine learning models to analyze existing datasets and to assess the predictive performance of each predictor variable. By using ablation experiments, the design will study their individual impact on early procrastination detection and develop effective intervention strategies with the goal of fostering improved academic outcomes and overall well-being.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
First Release of the SeaDataNet Aggregated Data Sets Products
Creators: name: Simoncelli, Simona
name: Tonani, Marina
name: Grandi, Alessandro
name: Coatanoan, Christine
name: Myroshnychenko, Volodymyr
name: Sagen, Helge
name: Bäck, Örjan
name: Scory, Serge
name: Schlitzer, Reiner
name: Fichaut, Michele
Publisher: name: SeaDataNet
Year: 2014

description: The document describes the procedure implemented for the generation of the SeaDataNet 2 versions of the aggregated products. Detailed descriptions of the data sets par sea region (Mediterranean Sea, Black Sea, Arctic Sea, Baltic Sea, North Sea and the North Atlantic Ocean) are given with the general description of the dataset, the data quality assessment procedure and results.
IGSN IDs: Use & Registration DataCite Training
Creators: name: Edmunds, Rorie
name: Ross, Cody
Publisher: name: Zenodo
Year: 2022
Subjects: Advanced Training
DataCite Member Meeting 2022
IGSN ID
Training Session

description: DataCite Member Meeting 2022 DataCite Training Session (Advanced Track) IGSN IDs: Use & Registration DataCite Training September 2022
The influence of time intervals between test attempts on student performance – an ML-focused analysis of synthetic data
Creators: name: ADITYA BARMAN
name: Boicu, Mihai
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: The practice of providing students with multiple attempts on assessments is common in many educational settings. However, the optimal time interval between retake attempts and its impact on student learning outcomes remains relatively unexplored. Existing research has shown varying results on the influence of the time interval, with some studies indicating longer intervals leading to increasing performance while others suggest no significant effect. This study analyzes synthetic data generated based on an undergraduate computing course at George Mason University, consisting of two formative tests and one summative test from the second week of classes. A statistical analysis reveals a larger mean time interval in hours between attempts for formative assessments (7.31 and 7.05) compared to summative assessments (4.52) but shows no consistent trend between score gain and time interval. A Random Forest Regression machine learning model was also employed to predict score gain based on time interval. Using data from over 900 unique students, the model generated high mean squared error values along with R-squared values of -0.02399, -0.04447, and -0.07393, indicating very little variance in score gain based on time interval. Other factors, like study methods, should be analyzed alongside time intervals in future research on optimizing academic assessment.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
Set partitions, tableaux, and subspace profiles under regular diagonal matrices
Creators: name: Prasad, Amritanshu
name: Ram, Samrith
Publisher: name: arXiv
Year: 2021
Subjects: subject: Combinatorics (math.CO)
subject: FOS: Mathematics
subject: 05A15, 05A18, 05A30, 15B33, 33C45

description: We introduce a family of univariate polynomials indexed by integer partitions. At prime powers, they count the number of subspaces in a finite vector space that transform under a regular diagonal matrix in a specified manner. This enumeration formula is a combinatorial solution to a problem introduced by Bender, Coley, Robbins and Rumsey. At $1$, they count set partitions with specified block sizes. At $0$, they count standard tableaux of specified shape. At $-1$, they count standard shifted tableaux of a specified shape. These polynomials are generated by a new statistic on set partitions (called the interlacing number) as well as a polynomial statistic on standard tableaux. They allow us to express $q$-Stirling numbers of the second kind as sums over standard tableaux and as sums over set partitions.
 For partitions whose parts are at most two, these polynomials are the non-zero entries of the Catalan triangle associated to the $q$-Hermite orthogonal polynomial sequence. In particular, when all parts are equal to two, they coincide with the polynomials defined by Touchard that enumerate chord diagrams by the number of crossings.
description: 30 pages; accepted version, to appear in European Journal of Combinatorics, vol. 124, February 2025. 104060
Tunable terahertz coherent perfect absorption in a monolayer graphene
Creators: name: Fan, Yuancheng
name: Zhang, Fuli
name: Zhao, Qian
name: Wei, Zeyong
name: Li, Hongqiang
Publisher: name: arXiv
Year: 2014
Subjects: subject: Optics (physics.optics)
subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: Coherent perfect absorber (CPA) was proposed as the time-reversed counterpart to laser: a resonator containing lossy medium instead of gain medium can absorb the coherent optical fields completely. Here, we exploit a monolayer graphene to realize the CPA in a non-resonant manner. It is found that quasi-CPA point exists in the terahertz regime for suspending monolayer graphene, and the CPA can be implemented with the assistant of proper phase modulation among two incident beams at the quasi-CPA frequencies. The graphene based CPA is found of broadband angular selectivity: CPA point splits into two frequency bands for the orthogonal $s$ and $p$ polarizations at oblique incidence, and the two bands cover a wide frequency range starting from zero frequency. Furthermore, the coherent absorption can be tuned substantially by varying the gate-controlled Fermi energy. The findings of CPA with non-resonant graphene sheet can be generalized for potential applications in terahertz/infrared detections and signal processing with two-dimensional optoelectronic materials.
description: 4 pages, 4 figures
A Comparative Analysis of Classical and Quantum Computer Trading Algorithms
Creators: name: ADEN MICHAEL KAMATH
name: Boicu, Mihai
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: Quantum computing has the potential to revolutionize the field of investing. By using quantum algorithms utilizing mean regression, long short-term memory networks, and neutral tree networks, investors can better understand complex financial markets and make more informed decisions. This research report investigates the potential benefits of quantum computing for portfolio optimization by comparing a classical algorithm that used standard statistical mean regression and a quantum computer algorithm, both analyzing the same dataset of historical stock prices and volatility to create perfectly traded portfolios. The quantum algorithm was able to create a better portfolio than the classical algorithm, even when given the same dataset, with the classical algorithm performing with a Sharpe Ratio of 1.23 for a chosen basket of stocks, and 0.77 for the S&P500 index, and the quantum computing algorithm performing with a Sharpe Ratio above 4, in some cases. This result suggests that when given the same data, quantum computers have better pattern recognition capabilities than classical computers, given their larger computing parameters, and that they have the potential to create better portfolios.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
title: Verstehensprobleme im deutschsprachigen Geografieunterricht
Creators: name: Denk, Tina
Year: 2021

description: Schulischer Erfolg in Österreich ist abhängig von der deutschen Sprache. Nicht alle Kinder und Jugendliche sprechen Deutsch als Erstsprache und nicht alle sind gleich und haben die gleichen Voraussetzungen. Bildungs- und Fachsprache spielt im schulischen Kontext eine wichtige Rolle, besonders dann wenn Deutsch nicht als Erstsprache gelernt wird. Die vorliegende Masterarbeit beschäftigt sich mit den Verstehensproblemen speziell im Geografieunterricht. Konrekt wurde diese Forschung in einer Mittelschule im Waldviertel mit drei DaZ-Schüler/innen durchgeführt. Zu Beginn wurde der Sprachstand mittels C-Test erfasst, im Anschluss wurden Unterrichtseinheiten in drei Klassen durchgeführt und analysiert. Dabei wurden eine Beobachtung und schriftliche Online-Aufgaben eingesetzt. Die empirische Untersuchung hat gezeigt, dass es im Fachunterricht zu Verstehensproblemen bei den Schüler/innen kommt und dass es aufgrund ihres Sprachstandes oft nicht möglich ist dem Unterricht zu folgen. Im Ausblick wird erwähnt, welche Voraussetzungen notwendig sind, damit die DaZ-Schüler/innen erfolgreich sein können. Denn die Fördermaßnahmen reichen nicht aus, um alle individuellen Förderschwerpunkte abzudecken. Die Ausgangslagen der Schüler/innen sollten optimal berücksichtigt werden um die bestmögliche Unterstützung bieten zu können.
The average number of integral points on the congruent number curves
Creators: name: Chan, Stephanie
Publisher: name: arXiv
Year: 2021
Subjects: subject: Number Theory (math.NT)
subject: FOS: Mathematics
subject: 11G05, 11N45, 11R45

description: We show that the total number of non-torsion integral points on the elliptic curves $\mathcal{E}_D:y^2=x^3-D^2x$, where $D$ ranges over positive squarefree integers less than $N$, is $O( N(\log N)^{-1/4+ε})$. The proof involves a discriminant-lowering procedure on integral binary quartic forms and an application of Heath-Brown's method on estimating the average size of the $2$-Selmer group of the curves in this family.
description: 21 pages
A Comparative Analysis of Hybrid Quantum Neural Networks in Binary Credit Defaulting Tasks
Creators: name: ADITYA SENGAR
name: AARYA VIJAYARAGHAVAN
name: JIWOO HWANG
name: Boicu, Mihai
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: Quantum machine learning has been shown to offer exponential computational boosts compared to current classical models through the exploitation of quantum mechanical properties such as entanglement and superposition. The finance subfield of fintech, where financial services are constructed around a customer’s demands, is believed to benefit with the use of hybrid quantum-classical machine learning algorithms. Classical algorithms struggle to create accurate, complex, and prompt financial analytical models resulting in over 25% of small and medium sized financial institutions losing customers each year. This study explores whether a hybrid quantum neural network (QNN) design provides a quantum advantage in either accuracy or time in the fintech task of credit defaulting. The hybrid quantum neural network model uses a variational quantum circuit built on a continuous variable architecture. The dataflow of the network consists of a classical network, quantum data encoding (which uses quantum squeezers, interferometers, displacement, and Kerr gates), QNN, and measurement. Although the quantum model took a longer time to train, it reported a higher prediction accuracy of 96.73% compared to 91.20% for the classical network. This work demonstrates the future implications of quantum integration in the financial field and the quantum advantage it offers on classification tasks.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFN$α$ therapy against Myeloproliferative Neoplasms
Creators: name: Hermange, Gurvan
name: Vainchenker, William
name: Plo, Isabelle
name: Cournède, Paul-Henry
Publisher: name: arXiv
Year: 2021
Subjects: subject: Quantitative Methods (q-bio.QM)
subject: Applications (stat.AP)
subject: Computation (stat.CO)
subject: FOS: Biological sciences
subject: FOS: Computer and information sciences

description: Myeloproliferative Neoplasms (MPN) are blood cancers that appear after acquiring a driver mutation in a hematopoietic stem cell. These hematological malignancies result in the overproduction of mature blood cells and, if not treated, induce a risk of cardiovascular events and thrombosis. Pegylated IFN$α$ is commonly used to treat MPN, but no clear guidelines exist concerning the dose prescribed to patients. We applied a model selection procedure and ran a hierarchical Bayesian inference method to decipher how dose variations impact the response to the therapy. We inferred that IFN$α$ acts on mutated stem cells by inducing their differentiation into progenitor cells; the higher the dose, the higher the effect. We found that the treatment can induce long-term remission when a sufficient (patient-dependent) dose is reached. We determined this minimal dose for individuals in a cohort of patients and estimated the most suitable starting dose to give to a new patient to increase the chances of being cured.
description: 23 pages,11 figures, and 2 tables for the article; the appendix starts on page 25, with 67 additional pages
Palladium-catalyzed hydroxycarbonylation of 1-dodecene in microemulsion systems: does reaction performance care about phase behavior?
Creators: name: Schmidt, Marcel
name: Urban, Carolina
name: Schmidt, Svenja
name: Schomäcker, Reinhard
Publisher: name: Technische Universität Berlin
Year: 2018
Subjects: subject: 540 Chemie und zugeordnete Wissenschaften
subject: 660 Chemische Verfahrenstechnik
subject: acylation
subject: catalysts
subject: organic compounds and functional groups
subject: phase
subject: phase diagram
subject: surfactants

description: Catalysis, particularly metal-catalyzed reactions in microemulsion systems, offers a sustainable approach for organic reactions in water. However, it is still a challenging task because of the complex role of the nonionic surfactant in such a system and the interaction of the phase behavior and reaction performance. To get a profound knowledge of this role and interaction, a systematic study of the palladium-catalyzed hydroxycarbonylation of 1-dodecene in a microemulsion system is reported. The influence of the temperature, additives such as cosolvents, the catalyst concentration, and the hydrophilicity of the surfactant and its concentration has been investigated with regard to both the phase behavior and reaction performance. Interestingly, the investigations reveal that not the phase behavior of the microemulsion system but mainly the dimension of the oil–water interface and the local concentrations of the substrates at this interface, which is provided by the amount and hydrophilicity of the surfactant, control the reaction performance of hydroxycarbonylation in these systems. Moreover, it was found that the local concentration of the active catalyst complex at the interface is essential for the reaction performance. Dependent on the surface active properties of the catalyst complex, its bulk concentration, and the nature and amount of additives, the local concentration of the active catalyst complex at the interface is strongly influenced, which has a huge impact on the reaction performance.
Ferroelectricity in $\mathrm{HfO_2}$ from a chemical perspective
Creators: name: Yuan, Jun-Hui
name: Mao, Ge-Qi
name: Xue, Kan-Hao
name: Bai, Na
name: Wang, Chengxu
name: Cheng, Yan
name: Lyu, Hangbing
name: Sun, Huajun
name: Wang, Xingsheng
name: Miao, Xiangshui
Publisher: name: arXiv
Year: 2022
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: Ferroelectricity observed in thin film $\mathrm{HfO_2}$, either doped with Si, Al, etc. or in the $\mathrm{Hf_{0.5}Zr_{0.5}O_2}$ form, has gained great technical significance. However, the soft mode theory faces a difficulty in explaining the origin of such ferroelectricity. In this work, we propose that the 7 cation coordination number of $\mathrm{HfO_2/ZrO_2}$ lies at the heart of this ferroelectricity, which stems from the proper ionic radii of Hf/Zr compared with O. Among the numerous compounds with non-centrosymmetric nature, e.g., $mm2$ point group, $\mathrm{HfO_2}$ and $\mathrm{ZrO_2}$ are special in that they are close to the border of 7 and 8 cation coordination, such that the 8-coordination tetragonal intermediate phase could greatly reduce the switching barrier. Other 7-coordination candidates, including $\mathrm{SrI_2}$, TaON, YSBr and YOF are also studied in comparison to $\mathrm{HfO_2}$/$\mathrm{ZrO_2}$, and six switching paths are analyzed in detail for the $Pca2_1$ phase. A rule of preferred switching path in terms of ionic radii ratio and coordination number has been established. We also show the possible route from ferroelectric $Pca2_1$ phase to monoclinic $P2_1/c$ phase in $\mathrm{HfO_2}$, which is relevant to the fatigue phenomenon.
description: 14 pages, 4 figures
Current fluctuations for totally asymmetric exclusion on the relaxation scale
Creators: name: Prolhac, Sylvain
Publisher: name: arXiv
Year: 2014
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: Mathematical Physics (math-ph)
subject: FOS: Physical sciences

description: The fluctuations of the current for the one-dimensional totally asymmetric exclusion process with $L$ sites are studied in the relaxation regime of times $T\sim L^{3/2}$. Using Bethe ansatz for the periodic system with an evolution conditioned on special initial and final states, the Fourier transform of the probability distribution of the fluctuations is calculated exactly in the thermodynamic limit $L\to\infty$ with finite density of particles. It is found to be equal to a sum over discrete realizations of a scalar field in a linear potential with coupling constant equal to the rescaled time $T/L^{3/2}$.
description: 9 pages, 2 figures
Non-contractible loops in the dense O(n) loop model on the cylinder
Creators: name: Alcaraz, F. C.
name: Brankov, J. G.
name: Priezzhev, V. B.
name: Rittenberg, V.
name: Rogozhnikov, A. M.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: FOS: Physical sciences

description: A lattice model of critical dense polymers $O(0)$ is considered for the finite cylinder geometry. Due to the presence of non-contractible loops with a fixed fugacity $ξ$, the model is a generalization of the critical dense polymers solved by Pearce, Rasmussen and Villani. We found the free energy for any height $N$ and circumference $L$ of the cylinder. The density $ρ$ of non-contractible loops is found for $N \rightarrow \infty$ and large $L$. The results are compared with those obtained for the anisotropic quantum chain with twisted boundary conditions. Using the latter method we obtained $ρ$ for any $O(n)$ model and an arbitrary fugacity.
description: arXiv admin note: text overlap with arXiv:0810.2231
Early Signs as Effective Indicators of Long-term Procrastination – A Literature Review
Creators: name: AANIKA TANGIRALA
name: AADHITYA RAMESH
name: Boicu, Mihai
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: Procrastination is a widespread issue that has a detrimental impact on one’s academic life and mental health. Thus, it is important to identify early signs in students that correlate with a greater risk of procrastination. Most early-detection procrastination research focuses on the analysis of student activity and interaction with course material to anticipate procrastination. However, the analysis of student-specific traits, such as personality, time perspective, and learning approach, in relation to procrastination habits is less studied. Some previous studies have analyzed the relationship between procrastination and factors such as a student’s personality, self-regulation, and general life satisfaction. These reports used survey data collection both to detect these early signs in participants and to measure procrastination. A variety of Machine Learning techniques were used to analyze this data, including correlational and regression analysis, neural networks, and linear support vector machines. This research found relationships such as procrastination being positively related to neuroticism and significantly predicted by poor time management, self-doubt, and irrational studying beliefs. An experiment will be developed to collect survey data for each of the student-specific traits and student activity data for measuring procrastination; ML models will analyze and explore relationships between these early signs and long-term procrastination.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
Quantum supremacy of the many-body fluctuations in the occupations of the excited particle states in a Bose-Einstein-condensed gas
Creators: name: Kocharovsky, Vitaly V.
name: Kocharovsky, Vladimir V.
name: Tarasov, Sergey V.
Publisher: name: arXiv
Year: 2022
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: Quantum Gases (cond-mat.quant-gas)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: We find a universal analytic formula for a characteristic function (Fourier transform) of a joint probability distribution for the particle occupation numbers in a BEC gas and the Hafnian Master Theorem generalizing the famous Permanent Master Theorem of MacMahon. We suggest an appealing model, a multi-qubit BEC trap formed by a set of qubit potential wells, and discuss specifics of such an atomic boson-sampling system vs a photonic one. Finally, the process of many-body fluctuations in a BEC trap is #P-hard for computing. It could serve as a basis for demonstrating quantum supremacy of the many-body interacting systems over classical simulators.
description: 15 pages, 1 figure
Measuring the Chern number of Hofstadter bands with ultracold bosonic atoms
Creators: name: Aidelsburger, M.
name: Lohse, M.
name: Schweizer, C.
name: Atala, M.
name: Barreiro, J. T.
name: Nascimbène, S.
name: Cooper, N. R.
name: Bloch, I.
name: Goldman, N.
Publisher: name: arXiv
Year: 2014
Subjects: subject: Quantum Gases (cond-mat.quant-gas)
subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Sixty years ago, Karplus and Luttinger pointed out that quantum particles moving on a lattice could acquire an anomalous transverse velocity in response to a force, providing an explanation for the unusual Hall effect in ferromagnetic metals. A striking manifestation of this transverse transport was then revealed in the quantum Hall effect, where the plateaus depicted by the Hall conductivity were attributed to a topological invariant characterizing Bloch bands: the Chern number. Until now, topological transport associated with non-zero Chern numbers has only been revealed in electronic systems. Here we use studies of an atomic cloud's transverse deflection in response to an optical gradient to measure the Chern number of artificially generated Hofstadter bands. These topological bands are very flat and thus constitute good candidates for the realization of fractional Chern insulators. Combining these deflection measurements with the determination of the band populations, we obtain an experimental value for the Chern number of the lowest band $ν_{\mathrm{exp}} =0.99(5)$. This result, which constitutes the first Chern-number measurement in a non-electronic system, is facilitated by an all-optical artificial gauge field scheme, generating uniform flux in optical superlattices.
La pêche de loisir aux filets sur l'estran girondin en 2014
Creators: name: Morandeau, Gilles
Publisher: name: Ifremer
Year: 2015

description: Ce document présente sous forme d'indicateurs synthétiques, l'effort de pêche et les captures réalisés par les pêcheurs à pied (pêche de loisir aux filets) sur l'estran girondin en 2014.
Additional file 2 of Gene networks for three feed efficiency criteria reveal shared and specific biological processes
Creators: name: Taussat, Sébastien
name: Mekki Boussaha
name: Yuliaxis Ramayo-Caldas
name: Martin, Pauline
name: Venot, Eric
name: Cantalapiedra-Hijar, Gonzalo
name: Hozé, Chris
name: Fritz, Sébastien
name: Renand, Gilles
Publisher: name: figshare
Year: 2020
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 2: Fig. S1. Manhattan plots of residual feed intake (a), residual gain (b) and feed efficiency ratio (c).
Developing a Model to Quantify Biological Molecule Cofilin
Creators: name: SIMRAN SALUJA
name: Nadiatou Miningou
name: Blackwell, Kim T
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: The base of learning and long term memory in animals is synaptic plasticity – the ability of neural synapses to strengthen or weaken over time, in response to changes in biological activity. Long-term potentiation (LTP) – a type of synaptic plasticity involves the strengthening of neural synapses. Cofilin, a biological molecule, plays a crucial role in reorganization of actin within the cytoskeleton to support synaptic plasticity. It also cuts off the capping proteins from actin in order to allow further growth. In order to gain a deeper understanding of the molecular basis of learning and memory, a model was developed to analyze cofilin, activated by various temporal patterns of synaptic input. To more precisely quantify cofilin activity, we built functions using python to determine how long cofilin stayed above the concentration threshold, and the area under the curve for this time. These functions analyze cofilin in various parts of the neuron (spine, dendrite, dendrite membrane, etc). We are also able to sum up different molecules within a region, in order to account for different forms of Cofilin. We used github to store code since it is open to the public, provides backup storage, and has version control.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
SCALABLE - D2.2 Report on up to date application performance accuracy and energy efficiency
Creators: name: Staffelbach, Gabriel
name: Vavrik, Radim
name: Vysocky, Ondrej
name: Riha, Lubomir
name: Pouech, Paul
name: Holzer, Markus
Publisher: name: Zenodo
Year: 2021
Subjects: benchmark
performance assessment
energy efficiency assessment
READEX

description: D2.2 - Report on up to date application performance accuracy and energy efficiency
An in-silico based approach to the analysis and determination of de novo peptide sequences and PEAKS efficacy based on LC-MS/MS data
Creators: name: JACIK, SOFIA
name: Werkheiser, Madison
name: Carfagno, Amy
name: Bishop, Barney
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: Currently embargoed. Coming soon!
description: Journal of Student-Scientists' Research, Vol. 5 (2023)
Stimulation of cell invasion by the Golgi Ion Channel GAAP/TMBIM4 via an H2O2-Dependent Mechanism.
Creators: name: Almeida, Nuno
name: Carrara, Guia
name: Palmeira, Carlos M
name: Fernandes, Ana S
name: Parsons, Maddy
name: Smith, Geoffrey L
name: Saraiva, Nuno
Publisher: name: Apollo - University of Cambridge Repository
Year: 2019
Subjects: Metabolism
Calcium
Hydrogen peroxide
Golgi apparatus
cell invasion
Tmbim

description: The mechanisms by which the Golgi apparatus (GA) impacts on cell invasion are poorly understood. The human Golgi Anti-Apoptotic Protein (hGAAP, also known as TMBIM4) is a highly conserved Golgi cation channel that modulates intracellular Ca2+ fluxes. Human GAAP is expressed in all human tissues, is essential for cell viability and provides resistance against a range of apoptotic stresses. Furthermore, hGAAP enhances adhesion and cell migration by increasing the turnover of focal adhesions due to activation of store-operated Ca2+ entry. Here, we describe a GA-derived mechanism that controls cell invasion. The overexpression of hGAAP stimulates 3-dimensional proteolytic cell invasion by a mechanism that is dependent on the accumulation of intracellular hydrogen peroxide, which might be produced by the hGAAP-dependent stimulation of mitochondrial respiration. These findings provide new insight into the complex mechanisms by which Ca2+ and reactive oxygen species signaling contribute to cell invasion and to the role of the GA in these processes.
Information can explain the dynamics of group order in animal collective behaviour
Creators: name: MacGregor, Hannah E. A.
name: Herbert-Read, James E.
name: Ioannou, Christos C.
Publisher: name: Apollo - University of Cambridge Repository
Year: 2020
Subjects: Article
/631/158/856
/631/601/18
article

description: Abstract: Animal groups vary in their collective order (or state), forming disordered swarms to highly polarized groups. One explanation for this variation is that individuals face differential benefits or costs depending on the group’s order, but empirical evidence for this is lacking. Here we show that in three-spined sticklebacks (Gasterosteus aculeatus), fish that are first to respond to an ephemeral food source do so faster when shoals are in a disordered, swarm-like state. This is because individuals’ visual fields collectively cover more of their environment, meaning private information is more readily available in disordered groups. Once social information becomes available, however, the arrival times of subsequent group members to the food are faster in more ordered, polarized groups. Our data further suggest that first responding individuals (those that benefit from group disorder) maintain larger differences in heading angle to their nearest neighbours when shoaling, thereby explaining how conflict over whether private or social information is favoured can drive dynamic changes in collective behaviour.
Incorporating dynamic health behaviors in spatial agent-based models of disease spread
Creators: name: ACHUTHAN, SHIVANI
name: Von Hoene, Emma
name: Anderson, Taylor
Publisher: name: Journal of Student-Scientists' Research
Year: 2023

description: Agent-based models (ABMs) serve as valuable tools to simulate the characteristics and interactions of autonomous agents within their environment. Disease spread ABMs, specifically, are essential to better predict disease outcomes and respond to outbreaks. However, many existing ABMs ignore or generalize the behavior component, a key driver of disease dynamics. Many ABMs that do incorporate behavior often lack a connection to human behavioral theories and data, and implement it in an ad-hoc manner, which oversimplifies the dynamics of human behavior. Therefore, this study aims to expand upon an agent decision-making framework to include individuals' dynamic health behaviors. This framework is based on a logistic regression model using odds ratios to determine the probability of adopting a behavior. To demonstrate dynamic behaviors, mask usage was focused on. Four variables were used to predict this behavior for each agent: income, political party, race, and worry about the disease. The framework was tested in a previously developed spatial ABM that simulates COVID-19 spread among the student population on George Mason University’s campus, specifically examining the impact of individuals’ worry about COVID-19 on mask usage. The spread of worry occurs when an agent is in the same classroom as an already worried agent. Three scenarios were tested changing the probability of becoming worried: 25%, 50% and 100%. Preliminary results show the spread of agent perceptions of the disease, causing individuals to ‘worry,’ has a direct effect on disease outcomes. The more worry among the population, the less cases overall. This framework demonstrates the flexibility to incorporate individuals’ perception based on other variables informed by behavioral constructs and data, making it adaptable to model various infectious diseases and health behaviors. Improving the representation of health behaviors in ABMs of disease spread can help prevent disease outbreaks and guide policy decisions.
description: Journal of Student-Scientists' Research, Vol. 5 (2023)