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Articles 1021 - 1050 of 1803
Full-Text Articles in Computer Sciences
Automatic Recognition, Segmentation, And Sex Assignment Of Nocturnal Asthmatic Coughs And Cough Epochs In Smartphone Audio Recordings: Observational Field Study, Filipe Barata, Peter Tinschert, Frank Rassouli, Claudia Steurer-Stey, Elgar Fleisch, Milo Puhan, Martin Brutsche, David Kotz, Tobias Kowatsch
Automatic Recognition, Segmentation, And Sex Assignment Of Nocturnal Asthmatic Coughs And Cough Epochs In Smartphone Audio Recordings: Observational Field Study, Filipe Barata, Peter Tinschert, Frank Rassouli, Claudia Steurer-Stey, Elgar Fleisch, Milo Puhan, Martin Brutsche, David Kotz, Tobias Kowatsch
Dartmouth Scholarship
Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio …
Not Intelligent: Encoding Gender Bias, Cara Tenenbaum
Not Intelligent: Encoding Gender Bias, Cara Tenenbaum
Minnesota Journal of Law, Science & Technology
No abstract provided.
Β-Amyloid And Tau Drive Early Alzheimer's Disease Decline While Glucose Hypometabolism Drives Late Decline, Tyler C. Hammond, Xin Xing, Chris Wang, David Ma, Kwangsik Nho, Paul K. Crane, Fanny Elahi, David A. Ziegler, Gongbo Liang, Qiang Cheng, Lucille M. Yanckello, Nathan Jacobs, Ai-Ling Lin
Β-Amyloid And Tau Drive Early Alzheimer's Disease Decline While Glucose Hypometabolism Drives Late Decline, Tyler C. Hammond, Xin Xing, Chris Wang, David Ma, Kwangsik Nho, Paul K. Crane, Fanny Elahi, David A. Ziegler, Gongbo Liang, Qiang Cheng, Lucille M. Yanckello, Nathan Jacobs, Ai-Ling Lin
Sanders-Brown Center on Aging Faculty Publications
Clinical trials focusing on therapeutic candidates that modify β-amyloid (Aβ) have repeatedly failed to treat Alzheimer’s disease (AD), suggesting that Aβ may not be the optimal target for treating AD. The evaluation of Aβ, tau, and neurodegenerative (A/T/N) biomarkers has been proposed for classifying AD. However, it remains unclear whether disturbances in each arm of the A/T/N framework contribute equally throughout the progression of AD. Here, using the random forest machine learning method to analyze participants in the Alzheimer’s Disease Neuroimaging Initiative dataset, we show that A/T/N biomarkers show varying importance in predicting AD development, with elevated biomarkers of Aβ …
Nobiletin Affects Circadian Rhythms And Oncogenic Characteristics In A Cell-Dependent Manner, Sujeewa S. Lellupitiyage Don, Kelly L. Robertson, Hui-Hsien Lin, Caroline Labriola, Mary E. Harrington, Stephanie R. Taylor, Michelle E. Farkas
Nobiletin Affects Circadian Rhythms And Oncogenic Characteristics In A Cell-Dependent Manner, Sujeewa S. Lellupitiyage Don, Kelly L. Robertson, Hui-Hsien Lin, Caroline Labriola, Mary E. Harrington, Stephanie R. Taylor, Michelle E. Farkas
Psychology: Faculty Publications
The natural product nobiletin is a small molecule, widely studied with regard to its therapeutic effects, including in cancer cell lines and tumors. Recently, nobiletin has also been shown to affect circadian rhythms via their enhancement, resulting in protection against metabolic syndrome. We hypothesized that nobiletin’s anti-oncogenic effects, such as prevention of cell migration and formation of anchorage independent colonies, are correspondingly accompanied by modulation of circadian rhythms. Concurrently, we wished to determine whether the circadian and anti-oncogenic effects of nobiletin differed across cancer cell lines. In this study, we assessed nobiletin’s circadian and therapeutic characteristics to ascertain whether these …
Distributed Adaptive State Estimation And Tracking Scheme For Nonlinear Systems Using Active Passive Sensor Networks, Akhilesh Raj, S. Jagannathan, Tansel Yucelen
Distributed Adaptive State Estimation And Tracking Scheme For Nonlinear Systems Using Active Passive Sensor Networks, Akhilesh Raj, S. Jagannathan, Tansel Yucelen
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes a novel adaptive neural network (NN) based distributed state estimation scheme for a heterogeneous sensor network (HSN), to estimate the state vector of an unknown nonlinear process/target by using sensed output when the target input remains unknown. The active nodes in the HSN can sense the target output based on the detection range. By using a connected graph, the active nodes will communicate their estimated state vector from their adaptive NN observer to other passive nodes in the neighborhood that cannot sense the target, so that they can estimate the target state vector. Next, a subset of …
Optimal Control Of Linear Continuous-Time Systems In The Presence Of State And Input Delays With Application To A Chemical Reactor, Rohollah Moghadam, Sarangapani Jagannathan
Optimal Control Of Linear Continuous-Time Systems In The Presence Of State And Input Delays With Application To A Chemical Reactor, Rohollah Moghadam, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, the optimal regulation of linear continuous-time systems with state and input delays is introduced by utilizing a quadratic cost function and state feedback. The Lyapunov-Krakovskii functional incorporating state and input delays is defined as a value function. Next, the Bellman type equation is formulated, and a delay Algebraic Riccati equation (DARE) over infinite time horizon is derived. By using the stationarity condition for the Bellman type equation, the optimal control input is obtained. It is demonstrated that the proposed optimal control input makes the closed-loop system asymptotically stable. Finally, simulation results confirm the theoretical claims by applying …
Dynamic Trajectory Generation And A Robust Controller To Intercept A Moving Ball In A Game Setting, Ravi Prakash, Laxmidhar Behera, Santhakumar Mohan, Sarangapani Jagannathan
Dynamic Trajectory Generation And A Robust Controller To Intercept A Moving Ball In A Game Setting, Ravi Prakash, Laxmidhar Behera, Santhakumar Mohan, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
Complex and interactive robot manipulation skills, such as playing a game of table tennis against a human opponent, are a novel problem with multifaceted challenges. Accurate dynamic trajectory generation in order to respond to the tennis ball from the opponent and a novel control scheme for robust and high-performance tracking of the ball in such dynamic situations is a prerequisite to winning the game. In this paper, the dynamic movement primitives (DMPs) are employed for the stable generation of dynamic trajectories in the presence of environmental uncertainties such as ball position and velocity, opponent position and velocity and so on. …
Addressing Parameter Uncertainty In Sd Models With Fit-To-History And Monte-Carlo Sensitivity Methods, Wayne Wakeland, Jack Homer
Addressing Parameter Uncertainty In Sd Models With Fit-To-History And Monte-Carlo Sensitivity Methods, Wayne Wakeland, Jack Homer
Complex Systems Faculty Publications and Presentations
We present a practical guide, including a step-by-step flowchart, for establishing uncertainty intervals for key model outcomes in the face of uncertain parameters. The process starts with Powell optimization (e.g., using VensimTM) to find a set of uncertain parameters (the “optimum” parameter set or OPS) that minimize the model fitness error relative to available reference behavior data. The optimization process also helps in refinement of assumed parameter uncertainty ranges. Next, Markov Chain Monte Carlo (MCMC) or conventional Monte Carlo (MC) randomization is used to create a sample of parameter sets that fit the reference behavior data nearly as well as …
Availability-Resilient Control Of Uncertain Linear Stochastic Networked Control Systems, Chandreyee Bhowmick, S. Jagannathan
Availability-Resilient Control Of Uncertain Linear Stochastic Networked Control Systems, Chandreyee Bhowmick, S. Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
The resilient output feedback control of linear networked control (NCS) system with uncertain dynamics in the presence of Gaussian noise is presented under the denial of service (DoS) attacks on communication networks. The DoS attacks on the sensor-to-controller (S-C) and controller-to-actuator (C-A) networks induce random packet losses. The NCS is viewed as a jump linear system, where the linear NCS matrices are a function of induced losses that are considered unknown. A set of novel correlation detectors is introduced to detect packet drops in the network channels using the property of Gaussian noise. By using an augmented system representation, the …
Online Optimal Adaptive Control Of A Class Of Uncertain Nonlinear Discrete-Time Systems, Rohollah Moghadam, Pappa Natarajan, Krishnan Raghavan, Sarangapani Jagannathan
Online Optimal Adaptive Control Of A Class Of Uncertain Nonlinear Discrete-Time Systems, Rohollah Moghadam, Pappa Natarajan, Krishnan Raghavan, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a multi-layer neural network (MNN) based online optimal adaptive regulation of a class of nonlinear discrete-time systems in affine form with uncertain internal dynamics is introduced. The multi-layer neural networks (MNN)-based actor-critic framework is utilized to estimate the optimal control input and cost function. The temporal difference (TD) error is derived from the difference between actual and estimated cost function. The MNN weights of both critic and actor are tuned at every sampling instant as a function of the instantaneous temporal difference and control policy errors. The proposed approach does not require the selection of any basis …
Identification Of Microscopy Cell Images By Using Convolutional Neural Network Application, Ajis Norfatin Farisya
Identification Of Microscopy Cell Images By Using Convolutional Neural Network Application, Ajis Norfatin Farisya
Student Works (2020-2029)
Breast cancer has been the major factor of cancer death and the second main cause of women’s deaths in the world. The false positive results of this cancer cell detection during the screening test leads to false treatment and emotional disturbance of the patients. Thus, breast cancer cell lines (MCF7) is used as the microscopy image samples together with the Human Bone Osteosarcoma Epithelial Cells (U2OS), and Human Hepatocyte as control to study the effectiveness of convolutional neural network (CNN) as a method of image recognition. The objectives of this study are to determine the ability of convolutional neural network …
An Improved Bone Age Assessment Using Advanced Image Processing And Deep Learning Approach, Kim Meng Liang
An Improved Bone Age Assessment Using Advanced Image Processing And Deep Learning Approach, Kim Meng Liang
Student Works (2020-2029)
Pediatricians often apply bone age assessment to measure the skeletal maturity of children and to predict the future height. These discrepancies are good indicators for diagnosing growth disorders. Normally, left hand skeletal is employed in this assessment. The low quality of ossification sites of carpals deteriorates the pediatrician’s visibility in inspecting the pertinent radiographic manifestations. This in turn affects the bone age assessment. Therefore, we have to enhance the quality before assessing them. Histogram equalization is one of the contrast enhancement techniques that suit this type of enhancement. Existing histogram equalizations, however, are confronting with problems in preserving the brightness …
Covid-19 Pandemic: Role Of Technology In Transforming Business To The New Normal, Fiona Fui-Hoon Nah, Keng Siau
Covid-19 Pandemic: Role Of Technology In Transforming Business To The New Normal, Fiona Fui-Hoon Nah, Keng Siau
Research Collection School Of Computing and Information Systems
COVID-19 has disrupted our lives and the economy. In this paper, we outline approaches in which information technology can be used to implement business strategies to enhance resilience by coping with, adapting to, and recovering from adversity resulting from the COVID-19 pandemic. We discuss how information technology such as digital supply chain, data analytics, artificial intelligence, machine learning, robotics, digital commerce, and Internet of Things can be used to enhance resilience and continuity of business.
Busting Myths And Dispelling Doubts About Covid-19, Mark Findlay
Busting Myths And Dispelling Doubts About Covid-19, Mark Findlay
Research Collection Yong Pung How School Of Law
The Centre for AI and Data Governance (CAIDG) at Singapore Management University (SMU) has embarked over past months on a programme of research designed to confront concerns about the pandemic and its control. Our interest is primarily directed to the ways in which AI-assisted technologies and mass data sharing have become a feature of pandemic control strategies. We want to know what impact these developments are having on community confidence and health safety. In developing this work, we have come across many myths that need busting.
Covid-19 Calls For Remote Reskilling And Retraining, C. Zou, W. Zhao, Keng Siau
Covid-19 Calls For Remote Reskilling And Retraining, C. Zou, W. Zhao, Keng Siau
Research Collection School Of Computing and Information Systems
Cui Zou, Wangchuchu Zhao, and Keng Siau respond directly to COVID-19 by framing the skills and training necessary to survive crises. The authors focus on the importance of helping organizations prepare beyond the current pandemic by teaching everyone how to use the technology tools -- and exploit the processes -- around remote working.
Camps: Efficient And Privacy-Preserving Medical Primary Diagnosis Over Outsourced Cloud, Jianfeng Hua, Guozhen Shi, Hui Zhu, Fengwei Wang, Ximeng Liu, Hao Li
Camps: Efficient And Privacy-Preserving Medical Primary Diagnosis Over Outsourced Cloud, Jianfeng Hua, Guozhen Shi, Hui Zhu, Fengwei Wang, Ximeng Liu, Hao Li
Research Collection School Of Computing and Information Systems
With the flourishing of ubiquitous healthcare and cloud computing technologies, medical primary diagnosis system, which forms a critical capability to link big data analysis technologies with medical knowledge, has shown great potential in improving the quality of healthcare services. However, it still faces many severe challenges on both users' medical privacy and intellectual property of healthcare service providers, which deters the wide adoption of medical primary diagnosis system. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis framework (CAMPS). Within CAMPS framework, the precise diagnosis models are outsourced to the cloud server in an encrypted manner, and …
Lightweight And Privacy-Aware Fine-Grained Access Control For Iot-Oriented Smart Health, Jianfei Sun, Hu Xiong, Ximeng Liu, Yinghui Zhang, Xuyun Nie, Robert H. Deng
Lightweight And Privacy-Aware Fine-Grained Access Control For Iot-Oriented Smart Health, Jianfei Sun, Hu Xiong, Ximeng Liu, Yinghui Zhang, Xuyun Nie, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the booming of Internet of Things (IoT), smart health (s-health) is becoming an emerging and attractive paradigm. It can provide an accurate prediction of various diseases and improve the quality of healthcare. Nevertheless, data security and user privacy concerns still remain issues to be addressed. As a high potential and prospective solution to secure IoT-oriented s-health applications, ciphertext policy attribute-based encryption (CP-ABE) schemes raise challenges, such as heavy overhead and attribute privacy of the end users. To resolve these drawbacks, an optimized vector transformation approach is first proposed to efficiently transform the access policy and user attribute set into …
Active Deep Learning Method To Automate Unbiased Stereology Cell Counting, Saeed Alahmari
Active Deep Learning Method To Automate Unbiased Stereology Cell Counting, Saeed Alahmari
USF Tampa Graduate Theses and Dissertations
Cell quantification in histopathology images plays a significant role in understanding and diagnosing diseases such as cancer and Alzheimers. The gold-standard for quantifying cells in tissue sections is the unbiased stereology approach. Unfortunately, in unbiased stereology current practices rely on a well-trained human to manually count hundreds of cells in microscopy images. However, this human-based manual approach is time-consuming, labor-intensive, subject to human errors, recognition bias, fatigue, variable training, poor reproducibility, and inter-observer error. Thus, the lack of high-throughput technology for automating unbiased stereology analyses remains a major obstacle to further progress in a wide range of neuroscience and cancer …
Synthesis, Characterisation And Biological Evaluation Of Tyramine Derived Schiff Base Ligand And Its Transition Metal(Ii) Complexes, Abdul Khader Jailani, N.S.K. Gowthaman, Mookkandi Palsamy Kesavan
Synthesis, Characterisation And Biological Evaluation Of Tyramine Derived Schiff Base Ligand And Its Transition Metal(Ii) Complexes, Abdul Khader Jailani, N.S.K. Gowthaman, Mookkandi Palsamy Kesavan
Karbala International Journal of Modern Science
In this study, a new tyramine derived Schiff base ligand (L) (L=1,3-phenylene-bis-4-aminoantipyrinyl-4-aminoethylphenol) and its derived transition metal(II) complexes [Cu(L)Cl2](1), [Ni(L)Cl2](2), [Co(L)Cl2] (3) and [Zn(L)Cl2] (4) have been synthesized and well characterized by the way of different spectroscopic and analytical techniques. Analytical and spectroscopic studies result suggests that metal(II) complexes more probably have octahedral geometry. DNA binding tendency of L and metal(II) complexes 1-4 have been assessed by probing their ability to bind with Calf Thymus DNA (CT-DNA) via electronic absorption and cyclic voltammetry titration methods. The results clearly reveal that the metal(II) …
Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience, Mohammad Al-Ramahi, Cherie Noteboom
Mining User-Generated Content Of Mobile Patient Portal: Dimensions Of User Experience, Mohammad Al-Ramahi, Cherie Noteboom
Research & Publications
Patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. The incorporation of patient portals provides the promise to deliver excellent quality, at optimized costs, while improving the health of the population. This study extends the existing literature by extracting dimensions related to the Mobile Patient Portal Use. We use a topic modeling approach to systematically analyze users’ feedback from the actual use of a common mobile patient portal, Epic’s MyChart. Comparing results of Latent Dirichlet Allocation analysis with those of human analysis validated the extracted …
Ginkgo Biloba Extract Protects Against Methotrexate-Induced Hepatotoxicity: A Computational And Pharmacological Approach, Lina Tariq Al Kury, Fazli Dayyan, Fawad Ali Shah, Zulkifal Malik, Atif Ali Khan Khalil, Abdullah Alattar, Reem Alshaman, Amjad Ali, Zahid Khan
Ginkgo Biloba Extract Protects Against Methotrexate-Induced Hepatotoxicity: A Computational And Pharmacological Approach, Lina Tariq Al Kury, Fazli Dayyan, Fawad Ali Shah, Zulkifal Malik, Atif Ali Khan Khalil, Abdullah Alattar, Reem Alshaman, Amjad Ali, Zahid Khan
All Works
Ginkgo biloba extract possess several promising biological activities; currently, it is clinically employed in the management of several diseases. This research work aimed to extrapolate the antioxidant and anti-inflammatory effects of Ginkgo biloba (Gb) in methotrexate (MTX)-induced liver toxicity model. These effects were analyzed using different in vivo experimental approaches and by bioinformatics analysis. Male SD rats were grouped as follows: saline; MTX; Gb (pretreated for seven days with 60, 120, and 180 mg/kg daily dose before MTX treatment); silymarin (followed by MTX treatment); Gb 180 mg/kg daily only; and silymarin only. Histopathological results revealed that MTX induced marked hepatic …
Inhibition Of Cancer Causing Genes Through The Delivery Of Omomyc In Anti-Myc Therapy: A Systematic Review, Angie Mcgraw
Inhibition Of Cancer Causing Genes Through The Delivery Of Omomyc In Anti-Myc Therapy: A Systematic Review, Angie Mcgraw
University Honors Theses
A systematic review of the available studies on the interference of OmoMyc with Myc's function in cancerous cells is presented. Myc is a transcription factor that regulates cellular processes such as apoptosis, proliferation, and differentiation. However, Myc is often overexpressed in a variety of cancers, resulting in abnormal growth of cancer cells. Although the inhibition of Myc has been highly desired, it remained a challenge due to its undruggable characteristics. Attempts to inhibit Myc have involved the usage of small-molecules, but these attempts have failed, causing adverse effects and incomplete inhibition of Myc. Despite promising preclinical studies of OmoMyc, it …
Emerging Technologies In Healthcare: Analysis Of Unos Data Through Machine Learning, Reyhan Merekar
Emerging Technologies In Healthcare: Analysis Of Unos Data Through Machine Learning, Reyhan Merekar
Student Theses and Dissertations
The healthcare industry is primed for a massive transformation in the coming decades due to emerging technologies such as Artificial Intelligence (AI) and Machine Learning. With a practical application to the UNOS (United Network of Organ Sharing) database, this Thesis seeks to investigate how Machine Learning and analytic methods may be used to predict one-year heart transplantation outcomes. This study also sought to improve on predictive performances from prior studies by analyzing both Donor and Recipient data. Models built with algorithms such as Stacking and Tree Boosting gave the highest performance, with AUC’s of 0.6810 and 0.6804, respectively. In this …
Prediction Of Drug-Drug Interaction Potential Using Machine Learning Approaches, Joseph Scavetta
Prediction Of Drug-Drug Interaction Potential Using Machine Learning Approaches, Joseph Scavetta
Theses and Dissertations
Drug discovery is a long, expensive, and complex, yet crucial process for the benefit of society. Selecting potential drug candidates requires an understanding of how well a compound will perform at its task, and more importantly, how safe the compound will act in patients. A key safety insight is understanding a molecule's potential for drug-drug interactions. The metabolism of many drugs is mediated by members of the cytochrome P450 superfamily, notably, the CYP3A4 enzyme. Inhibition of these enzymes can alter the bioavailability of other drugs, potentially increasing their levels to toxic amounts. Four models were developed to predict CYP3A4 inhibition: …
Using Network Modeling To Understand The Relationship Between Sars-Cov-1 And Sars-Cov-2, Elizabeth Brooke Haywood, Nicole A. Bruce
Using Network Modeling To Understand The Relationship Between Sars-Cov-1 And Sars-Cov-2, Elizabeth Brooke Haywood, Nicole A. Bruce
Biology and Medicine Through Mathematics Conference
No abstract provided.
Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead
Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead
Engineering Faculty Articles and Research
Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …
A Capacitive Sensing Gym Mat For Exercise Classification & Tracking, Adam Goertz
A Capacitive Sensing Gym Mat For Exercise Classification & Tracking, Adam Goertz
Computer Science and Computer Engineering Undergraduate Honors Theses
Effective monitoring of adherence to at-home exercise programs as prescribed by physiotherapy protocols is essential to promoting effective rehabilitation and therapeutic interventions. Currently physical therapists and other health professionals have no reliable means of tracking patients' progress in or adherence to a prescribed regimen. This project aims to develop a low-cost, privacy-conserving means of monitoring at-home exercise activity using a gym mat equipped with an array of capacitive sensors. The ability of the mat to classify different types of exercises was evaluated using several machine learning models trained on an existing dataset of physiotherapy exercises.
The Mental Health Of Black Men: Stabilizing Trauma With Emotional Intelligence, Davis Brandford
The Mental Health Of Black Men: Stabilizing Trauma With Emotional Intelligence, Davis Brandford
School of Professional Studies
The purpose of this study is to explore the relationship between the impact of historical trauma and barriers on African-American males and the effects of emotional intelligence in reducing traumatic experiences. This research study is based on previous research and studies that explores the historical review of African- American oppression, trauma in black males, and mental health in the African American community. This study will utilize the historical trauma and emotional intelligence theories to explore barriers that African Americans have experienced over time and the role emotional intelligence can play to reduce trauma. It also explores the relevance of historical …
Crisis Communication And Executive Leadership: Ethical Shortcomings In Government, Daniel Davidoff
Crisis Communication And Executive Leadership: Ethical Shortcomings In Government, Daniel Davidoff
School of Professional Studies
This research thesis project is an analysis of how and why governments fail in their attempts at crisis communication. The hypotheses tested are: there exists a negative correlation between unethical leadership and successful crisis communication practices. And governments are more likely to experience these failures due to ethical disconnects in modern politics. Research includes a review of relevant academic literature regarding crisis communication theory, as well as the ethical framework that can be applied to that theory. Cases considered are Hurricane Katrina, the choking death of Eric Garner, and the COVID-19 global pandemic. The research project concludes with a recommendation …
Becoming A Trauma Informed Care Organization, Katie Robery
Becoming A Trauma Informed Care Organization, Katie Robery
School of Professional Studies
The Brockton Area Mutli Services Inc or BAMSI is a private, non-profit human services organization providing services to adults and children with developmental disabilities, mental illness, behavioral health, and public health needs. Founded in 1975, BAMSI is in the business of “bringing people and services together.” BAMSI transforms lives by building the capacity of individuals, families, organizations, and communities to learn, thrive, and achieve their goals. BAMSI services promote the recovery, resiliency, and self-determination of the people we serve. As a human services leader, we strive to be the provider of choice. (Duffy, 2019) With an emphasis on respect and …