Digital Commons Network^™

Acoustic Wave Manipulation Through Sparse Robotic Actuation, Tristan Shah, Noam Smilovich, Feruza Amirkulova, Samer Gerges, Stas Tiomkin

Faculty Research, Scholarly, and Creative Activity

Recent advancements in robotics, control, and machine learning have facilitated progress in the challenging area of object manipulation. These advancements include, among others, the use of deep neural networks to represent dynamics that are partially observed by robot sensors, as well as effective control using sparse control signals. In this work, we explore a more general problem: the manipulation of acoustic waves, which are partially observed by a robot capable of influencing the waves through spatially sparse actuators. This problem holds great potential for the design of new artificial materials, ultrasonic cutting tools, energy harvesting, and other applications. We develop …

Age-Dependent Brain Responses To Mechanical Stress Determine Resilience In A Chronic Lymphatic Drainage Impairment Model, Zachary Gursky, Zohaib Nisar Khan, Sunil Koundal, Ankita Bhardwaj, Joaquin Caceres Melgarejo, Kaiming Xu, Xinan Chen, Hung-Mo Lin, Xianfeng Gu, Hedok Lee, Jonathan Kipnis, Yoav Dori, Allen Tannenbaum, Laura Santambrogio, Helene Benveniste

2020-Current year OA Pubs

The outflow of 'dirty' brain fluids from the glymphatic system drains via the meningeal lymphatic vessels to the lymph nodes in the neck, primarily the deep cervical lymph nodes (dcLN). However, it is unclear whether dcLN drainage is essential for normal cerebral homeostasis. Using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and computational fluid dynamics, we studied the impact of long-term mechanical stress from compromised dcLN drainage on brain solute and fluid outflow in anesthetized rats. We found that in young, but not middle-aged, rats, impairment of dcLN drainage was linked to moderately increased intracranial pressure and the emergence of extracranial …

Fun! Friends! Famous People! Why Fans Attend Anime Conventions, Billy Tringali, Maria Alberto, Jeremiah Martinez

Proceedings from the Document Academy

In 2021 during the global Covid-19 lockdowns, Billy and Maria ran an IRB-exempted online survey, looking to hear from fans who attend anime conventions. Conventions had been shut down as non-essential services that drew large crowds, and we hoped to capture a screenshot of this moment, to better learn from it in the future. And the resulting data collection went quite well – we were able to partner with a major anime organization to share the survey, and our 1000+ respondents had a lot to say about the conventions they were missing during lockdowns.

Ultimately, we found a significant emphasis …

Thermal Spreader Materials On Flexible Polymer Substrates

Defensive Publications Series

The present disclosure is generally directed to techniques and materials for thermal spreader materials, such as for use in products and applications with flexible polymer-based substrates

The Degree Of Early Grade Supervisors' Practice Of Electronic Supervision In Jerash Governorate Schools In Light Of The Reading And Math Initiative From The Perspective Of School Principals And Early Grade Teachers, Mayson Ghatasheh, Ahmad Rathwan

Jordan Journal of Applied Science-Humanities Series

The study aimed to reveal the degree of practicing electronic supervision by early grades supervisors in Jerash Governorate schools, considering the reading and mathematics initiative, from the perspective of school principals and early grades teachers. To achieve this aim, the descriptive correlational method was used, employing an electronic supervision questionnaire consisting of 25 items. The sample consisted of 325 principals and early grades teachers. The researchers distributed the electronic questionnaire randomly to the sample from schools of education in Jerash Governorate. The results indicated that the degree of electronic supervision practiced by early grades supervisors in light of the reading …

The Effectiveness Of The Constructivist Learning Model In The Academic Achievement Of Regular Students And Students With Learning Difficulties In Mathematics In Inclusive Basic Schools In Amman, Mohammed Sakarneh, Munther Al-Sweilemian

Jordan Journal of Applied Science-Humanities Series

The current study aimed to evaluate the effectiveness of the Constructivist Learning Model in the achievement of fourth-grade students with and without learning disabilities in mathematics in inclusive Jordanian basic schools in Amman. To answer the research questions, the researchers employed a quasi-experimental design, utilizing pre- and post-achievement tests along with the teacher's guide to collect data. Data analysis was conducted using the t-test, one-way ANOVA, and Scheffé post-hoc test. The results indicated that the constructivist theory is effective in teaching both students with and without learning disabilities in the fourth grade in inclusive Jordanian basic schools. Additionally, there were …

Generalized Transversality Conditions For Fuzzy Quantum-Symmetric Variational Problems Via Granular Approach, Martin Bohner, Ewa Girejko, Agnieszka B. Malinowska, Linh Nguyen, Baruch Schneider, Tri Truong

Mathematics and Statistics Faculty Research & Creative Works

This paper investigates fuzzy q-symmetric variational problems with natural boundary conditions. Based on the relative distance measure fuzzy arithmetic and horizontal membership functions (HMFs), we propose novel concepts of differentiability and integrability for fuzzy functions on quantum geometric subsets of real numbers. Then, fundamental foundations of q-symmetric calculus of variations based on HMFs are provided. With the help of HMFs and granular q-symmetric differentiability, we derive necessary optimality conditions for fuzzy q-symmetric variational problems that depend on free endpoints. Moreover, sufficient conditions for minimizers of q-symmetric variational problems are obtained. Some numerical examples illustrating the proposed approach are presented.

Novel Statistical And Topological Data Analyses Of 2d Electronic Images, Robert L. Paige, Vic Patrangenaru

Mathematics and Statistics Faculty Research & Creative Works

In this paper, novel statistical and topological data analyses of 2D images are developed. One considers methodologies based on the Region Covariance Descriptor (RCD) and Topological Data Analysis (TDA) rooted in the simplicial as well as cubical persistent homologies. These methods provide statistical methods for data from populations of complex data objects that are elements of non-Euclidean spaces. The 2D image data considered consist of pictures of two leaves—A and B—from the same tree, twenty of each leaf, from different perspectives. The novel statistical procedures developed are used for correctly determining that leaf A images and leaf B images are …

Risk Allocation Model For Price Escalations In Construction Projects: Integrating Bargaining Game Theory And Probabilistic Bayesian Modeling, Yasser Jezzini, Rayan H. Assaad, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Economic market uncertainties, like those witnessed during pandemics and supply chain-related challenges, introduce complexity in ascertaining precise price estimates for construction materials. Despite some studies on risk allocation in construction projects utilizing quantitative and qualitative approaches, these studies fail to explain the underlying process of risk allocation due to a lack of theoretical support, indicating a need for a more structured and theoretically grounded approach to understanding risk allocation, specifically under volatile market conditions. To bridge this knowledge gap, this study proposes a novel game-theoretical model integrating Bayesian statistics and bargaining game theory dynamics, offering a robust framework for optimal …

Greedy Algorithm For Neural Networks For Indefinite Elliptic Problems, Qingguo Hong, Jiwei Jia, Young Ju Lee, Ziqian Li

Mathematics and Statistics Faculty Research & Creative Works

The paper presents a priori error analysis of the shallow neural network approximation to the solution to the indefinite elliptic equation and a cutting-edge implementation of the Orthogonal Greedy Algorithm (OGA) tailored to overcome the challenges of indefinite elliptic problems, which is a domain where conventional approaches often struggle due to the lack of coerciveness. A rigorous priori error analysis that shows the neural network's ability to approximate the solution of indefinite problems is confirmed numerically by OGA. We also present the error analysis of the relevant numerical quadrature. In particular, massive numerical implementations are conducted to justify the theory, …

Utilizing Generative Ai To Counter Learner Groupthink By Introducing Controversy In Collaborative Problem-Based Learning Settings, Andrew Wiss, Mary Showstark, Kyle Dobbeck, Jennifer Pattershall-Geide, Elke Zschaebitz, Dawn Joosten-Hagye, Kirsten Potter, Erin Embry

Montclair State University Scholarship & Creative Works

This article highlights the foundational challenge of rapid interprofessional student team formation and the potential challenges that groupthink poses for newly-formed teams participating in collaborative problem-based learning activities. This article describes a mixed-methods study that addresses groupthink by introducing a generative artificial intelligence-based agent (genAI agent) into the small group processes of student teams engaging in a session of a well-established virtual interprofessional education methodology. The integration of this novel genAI tool into each student team was an intentional pedagogical technique, introduced in response to the challenges that newly-formed student teams may encounter as they rapidly come together and potentially …

Ds 637-001: Python And Mathematics, Nikita Nemane

Data Science Syllabi

No abstract provided.

Ds 677-001: Deep Learning, Akshay Rangamani

Data Science Syllabi

No abstract provided.

Wofford Today Fall 2025, Wofford College. Office Of Marketing And Communications

Wofford Today

No abstract provided.

It 220-003: Wireless Networks, Dipesh Patel

Informatics Syllabi

No abstract provided.

Phys 102 - All: General Physics I Lecture, Physics Department

Physics Syllabi

No abstract provided.

Opse 301 - 101: Optical Science & Engineering Optics, Tino Hoffman

Physics Syllabi

No abstract provided.

Phys 121 - 001: Physics Ii Lecture, Physics Department

Physics Syllabi

No abstract provided.

Phys 121 - H: Physics Ii Lecture, Tao Zhou

Physics Syllabi

No abstract provided.

Phys 122 - All: Electricity And Magnetism, Tao Zhou

Physics Syllabi

No abstract provided.

Phys 432 - 001: Electromagnetism I, Lindsay Goodwin

Physics Syllabi

No abstract provided.

Phys 621 - 101: Classical Electrodynamics, Satoshi Inoue

Physics Syllabi

No abstract provided.

Cs 610-1j1: Data Structures And Algorithms, Pan Xu

Computer Science Syllabi

No abstract provided.

Cs 610-851: Data Structures And Algorithms, Ali Mili

Computer Science Syllabi

No abstract provided.

Cs 101-001: Computer Programming & Problem Solving, Frank Shih

Computer Science Syllabi

No abstract provided.

Cs 506-851: Foundations Of Computer Science I (Discrete Mathematics), Adrian Ionescu

Computer Science Syllabi

No abstract provided.

Cs 375-001: Introduction To Machine Learning, Khalid Bakhshaliyev

Computer Science Syllabi

No abstract provided.

Cs 482-001 Data Mining, Khalid Bakhshaliyev

Computer Science Syllabi

No abstract provided.

Ece 232-001: Circuits & Systems Ii, Joshua Taylor

Electrical and Computer Engineering Syllabi

No abstract provided.

Ece 252-101: Microprocessors, Azeez Bhavnagarwala

Electrical and Computer Engineering Syllabi

No abstract provided.