Digital Commons Network^™

Efficient Internalization Of Nano Architectured 177lu-Hyaluronic Acid@ Zirconium-Based Metal-Organic Framework For The Treatment Of Neuroblastoma: Unravelling Toxicity, Stability, Radiolabelling And Bio-Distribution, Sanjay Kulkarni, Abhijeet Pandey, Soji Soman, Shivanand H. Nannuri

Open Access archive

Zirconium-based metal-organic frameworks (UiO-66) have gained considerable attention owing to their versatile application. In the present research, UiO-66 was synthesized via a defect engineering approach, and its toxicity profile was explored. The synthesized nanomaterial was extensively characterized via spectroscopic methods such as FTIR and Raman spectroscopy, which confirmed the formation of the framework. X-ray diffraction (XRD) and transmission electron microscopy (TEM) were used to determine the crystallinity, shape and size of the nanoformulations. Thermal gravimetric analysis, ¹H NMR spectroscopy and Brunauer–Emmett–Teller (BET) surface area analysis were used to identify the differences between pristine and defective UiO-66. Furthermore, the synthesized …

Deep-Millet: A Deep Learning Model For Pearl Millet Disease Identification To Envisage Precision Agriculture, I. Johnson, X. Anitha Mary, A. Peniel Winifred Raj, J. Chalmers

Open Access archive

Plants are integral to human sustenance, serving as fundamental sources of sustenance, materials, and energy, crucial for economic prosperity. However, their productivity and yield are increasingly threatened by pests and diseases, exacerbated by shifting climatic conditions. Pearl millet, a vital crop in Africa and Asia, is particularly susceptible to a range of diseases including downy mildew, rust, ergot, smut, and blast, posing significant risks to crop yield and quality. Timely and accurate disease identification is paramount for effective management strategies. Traditional methods of disease detection relying on visual identification are laborious, costly, and often require specialized expertise, presenting formidable challenges …

Exploring The Contribution Of Cancer Palliative Care Development Toward Alleviating The Human Crisis Of Suffering In Low- And Middle-Income Countries: A Framework Synthesis Protocol, Shrikant Atreya, Arathi Rao, Vijay Shree Dhyani, Mebin Mathew

Open Access archive

Objectives. Inadequate access to cancer care, high mortality, and out-of-pocket expenditure contribute to health-related suffering in low- and middle-income countries, making palliative care a relevant option. How palliative care development has alleviated suffering is not systematically studied, necessitating this review’s conduct. The objective of this systematic review with a framework synthesis approach is to identify and map the dimensions and indicators of cancer palliative care development and the components of integration between cancer and palliative care in LMICs. Methods. Uni- and multi-disciplinary databases like Cochrane, MEDLINE (PubMed), EMBASE, CINAHL Complete, and PsycINFO will be systematically searched for eligible studies exploring …

Improved Reduced Switch Converter Topology For Torque Ripple Reduction In Four-Phase Switched Reluctance Motor Under Dynamic Loading, Ritika Asati, Deepak S. Bankar, Vinay Kumar Jadoun, Majed A. Alotaibi

Open Access archive

In this paper, simulation, and performance comparison of a switched reluctance motor (SRM) for a classical asymmetric bridge converter and an improved reduced switch converter topology are provided. In an improved converter, switches are shared by adjacent phases, and one diode is connected in series with each phase winding to avoid reversal of current. Double-phase magnetization is adopted for the analysis of both converter topologies. The performance of both converter topologies are demonstrated using a proportional plus integral (PI) based hysteresis current controller and dynamic load on a 7.5 kW, 8/6 pole, 4-phase SRM. Improved reduced switch converter topology uses …

Role Of Absolute Versus Relative Voice Rest In Post-Operative Management Of Benign Vocal Fold Lesions, Mihika Sinha, Suresh Pillai, Shama Shetty, Usha Devadas

Open Access archive

Objective Most otolaryngologists advocate absolute voice rest after laryngeal surgery, which proves difficult for patients, so we decided to evaluate the role of absolute voice rest versus relative voice rest in the post-operative management of benign lesions. Methods Forty patients were recruited and divided in two groups: absolute voice rest and relative voice rest. Pre- and post-operative voice analysis (fundamental frequency, jitter, shimmer, voice handicap index, voice-related quality-of-life scale scores and compliance) were noted at one week and one month. Results Voice analysis parameters including jitter (p = 0.035), shimmer (p = 0.020), voice handicap index (p < 0.001) and compliance (p < 0.001) were better in the relative voice-rest group. Frequency, number of voice breaks and voice-related quality of life showed no statistically significant results. Conclusion There was no significant benefit of absolute voice rest on post-operative outcomes as determined by acoustic variables. Compliance and quality-of-life scores were low in the strict voice-rest group. Therefore, we should reconsider post-surgical voice-rest protocol.

Phage Therapy: A Novel Approach Against Multidrug-Resistant Pathogens, Arushi Kapoor, Samriti Balaji Mudaliar, Vyasraj G. Bhat, Ishita Chakraborty

Open Access archive

The rapid rise of multidrug-resistant (MDR) organisms has created a critical need for alternative treatment options. Phage therapy is gaining attention as an effective way to fight bacterial infections by using lytic bacteriophages to specifically target and kill harmful bacteria. This review discusses several phage therapeutic options and emphasizes new developments in phage biology. Phage treatment has proven to be successful against MDR bacteria, as evidenced by multiple human clinical trials that indicate favorable results in treating a range of diseases caused by these pathogens. Despite these promising results, challenges such as phage resistance, regulatory hurdles, and the need for …

Zero Textbook Cost Syllabus For Bus 9430 (Business Analytics Projects Management), David Messineo

Open Educational Resources

This course delves deep into the pivotal roles, multifaceted opportunities, and inherent challenges faced by organizations aiming to harness business analytics as a competitive edge.

• Business Analytics Frameworks: We'll tap into various frameworks such as Lean, Six Sigma, Benchmarking, Kaizen, Value Chain, Balanced Scorecard, and Strategy Maps. While prior knowledge of these is not mandatory, a little research will prove invaluable when tackling class assignments.

• Project Lifecycle Management: The course draws upon CRISP-DM, Design Thinking, Story-Telling, Systems Thinking, TQM, and Agile/Kanban Methods to instill and enhance a culture of data-driven, evidence-based decision-making in an enterprise.

Prior exposure to these methodologies …

Minutes, Curriculum Committee Meeting, Tuesday, October 01, 2024, Curriculum Committee

Curriculum Committee Minutes

No abstract provided.

Tyranny Of The One, Laurence Claus

Constitutional Commentary, Vol. 37 and later (2023–present)

Book review of Tyranny of the Minority, by Steven Levitsky and Daniel Ziblatt

Excluded But Equal, Gali Racabi

William & Mary Bill of Rights Journal

Work law is excessively exclusionary. Canonical examples include agriculture workers, independent contractors, and domestic workers. But work law excludes far more workers and employers than those familiar categories. Whole sectors, occupations, business sizes, wage arrangements, geographical areas, and more are excluded from the basic tenets of economic citizenship, including federal, state, and local minimum wage; overtime; and safety and labor protection laws.

Equal protection claims govern the authority of legislatures to determine who is in and who is out of work laws. Excluded workers and included employers now routinely use state and federal constitutional equal protection arguments against exclusionary work …

Approaching A U.S. Regulatory Framework For Sporeless Seaweed Biotechnology, Gabriela Martinez, Terra Bowling, Catherine M. Janasie, Charlie Doering-Powell, Amalia Aruda Almada

William & Mary Environmental Law and Policy Review

Seaweed farming is the fastest-growing aquaculture sector in the United States. The development of sterile, selectively bred (sporeless) seaweed aims to support the economic viability of future large offshore seaweed operations and minimize the environmental risks of these operations to wild seaweed beds. Farmers have implemented analogous breeding techniques to control and enhance most terrestrial crops and common aquaculture products (e.g., oysters and carp). This Article draws from existing federal and state aquaculture policies to explore four pillars critical to a future sporeless seaweed regulatory framework: ensuring maintained sterility, tracing the geographic origin of seed, securing offshore permits, and facilitating …

Spatiotemporal Climatology Of Georgia Tropical Cyclones And Associated Rainfall, Reilly Corkran, Jill Trepanier, Vincent Brown

Faculty Publications

Tropical cyclones (TCs), often characterized by high wind speeds and heavy rainfall, cause widespread devastation, affecting millions of people and leading to economic losses worldwide. TC-specific research in Georgia is scarce, likely due to the minimal geographical extent of its coast and the infrequency of direct landfalls. Research on Georgia TCs does not account for storms that make landfall in other southeastern states (e.g., Florida) and continue north, northeast, or northwest into Georgia. This study used the North Atlantic Basin hurricane database (HURDAT2) to quantify the spatiotemporal patterns of direct and indirect landfalling of Georgia tropical cyclones (>16 ms−1) …

Scene Classification Of Remote Sensing Image Based On Multi-Path Reconfigurable Neural Network, Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin, Lei Wang

Faculty Publications

Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to achieve the best performance in scene classification of multiple remote sensing land images. Therefore, to determine which model is the best for the current recognition classification tasks, it is often necessary to select and experiment with many different models. However, finding the optimal model is accompanied by an increase in trial-and-error costs and is a waste of …

The Animo Econometric Model Database Of De La Salle University: Variables And Technical Notes, Eva Marie Aragones, Jesus Felipe, Julian Perez, Mariel Monica Sauler

Angelo King Institute for Economic and Business Studies (AKI)

No abstract provided.

Enzyme Encapsulation Within The Hk97 Virus-Like Particle: An Investigation Of Substrate Inhibition Kinetics Within A Confined And Crowded Environment, Joseph B. Lively

Chemistry Theses

Substrate inhibition is a paradoxical phenomenon observed in enzyme kinetics where increasing substrate concentrations lead to a marked decrease in the rates of enzyme-catalyzed reactions. Affecting an estimated 20% of studied enzymes, substrate inhibition poses significant challenges to the understanding of their function in essential biological processes and to their exploitation in industrial and therapeutic contexts. Studies show substrate inhibition to be a real limitation in vitro and rational conclusions have been drawn to explain the relevance of substrate inhibition in the self-regulation of biological pathways. However, there is currently no consensus on what role substrate inhibition plays in vivo …

Promise And Peril Of Collaborative Code Generation Models : Balancing Effectiveness And Memorization, Zhi Chen, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In the rapidly evolving field of machine learning, training models with datasets from various locations and organizations presents significant challenges due to privacy and legal concerns. The exploration of effective collaborative training settings, which are capable of leveraging valuable knowledge from distributed and isolated datasets, is increasingly crucial. This study investigates key factors that impact the effectiveness of collaborative training methods in code next-token prediction, as well as the correctness and utility of the generated code, showing the promise of such methods. Additionally, we evaluate the memorization of different participant training data across various collaborative training settings, including centralized, federated, …

Pvp-Ssd: Point-Voxel Fusion With Partitioned Point Cloud Sampling For Anchor-Free Single-Stage Small 3d Object Detection, Xinlin Wu, Yibin Tian, Yin Pan, Zhiyuan Zhang, Xuesong Wu, Ruisheng Wang, Zhi Zeng

Research Collection School Of Computing and Information Systems

Single-stage object detection from 3D point clouds in autonomous driving faces significant challenges, particularly in accurately detecting small objects. To address this issue, we propose a novel method called Point-Voxel dual-branch feature extraction with Partitioned point cloud sampling for anchor-free Single-Stage Detection of 3D objects (PVP-SSD). The network comprises two branches: a point branch and a voxel branch. In the point branch, a partitioned point cloud sampling strategy leverages axial features to divide the point cloud. Then, it assigns different sampling weights to various segments to enhance the sampling accuracy. Additionally, a local feature enhancement module explicitly calculates the correlation …

Electrochemical Impedance Spectroscopy Based Discrimination Of Materials Using Artificial Neural Networks, Rob Christian M. Caduyac, John Paolo A. Ramoso, Jose Pocholo R. Arazas, Rock Christian V. Tomas

Journal Article

Electrochemical Impedance Spectroscopy (EIS) is a technique used for determining the electrical properties of a material. EIS generates impedance spectra across a frequency range that is unique in a specific material. However, the discrimination of materials is challenging as EIS produces high-dimensional data. In this study, Artificial Neural Networks (ANN) were used as a machine learning algorithm for classification. Nyquist Plots and Principal Component Analysis (PCA) were done prior classification for data visualization purposes. Results showed that the five materials have different Nyquist plot shapes, and 2D PCA is close to being successful in discriminating all materials. Only Paper and …

The Filipino Family In Agriculture: Case Studies Of Vegetable Farming Families In Nagcarlan, Laguna, Philippines, Sunshine Therese S. Alcantara

Journal Article

Farming in the Philippines is a collective effort involving family members. Filipino farming families play a vital role in sustaining the country's food production. This study employed a qualitative approach to explore how children are socialized into participating in farm work. The research involved in-depth interviews with five families engaged in vegetable farming in an upland community in Nagcarlan, Laguna, Philippines. Thematic analysis was used to identify recurring themes from the interviews. The study highlights that parents are the primary agents of socialization for their children in agriculture. They often teach their children how to farm to prepare them for …

Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu

Research Collection School Of Computing and Information Systems

With the rising awareness of data assets, data governance, which is to understand where data comes from, how it is collected, and how it is used, has been assuming evergrowing importance. One critical component of data governance gaining increasing attention is auditing machine learning models to determine if specific data has been used for training. Existing auditing techniques, like shadow auditing methods, have shown feasibility under specific conditions such as having access to label information and knowledge of training protocols. However, these conditions are often not met in most real-world applications. In this paper, we introduce a practical framework for …

Efficient Cascaded Multiscale Adaptive Network For Image Restoration, Yichen Zhou, Pan Zhou, Teck Khim Ng

Research Collection School Of Computing and Information Systems

Image restoration, encompassing tasks such as deblurring, denoising, and super-resolution, remains a pivotal area in computer vision. However, efficiently addressing the spatially varying artifacts of various low-quality images with local adaptiveness and handling their degradations at different scales poses significant challenges. To efficiently tackle these issues, we propose the novel Efficient Cascaded Multiscale Adaptive (ECMA) Network. ECMA employs Local Adaptive Module, LAM, which dynamically adjusts convolution kernels across local image regions to efficiently handle varying artifacts. Thus, LAM addresses the local adaptiveness challenge more efficiently than costlier mechanisms like self-attention, due to its less computationally intensive convolutions. To construct a …

Calibrated One-Class Classification For Unsupervised Time Series Anomaly Detection, Hongzuo Xu, Yijie Wang, Songlei Jian, Qing Liao, Yongjun Wang, Guansong Pang

Research Collection School Of Computing and Information Systems

Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty …

Gradualreality: Enhancing Physical Object Interaction In Virtual Reality Via Interaction State-Aware Blending, Hyuna Seo, Juheon Yi, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

We present GradualReality, a novel interface enabling a Cross Reality experience that includes gradual interaction with physical objects in a virtual environment and supports both presence and usability. Daily Cross Reality interaction is challenging as the user’s physical object interaction state is continuously changing over time, causing their attention to frequently shift between the virtual and physical worlds. As such, presence in the virtual environment and seamless usability for interacting with physical objects should be maintained at a high level. To address this issue, we present an Interaction State-Aware Blending approach that (i) balances immersion and interaction capability and (ii) …

D2sr: Decentralized Detection, De-Synchronization, And Recovery Of Lidar Interference, Darshana Rathnayake, Hemanth Sabbella, Meera Radhakrishnan, Archan Misra

Research Collection School Of Computing and Information Systems

We address the challenge of multi-LiDAR interference, an issue of growing importance as LiDAR sensors are embedded in a growing set of pervasive devices. We introduce a novel approach named D2SR, enabling decentralized interference detection, mitigation, and recovery without explicit coordination among nearby LiDAR devices. D2SR comprises three stages: (a) Detection, which identifies interfered frames, (b) Mitigation, which performs time-shifting of a LiDAR’s active period to reduce interference, and (c) Recovery, which corrects or reconstructs the depth values in interfered regions of a depth frame. Key contributions include a lightweight interference detection algorithm achieving an F1-score of 92%, a simple …

Rational Catalyst Synthesis For Sustainable Energy Applications, Alaba Upe Ojo

Theses and Dissertations

Energy accounts for two-thirds of the global climate challenges. Effective utilization of green hydrogen or finding cleaner ways to improve energy generation is necessary to combat climate crisis and allow for a more sustainable future. This work evaluates three heterogeneous catalytic processes which contribute to a sustainable future. In chapter 2, we studied the hydrogenation of toluene to methylcyclohexane; a reaction that can potentially serve as a medium for hydrogen storage and help to overcome the safety challenges of transporting gaseous hydrogen. We found that palladium catalysts on certain carbons exhibited significantly enhanced toluene hydrogenation rates compared to activated carbons …

Increasing Engagement And Self-Efficacy Levels In Underachieving 5Th Grade Students: A Qualitative Study, Raney Altman

Theses and Dissertations

Student achievement and mastery of content taught in reading is measured each year in the state of South Carolina. A pattern of low achievement was identified, but more specifically, there were patterns of low engagement and self-efficacy levels in 5th grade students in this study. Because reading is a critical tool needed to participate in all life necessities such as finding work and experiencing interviews, paying bills, and following simple everyday directions, it was evident in student behavior in the classroom that adjustments needed to be made instructionally to ensure success for all students in school and in future endeavors. …

Utilizing Project-Based Learning To Improve Statistical Literacy, Edward Sabat

Theses and Dissertations

This action research study examined the impact of Project-Based Learning (PBL) on statistical literacy among community college students enrolled in introductory statistics courses. The study addressed the challenge students face in interpreting statistical results in real-world contexts, despite achieving correct numerical answers. Utilizing a PBL approach during the hypothesis testing unit, the study employed an explanatory sequential mixed methods design, incorporating a pretest and posttest using the Reasoning and Literacy Instrument (REALI), followed by structured interviews. Quantitative results demonstrated improvement in statistical literacy in all areas assessed on the REALI, particularly in the area of hypothesis testing and p-values. Qualitative …

Final Bpsou Insufficiently Reclaimed Sites Belle Of Butte (Bres No. 8 & Ur-39) Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Large-Scale Graph Label Propagation On Gpus, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun

Research Collection School Of Computing and Information Systems

Graph label propagation (LP) is a core component in many downstream applications such as fraud detection, recommendation and image segmentation. In this paper, we propose GLP, a GPU-based framework to enable efficient LP processing on large-scale graphs. By investigating the data processing pipeline in a large e-commerce platform, we have identified two key challenges on integrating GPU-accelerated LP processing to the pipeline: (1) programmability for evolving application logics; (2) demand for real-time performance. Motivated by these challenges, we offer a set of expressive APIs that data engineers can customize and deploy efficient LP algorithms on GPUs with ease. To achieve …

Motif Graph Neural Network, Xuexin Chen, Ruicui Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao

Research Collection School Of Computing and Information Systems

Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, the existing motif-based GNNs still often suffer from less discriminative power on high-order …