Digital Commons Network^™

Utilizing Wearable Technology To Characterize And Facilitate Occupant Collaborations In Flexible Workspaces, Kristi Maisha, Mario Frei, Matias Quintana, Xuan Yun Chua, Rishee Jain, Clayton Miller

Research Collection College of Integrative Studies

Hybrid working strategies have become, and will continue to be, the norm for many offices. This raises two considerations: newly unoccupied spaces needlessly consume energy, and the occupied spaces need to be effectively used to facilitate meaningful interactions and create a positive, sustainable work culture. This work aims to determine when spontaneous, collaborative interactions occur within the building and the environmental factors that facilitate such interactions. This study uses smartwatch-based micro-surveys using the Cozie platform to identify the occurrence of and spatially place interactions while categorizing them as a collaboration or distraction. This method uniquely circumvents pitfalls associated with surveying …

Cohort Comfort Models — Using Occupant’S Similarity To Predict Personal Thermal Preference With Less Data, Matias Quintana, Stefano Schiavon, Federico Tartarini, Joyce Kim, Clayton Miller

Research Collection College of Integrative Studies

Cohort Comfort Models (CCM) are introduced as a technique for creating a personalized thermal prediction for a new building occupant without the need to collect large amounts of individual comfort-related data. This approach leverages historical data collected from a sample population, who have some underlying preference similarity to the new occupant. The method uses background information such as physical and demographic characteristics and one-time onboarding surveys (satisfaction with life scale, highly sensitive person scale, personality traits) from the new occupant, as well as physiological and environmental sensor measurements paired with a few thermal preference responses. The framework was implemented using …

Cozie Apple: An Ios Mobile And Smartwatch Application For Environmental Quality Satisfaction And Physiological Data Collection, Federico Tartarini, Mario Frei, Stefano Schiavon, Xuan Yun Chua, Clayton Miller

Research Collection College of Integrative Studies

Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex to achieve in a reliable, longitudinal, and non-intrusive way. This paper introduces Cozie Apple, an open-source mobile and smartwatch application for iOS devices. This platform allows people to complete a watch-based micro-survey and provide real-time feedback about environmental conditions via their Apple Watch. It leverages the inbuilt sensors of the smartwatch to collect physiological (e.g., heart rate, activity) and environmental (sound level) data. This paper outlines data collected from 48 research participants who used the platform to report perceptions of urban-scale environmental comfort (noise and thermal) …

Leveraging Campus-Scale Wi-Fi Data For Activity-Based Occupant Modeling In Urban Energy Applications, Martín Mosteiro-Romero, Clayton Miller, Matias Quintana, Adrian Chong, Rudi Stouffs

Research Collection College of Integrative Studies

The widespread availability of open datasets in urban areas is transforming how urban energy systems are planned, simulated, and visualized. Urban energy models, however, require an understanding of urban dwellers, as their activities create the demands for energy in buildings. In this paper, we explore using campus-scale Wi-Fi data to identify typical occupant activity patterns as an input to an agent-based model of building occupants at the district scale. The data is taken from a Singaporean university’s Wi-Fi network at high resolution. Each record comprises a timestamp, a device identifier, the location of the device within the campus, and the …

The Building Data Genome Directory – An Open, Comprehensive Data Sharing Platform For Building Performance Research, Xiaoyu Jin, Chun Fu, Hussain Kazmi, Atilla Balint, Ada Canaydin, Matias Quintana, Filip Biljecki, Fu Xiao, Clayton Miller

Research Collection College of Integrative Studies

The building sector plays a crucial role in the worldwide decarbonization effort, accounting for significant portions of energy consumption and environmental effects. However, the scarcity of open data sources is a continuous challenge for built environment researchers and practitioners. Although several efforts have been made to consolidate existing open datasets, no database currently offers a comprehensive collection of building data types with all subcategories and time granularities (e.g., year, month, and sub-hour). This paper presents the Building Data Genome Directory, an open data-sharing platform serving as a one-stop shop for the data necessary for vital categories of building energy research. …

Echoes Of The Zoning: Confronting Legal Realism In The Arguments For Reparations From The Atlantic Slave Trade And Modern-Day Human Trafficking, Glenys P. Spence

Fordham International Law Journal

This Article is based on the premise that modern day human trafficking, like the transatlantic slave trade, violates jus cogens norms, and thus the practice was and still is a violation of US laws under customary international law. The analysis will examine the laws that were applied to chattel slavery in England and her colonies through the lens of some seminal slavery cases to unearth the tyranny of interpretation in human trafficking reparations and liability claims under the current Supreme Court jurisprudence and the Alien Tort Statute (“ATS”). The featured cases will reveal that the same philosophies undergirding the jurisprudence …

Assessment Of The Dimensionless Groups-Based Scale-Up Of Gas–Solid Fluidized Beds, Faraj M. Zaid, Haider Al-Rubaye, Thaar M. Aljuwaya, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

The most common scale-up approach for gas–solids fluidized beds is based on matching the governing dimensionless parameters. in the literature, this approach has been validated only by means of measuring global parameters between different sizes of fluidized beds. However, such global measurements are not sufficient to depict all the interplaying hydrodynamic phenomena and hence verify the scale-up relationships. Therefore, to assess this approach, an advanced gas–solids optical probe and pressure transducer measurement techniques have been applied to quantify local hydrodynamic parameters in two different sized fluidized beds. Four different sets of experimental conditions were designed and conducted to examine the …

Nanomaterial Payload Delivery To Central Nervous System Glia For Neural Protection And Repair, Jayant Saksena, Adelle E. Hamilton, Ryan J. Gilbert, Jonathan M. Zuidema

Chemical and Biochemical Engineering Faculty Research & Creative Works

Central nervous system (CNS) glia, including astrocytes, microglia, and oligodendrocytes, play prominent roles in traumatic injury and degenerative disorders. Due to their importance, active pharmaceutical ingredients (APIs) are being developed to modulate CNS glia in order to improve outcomes in traumatic injury and disease. While many of these APIs show promise in vitro, the majority of APIs that are systemically delivered show little penetration through the blood–brain barrier (BBB) or blood-spinal cord barrier (BSCB) and into the CNS, rendering them ineffective. Novel nanomaterials are being developed to deliver APIs into the CNS to modulate glial responses and improve outcomes in …

Advances In Bioelectrode Design For Developing Electrochemical Biosensors, Nabajyoti Kalita, Sudarshan Gogoi, Shelley D. Minteer, Pranab Goswami

Chemistry Faculty Research & Creative Works

The critical performance factors such as selectivity, sensitivity, operational and storage stability, and response time of electrochemical biosensors are governed mainly by the function of their key component, the bioelectrode. Suitable design and fabrication strategies of the bioelectrode interface are essential for realizing the requisite performance of the biosensors for their practical utility. A multifaceted attempt to achieve this goal is visible from the vast literature exploring effective strategies for preparing, immobilizing, and stabilizing biorecognition elements on the electrode surface and efficient transduction of biochemical signals into electrical ones (i.e., current, voltage, and impedance) through the bioelectrode interface with the …

Evaluating The Relative Influence Of Soil Water Potential, Soil Moisture, And Vapor Pressure Deficit On Semi-Arid Vegetation Dynamics, Kayla R. Jamerson

Graduate Student Theses, Dissertations, & Professional Papers

Knowledge of vegetation’s response to soil water availability and atmospheric demand is critical to understanding the impact of climate change on semi-arid ecosystems. However, limited field-based research has been conducted to assess the relative importance of these drivers and previous research has simplified the assessment of soil water availability by relying on soil volumetric water content (VWC) as a primary control on plant growth, which, as opposed to soil water potential (Ψsoil), does not account for the effects of soil texture on plant available water. To address these gaps, we compared remotely sensed indicators of vegetation response to field based …

An Explainable Deep Learning Model For Prediction Of Severity Of Alzheimer's Disease, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi

Chemistry Faculty Research & Creative Works

Deep Convolutional Neural Networks (CNNs) have become the go-To method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer's disease (AD). AD diagnosis and prognosis heavily rely …

Conditioning On Parental Mating Types Can Reduce Necessary Assumptions For Mendelian Randomization, Keisuke Ejima, Nianjun Liu, Luis Miguel Mestre, Gustavo De Los Campos, David B Allison

Children’s Nutrition Research Center Staff Publications

Mendelian randomization (MR) has become a common tool used in epidemiological studies. However, when confounding variables are correlated with the instrumental variable (in this case, a genetic/variant/marker), the estimation can remain biased even with MR. We propose conditioning on parental mating types (a function of parental genotypes) in MR to eliminate the need for one set of assumptions, thereby plausibly reducing such bias. We illustrate a situation in which the instrumental variable and confounding variables are correlated using two unlinked diallelic genetic loci: one, an instrumental variable and the other, a confounding variable. Assortative mating or population admixture can create …

Environmentally-Aware And Energy-Efficient Multi-Drone Coordination And Networking For Disaster Response, Chengyi Qu, Francesco Betti Sorbelli, Rounak Singh, Prasad Calyam, Sajal K. Das

Computer Science Faculty Research & Creative Works

In a Disaster Response Management (DRM) Scenario, Communication and Coordination Are Limited, and Absence of Related Infrastructure Hinders Situational Awareness. Unmanned Aerial Vehicles (UAVs) or Drones Provide New Capabilities for DRM to Address These Barriers. However, There is a Dearth of Works that Address Multiple Heterogeneous Drones Collaboratively Working Together to Form a Flying Ad-Hoc Network (FANET) with Air-To-Air and Air-To-Ground Links that Are Impacted By: (I) Environmental Obstacles, (Ii) Wind, and (Iii) Limited Battery Capacities. in This Paper, We Present a Novel Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking Scheme that Features a Reinforcement Learning (RL) based Location Prediction …

Programmable Software-Defined Testbed For Visible Light Uav Networks: Architecture Design And Implementation, Yue Zhang, Nan Cen

Computer Science Faculty Research & Creative Works

As of Today, There Has Been Increasing Research on Designing Optimization Algorithms and Intelligent Network Control Methods for Visible Light Unmanned Aerial Vehicles (UAV) Networks to Provide Pervasive and Broadband Connections. for Those Theoretical Analysis based Algorithms, there is an Urgent Need to Have a Visible Light UAV Network Platform that Can Help Evaluate the Proposed Algorithms in Real-World Scenarios. However, to the Best of Our Knowledge, there is Currently No Dedicated High Data Rate and Flexible Visible Light UAV Networking Prototype. to Bridge This Gap, in This Paper, We First Design a Novel Programmable Software-Defined Architecture for Visible Light …

Sigmoid Activation-Based Long Short-Term Memory For Time Series Data Classification, Sajal Das

Computer Science Faculty Research & Creative Works

With the enhanced usage of Artificial Intelligence (AI) driven applications, the researchers often face challenges in improving the accuracy of the data classification models, while trading off the complexity. In this paper, we address the classification of time series data using the Long Short-Term Memory (LSTM) network while focusing on the activation functions. While the existing activation functions such as sigmoid and tanh are used as LSTM internal activations, the customizability of these activations stays limited. This motivates us to propose a new family of activation functions, called log-sigmoid, inside the LSTM cell for time series data classification, and analyze …

Unifying Threats Against Information Integrity In Participatory Crowd Sensing, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

This article proposes a unified threat landscape for participatory crowd sensing (P-CS) systems. Specifically, it focuses on attacks from organized malicious actors that may use the knowledge of P-CS platform's operations and exploit algorithmic weaknesses in AI-based methods of event trust, user reputation, decision-making, or recommendation models deployed to preserve information integrity in P-CS. We emphasize on intent driven malicious behaviors by advanced adversaries and how attacks are crafted to achieve those attack impacts. Three directions of the threat model are introduced, such as attack goals, types, and strategies. We expand on how various strategies are linked with different attack …

Reward Maximization For Disaster Zone Monitoring With Heterogeneous Uavs, Wenzheng Xu, Chengxi Wang, Hongbin Xie, Weifa Liang, Haipeng Dai, Zichuan Xu, Ziming Wang, Bing Guo, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we study the deployment of $K$ heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of $K$ heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one …

Robust Federated Learning Against Backdoor Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning is a Privacy-Preserving Alter-Native for Distributed Learning with No Involvement of Data Transfer. as the Server Does Not Have Any Control on Clients' Actions, Some Adversaries May Participate in Learning to Introduce Corruption into the Underlying Model. Backdoor Attacker is One Such Adversary Who Injects a Trigger Pattern into the Data to Manipulate the Model Outcomes on a Specific Sub-Task. This Work Aims to Identify Backdoor Attackers and to Mitigate their Effects by Isolating their Weight Updates. Leveraging the Correlation between Clients' Gradients, We Propose Two Graph Theoretic Algorithms to Separate Out Attackers from the Benign Clients. under …

Message From The Ieee Mdm 2023 Test-Of-Time Committee, Christian S. Jensen, Sanjay Kumar Madria, Timos Sellis

Computer Science Faculty Research & Creative Works

No abstract provided.

Q-Learning For Sum-Throughput Optimization In Wireless Visible-Light Uav Networks, Yuwei Long, Nan Cen

Computer Science Faculty Research & Creative Works

Unmanned Aerial Vehicles (UAVs) Have Been Adopted as Aerial Base Stations (ABSs) to Provide Wireless Connectivity to Ground Users in Events of Increased Network Demand, and Points-Of-Failure Infrastructure (Such as in Disasters). However, with the Existing Crowded Radio Frequency (RF) Spectrum, UAV ABSs Cannot Provide High-Data-Rate Communication Required in 5G and beyond. to Address This Challenge, Visible Light Communication (VLC) is Proposed to Be Equipped on UAVs to Take Advantage of the Flexible and On-Demand Deployment Feature of the UAV, and the High-Data-Rate Communication of the VLC. However, VLC Has Strong Alignment Requirements between Transceivers, Therefore, How to Determine the …

Lightesd: Fully-Automated And Lightweight Anomaly Detection Framework For Edge Computing, Ronit Das, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

Anomaly Detection is Widely Used in a Broad Range of Domains from Cybersecurity to Manufacturing, Finance, and So On. Deep Learning based Anomaly Detection Has Recently Drawn Much Attention Because of its Superior Capability of Recognizing Complex Data Patterns and Identifying Outliers Accurately. However, Deep Learning Models Are Typically Iteratively Optimized in a Central Server with Input Data Gathered from Edge Devices, and Such Data Transfer between Edge Devices and the Central Server Impose Substantial overhead on the Network and Incur Additional Latency and Energy Consumption. to overcome This Problem, We Propose a Fully Automated, Lightweight, Statistical Learning based Anomaly …

A Distributed Algorithm For Identifying Strongly Connected Components On Incremental Graphs, S. Srinivasan, A. Khanda, S. Srinivasan, A. Pandey, S. (Sajal) K. Das, S. Bhowmick, B. Norris

Computer Science Faculty Research & Creative Works

Incremental graphs that change over time capture the changing relationships of different entities. Given that many real-world networks are extremely large, it is often necessary to partition the network over many distributed systems and solve a complex graph problem over the partitioned network. This paper presents a distributed algorithm for identifying strongly connected components (SCC) on incremental graphs. We propose a two-phase asynchronous algorithm that involves storing the intermediate results between each iteration of dynamic updates in a novel meta-graph storage format for efficient recomputation of the SCC for successive iterations. To the best of our knowledge, this is the …

Optimizing Federated Learning In Leo Satellite Constellations Via Intra-Plane Model Propagation And Sink Satellite Scheduling, Mohamed Elmahallawy, Tie (Tony) T. Luo

Computer Science Faculty Research & Creative Works

The advances in satellite technology developments have recently seen a large number of small satellites being launched into space on Low Earth orbit (LEO) to collect massive data such as Earth observational imagery. The traditional way which downloads such data to a ground station (GS) to train a machine learning (ML) model is not desirable due to the bandwidth limitation and intermittent connectivity between LEO satellites and the GS. Satellite edge computing (SEC), on the other hand, allows each satellite to train an ML model onboard and uploads only the model to the GS which appears to be a promising …

Promoting Enhanced Decision-Making And Proactive Management For Modularization In The Construction Industry Using Risk-Based Approach, Analytical Hierarchy Process, And Graph Theory, Mohamad Abdul Nabi

Doctoral Dissertations

"Modularization is an approach that can potentially mitigate many of the current construction industry’s challenges. Despite the documented advantages of modularization, the industry is still not able to capitalize on its full potential and benefits. The latter is due to the outdated decision-making models that may not reflect the multiple factors involved in modular construction, the unique source of risks associated with modular construction methods, need for continuous improvement, and complex contractual requirements and disputes. As such, the goal is to promote the use of modularization in the construction industry by enhancing decision-making and proactive risk management. The associated objectives …

Impact Of A Respiratory Health Program On Attitudes, Knowledge, And Behaviors Related To Respirator Use In Farmers And Ranchers, Rebecca White

Doctor of Nursing Practice (DNP) Practice Innovation Projects

Background: Studies show an association between agricultural occupational hazards and respiratory illnesses. Personal protective equipment (PPE) has protective effects, but limited education and inadequate respiratory protection are common among agricultural workers.
Methods: A demographic questionnaire and two pre-intervention surveys were initially administered to farmers and ranchers. The surveys measured attitude, knowledge, and behaviors related to respiratory safety and PPE practices. Pulmonary function testing, educational handouts, and respirator fit testing was provided in a variety of settings, and one N95 mask was given to each participant. Two surveys were distributed 3 months post-intervention.
Results: The pre- and post-intervention surveys were matched …

Clinical Care Protocol For Enhanced Nurse Competency In Caring For The Patient With An External Ventricular Drainage (Evd) Device, Lauren Orson

Doctor of Nursing Practice (DNP) Practice Innovation Projects

Background: The focus of this Doctor of Nursing Practice Project surrounds the need for an evidence-based clinical nursing protocol to augment nurse competency and therefore streamline care to patients with an external ventricular drain (EVD).
Methods: A clinical protocol alongside tailored education was used to promote nurse competency for EVD management. A pre-implementation survey was completed evaluating the nurses’ baseline competency when managing an EVD. The nursing protocol was then implemented for bedside use. After 2 months, the survey was completed once again and paired for statistical analysis.
Results: Statistical analysis using the Wilcoxon signed-rank nonparametric test with dependent matched …

Analysis Of Forensic Relevant Cell Mixtures Using Cellular Autofluorescence And Fluorescence Activated Cell Sorting (Facs), Shayla Smith

Master of Science in Forensic Science Directed Research Projects

Biological mixtures containing DNA from more than one contributor can make DNA profile interpretation challenging, especially for ‘touch’ or trace biological evidence. One approach to improve this is by differentiating cell populations from separate individuals prior to DNA profiling by utilizing intrinsic features of cells such as size, shape, and biochemical profiles. The goal of this study was to develop a new method for detecting and separating contributor populations in biological mixture samples using flow cytometry, a rapid and non-destructive approach with an unexplored potential for forensic casework. To accomplish this, two different mixture systems were investigated (1) mixtures containing …

A Quality Improvement Initiative: Addressing Workplace Violence Toward Nurses To Promote A Culture Of Safety In An Inpatient Psychiatric Facility, Ernestina Danso

DNP Scholarly Projects

This quality improvement initiative aimed to address workplace violence against nurses in inpatient psychiatric facilities and promote a culture of safety. The QI project involved 27 participants, including inpatient psychiatric nurses and mental health counselors from Cahill 4 and 7 West units at the Cambridge Campus. The primary objective was to identify challenges that hindered the effective utilization of the Broset Violence Checklist (BVC) tool for referring patients to sensory modulation rooms.

Pre- and post-intervention surveys were conducted to assess participants' self-reported knowledge and barriers to BVC and sensory room utilization. Educational sessions covering workplace violence, debriefing techniques, and the …

An Effective Transfer Learning Based Landmark Detection Framework For Uav-Based Aerial Imagery Of Urban Landscapes, Bishwas Praveen, Vineetha Menon, Tathagata Mukherjee, Bryan Mesmer, Sampson Gholston, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

Aerial imagery captured through airborne sensors mounted on Unmanned Aerial Vehicles (UAVs), aircrafts, satellites, etc. in the form of RGB, LiDAR, multispectral or hyperspectral images provide a unique perspective for a variety of applications. These sensors capture high-resolution images that can be used for applications related to mapping, surveying, and monitoring of crops, infrastructure, and natural resources. Deep learning based algorithms are often the forerunners in facilitating practical solutions for such data-centric applications. Deep learning-based landmark detection is one such application which involves the use of deep learning algorithms to accurately identify and locate landmarks of interest in images captured …

Tennessee Archivist 42, No. 1, Society Of Tennessee Archivists

Tennessee Archivist newsletters

Winter 2023 issue of the Tennessee Archivist, a newsletter produced by the Society of Tennessee Archivists.