PhD, University of Cincinnati, 2017, Arts and Sciences: Psychology

Annually, over 3 million reports of child maltreatment are made to state agencies in the United States (HHS, 2013). Between four and seven child deaths occur daily in the U.S. due to child abuse and neglect (USGAO, 2011; HHS, 2013). Adult survivors of child sexual abuse report poorer interpersonal relationship functioning, high-risk sexual behavior, and a tendency toward revictimization. (Polusny & Follette, 1995). Safety and risk assessments play a critical role in keeping children safe and preventing maltreatment recurrence (Fluke et al, 2001; DePanfilis & Scannapieco, 1994). A study by Dorsey et al (2008) revealed low correspondence between caseworkers' assessments and subsequent reports of maltreatment, indicating that considerable work is needed to improve accuracy and identification of children who are unsafe or at risk. There have been few studies on the predictive utility of safety assessments. Additional research is needed to examine how caseworkers utilize the existing knowledge about safety factors and correlates of safety to make safety decisions The first objective was to examine the association between safety factors and the safety decision. The second objective was to investigate the effect of adult protective capacity and child vulnerability on caseworker safety decisions. Finally, the predictive utility of caseworker safety decisions was evaluated by considering their relationships with risk, case disposition, and case decision. The study sample was drawn from Ohio's Statewide Automated Child Welfare Information System (SACWIS) database. Safety assessment items were 15 dichotomous indicators. Each safety assessment item was assigned to one of three categories: 1) safety factors (SF); 2) indicators of a lack of adult protective capacity (APC), and; 3) indicators of child vulnerability (CV). Odds ratios were computed between individual safety factors and between each safety factor and the safety response. Two logistic regression models predi (open full item for complete abstract)

Committee: Steven Howe Ph.D. (Committee Chair); Adam Carle Ph.D. (Committee Member); Stacie Furst-Holloway Ph.D. (Committee Member) Subjects: Psychology

Doctor of Philosophy, The Ohio State University, 2006, Industrial and Systems Engineering

The safety of the American workplace has improved dramatically over the past 30 years. This improvement is directly correlated with the adoption and enforcement of OSHA regulations (OSHA, “OSHA Facts”). However, despite the great strides that have been achieved, some industry sectors continue to produce unnecessarily high numbers of serious and preventable injuries. Machine-related injuries are responsible for nearly half of the thousands of amputation injuries that occur each year. Most machine injuries are preventable through known methods that are well documented. For most machines, OSHA provides guarding and operational requirements that are very general and broadly applicable. However, in the case of mechanical power presses the codes are quite specific and intended to address the specific hazards associated with such presses. This study proposes that the OSHA codes related to mechanical power presses are adequate and address most of the guarding concerns, but employers often fail to comply with the codes, apparently out of a lack of understanding of their implementation. It is hypothesized that an effective tool to help guide personnel through the evaluation of press safety hazards will improve the likelihood of an individual in accurately identifying press hazards. Based on the perceived need, a software tool was developed to assist in the hazard identification process. This tool was tested experimentally to determine its effectiveness. The hazard evaluation performance of a software-assisted group of novices was compared with the performances of a peer group and a group of press professionals, both comparison groups using traditional evaluation methods (specifically ANSI B11.TR3). Each of the experimental groups evaluated three different mechnical power presses. The hazards identified by each experimental group were to address the specific requirements of the applicable OSHA codes for guarding of mechanical power presses (29CFR1910.212 and 29CFR1910.217). Th (open full item for complete abstract)

Committee: Gary Maul (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2022, Civil Engineering

Throughout the last several years, the number of detrimental accidents is still considered high and not going below a certain verge. One of the main problems that may put people's safety in danger is the lack of real-time detection, assessment, and recognition of predictable safety risks. Current real-time risk identification solutions are limited to proximity sensing, which lacks in providing meaningful values of the overall safety conditions in real-time. The overall objective of this research is to envision, design, develop, assemble, and examine an automated intelligent real-time risk assessment (AIR) system. A holistic safety assessment approach is followed to include identification, prioritization, detection, evaluation, and control at risk exposure time. Multi-sensor technologies based on Radio-Frequency Identification (RFID) are integrated with a risk assessment intelligent system. The intelligent system is based on fuzzy fault tree analysis (FFTA), a deductive approach that comprehensively systemizes possible concurrent basic and conditional risk events, not risk symptoms, from major subgroups of triggering, enabling, and environment-related risks. System prototype is developed and examined for functionality and deployment requirements to prove the concept for on-foot building construction worker at site. The experimental examination results showed that the AIR system was able to detect, assess, and sound deliver combined evaluation of concurrent diverse risks presented in a worker's range at real-time of exposure. The AIR system performance has met the criteria of validity, significance, simplicity, representation, accuracy, and precision and timeliness. The reliability of the AIR system to deliver quantitative values of risk proximity was limited due to the RF signal attenuation caused by different materials at site. Nevertheless, AIR system was reliable in real-time assessment and declaration of risk types, values, and proximity in a subjective lingu (open full item for complete abstract)

Committee: TARUNJIT BUTALIA (Advisor); RONGJUN QIN (Committee Co-Chair); CHARLES TOTH (Committee Member); RACHEL KAJFEZ (Committee Member) Subjects: Civil Engineering; Cognitive Psychology; Communication; Health Education; Information Science; Linguistics; Mathematics; Occupational Safety

Doctor of Philosophy, The Ohio State University, 2019, Nuclear Engineering

Traditionally, Probabilistic Risk/Safety Assessment (PRA/PSA) uses Boolean logic event-tree (ET)/fault-tree (FT) formalism to quantify the risk associated with Nuclear Power Plant (NPP) operation. The PRA process aims at identifying accident scenarios, quantifying their likelihood and evaluating their consequences. For this purpose, it often makes use of conservative assumptions and relies on expert judgment. The need for more realistic analysis and reduction of reliance on expert judgment has led to the development of dynamic methodologies. These methods, such as the Dynamic Event Tree (DET), integrate probabilistic analysis with simulation of plant behavior. While many DET applications have appeared in the literature, the effect of system recoveries has not yet been fully addressed. This dissertation proposes a framework to systematically integrate system recoveries within DET as possible branching conditions. The framework also allows the possibility to model multiple failures and recoveries for a given system within the DET. The modeling of failures and recoveries is not quite straightforward for two reasons: (i) the thermal-hydraulic (TH) model has to explicitly account for all the phenomenological dependencies among systems, and, (ii) from probability perspective, failure and recovery distributions may be correlated. To address these two issues, the framework uses a modeling strategy that labels TH events, particularly those that may be similar phenomenologically and may occur under different system configurations and/or times as separate (but possibly correlated) events. Multidimensional distributions are used to account for such correlations among failure and recovery distributions. While this approach allows to model the NPP evolution in a more general and systematic way, it makes controlling the TH model more complicated since the number of coded statements increases. In order to verify the model correctness, the use of a graphical tool is propo (open full item for complete abstract)

Committee: Tunc Aldemir PhD (Advisor); Carol Smidts PhD (Committee Member); Marat Khafizov PhD (Committee Member); Valentin Rychkov PhD (Committee Member); Andrea Alfonsi PhD (Committee Member) Subjects: Nuclear Engineering

Master of Science, The Ohio State University, 2024, Mechanical Engineering

In the progression towards SAE Level 4 automation, the functional safety of automated driving systems is deemed essential, especially in the event of faults. The ISO 26262 functional safety standard is utilized to evaluate the risks associated with malfunctions in electrical/electronic (E/E) systems, based on a subjective assessment by safety experts. Yet, this standard primarily relies on qualitative measures and lacks provisions for real-time risk estimation. In this thesis, a risk estimation methodology has been developed to fill this gap, offering a quantitative method suitable for real-time risk analysis. A diagnostic system has been created to supplement the existing onboard diagnostic modules provided by the OEM. This integration creates a dual-layer safety net, ensuring secure operation in autonomous mode and providing a reliable fallback to the human operator when required. The quantitative risk estimation model that calculates the probability of collision, accounts for sensor and actuator faults amid measurement uncertainties. Based on the estimated probability, fault behavior is dynamically classified into distinct risk regions. The system is designed to respond appropriately to the situation by tailoring mitigating actions from minor adjustments to fallback protocols based on the level of risk and the type of fault. The proposed framework is illustrated through scenario-based testing via multiple simulations and closed-course evaluation using the test vehicle. This research has been conducted to contribute towards OSU's team, Buckeye AutoDrive, participating in Year 3 of the SAE AutoDrive Challenge II.

Committee: Giorgio Rizzoni (Advisor); Qadeer Ahmed (Committee Member) Subjects: Automotive Engineering; Electrical Engineering; Mechanical Engineering; Systems Design; Transportation

MS, University of Cincinnati, 2024, Engineering and Applied Science: Civil Engineering

Roadside incidents are a leading cause of driver fatalities in the United States, with a significant number involving collisions with barriers such as guardrails. Guardrails are the most common safety barriers installed along the roadside to maintain the vehicle's trajectory and shield it from roadside hazards. The functionality of the guardrail heavily relies on its structural integrity and condition. A damaged guardrail might not only fail to perform but also pose a danger when severely deformed. Conventional inspection methods for guardrails are labor-intensive, time-consuming, and prone to human error. These methods face time constraints and fail to provide continuous monitoring, which is critical for prompt maintenance and ensuring road safety. While recent advancements in computer vision have paved the way for automating the inspection and assessment of infrastructure assets, research specifically focused on guardrails has been limited, with existing automated solutions not fully addressing the challenges associated with their inspection. These challenges include the accurate identification and assessment of guardrail damages under varying lighting and weather conditions and the computational demands of real-time processing. This study addresses these challenges by introducing a novel framework utilizing advanced computer vision techniques, like YOLOv8 models and Deep OC-SORT tracker, integrated with camera and GPS systems mounted on a vehicle. This system automates the detection, localization, and severity assessment of guardrail damages. It enhances the accuracy and efficiency of inspections, allows for faster response time for maintenance, and ultimately contributes to safer road conditions. The success of this system serves as a model for similar unaddressed applications in other areas of infrastructure management, demonstrating the potential of artificial intelligence in public safety and asset management.

Committee: Munir Nazzal Ph.D. (Committee Chair); Nabil Nassif Ph.D. (Committee Member); Lei Wang Ph.D. (Committee Member) Subjects: Civil Engineering

PhD, University of Cincinnati, 2024, Pharmacy: Pharmaceutical Sciences

Commercial sunscreen products have been developed to protect human skin from ultraviolet radiation (UVR) with the use of chemical ingredients known as ultraviolet filters (UVFs). Sunscreens are important in protecting human health by preventing skin cancer. However, there have been several scientific publications investigating the potential impact of UVFs on environmental health in recent years. In response to the growing concern, the National Academies of Sciences, Engineering, and Medicine (NASEM) published a review consensus report on this topic and one knowledge gap identified is the need to further research environmental emissions of UVFs. This doctoral research set out to investigate two variables important to environmental emissions estimates of sunscreen UVFs: the amount applied to the skin (i.e., application thickness) and the amount released from the skin (i.e., release percentage). The hypothesis that was tested is the assumption of 100% sunscreen and UVF release from the skin is not representative of real-world conditions and leads to over-estimates of direct environmental emissions of UVFs from sunscreens. Three different studies were designed and executed to test this hypothesis. A large-scale web-based survey was developed and fielded to participants with the aim to measure sunscreen application aided by a visual reference and determine a more representative overall value for the United States (US) population. In the end, three online surveys and one home usage study were conducted. Results of this research support the inference that consumers are not applying the US Food and Drug Administration recommended application of 2.0 mg/cm2 and individual sunscreen application is highly variable. Next, two sunscreen rinse-off and release experiments were conducted. An in vivo rinse-off experiment using human volunteers aimed to determine a baseline UVF release percentage. The data from these experiments determined the average UVF release percentage wa (open full item for complete abstract)

Committee: Harshita Kumari Ph.D. (Committee Chair); Kavssery Ananthapadmanabhan ENG.SC.D. (Committee Member); Carys Mitchelmore Ph.D. (Committee Member); Kevin Li Ph.D. (Committee Member); James Coleman Ph.D. (Committee Member) Subjects: Pharmaceuticals

MS, University of Cincinnati, 2023, Engineering and Applied Science: Civil Engineering

The concepts of “smart mobility” and “vehicle automation” have recently emerged with the aim of eliminating traffic crashes caused by driver error such as distraction, recklessness, speeding, alcohol/drugs-impaired driving, and fatigue, by minimizing or completely removing the human from the driving task. However, the uncertainty about the safety benefits of fully automated vehicles (AVs), especially in the short term, as well as the long-expected transition period before they become widespread on roadways makes it crucial to investigate their potential impact on traffic performance. Therefore the main objective of this research was to use a microsimulation-based method to assess the potential delay and safety impacts of AVs in a mixed autonomy setting where both human-driven and fully automated vehicles operate in the same traffic stream, at a busy signalized intersection with permitted left turns and right turns on red. PTV VISSIM was used to develop the simulation environment comprising of 13 simulation cases, with 4 traffic composition scenarios and 5 different AV market penetration rates (0%, 25%, 50%, 75%, and 100%). The Surrogate Safety Assessment Model (SSAM) was employed to conduct a thorough safety analysis of the diverse scenarios, through the utilization of PET and TTC as surrogate safety measures. 3 TTC approaches were then considered by varying the TTC threshold values to determine the number potential traffic conflicts. The results indicate that at 25% AV MPR, the average intersection delay could be reduced by 25% and as AVs reach 100% MPR, this reduction could go up to 60%. In terms of traffic safety, the total number of conflicts generated decreased as AV MPR increased, with potential safety benefits ranging from 11% to 81% as the AV percentage increased from 25% to 100%. The advanced capabilities of AVs were clearly highlighted in the analysis of rear-end conflicts, where incidents involving human-driven vehicles colliding with autonomous vehic (open full item for complete abstract)

Committee: John Ash Ph.D. (Committee Chair); Zhixia Li Ph.D. (Committee Member); Mohamed Ahmed Ph.D. (Committee Member) Subjects: Civil Engineering

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

Autonomous vehicles (AVs) have the potential to revolutionize transportation safety. However, there is no consensus yet on how to effectively evaluate the safety of self-driving cars. This dissertation addresses the challenge of safety evaluation for AVs by integrating concepts from vehicle and traffic modeling, control theory, optimization, and both naturalistic and simulation-based data-driven methods. An alternative to the exhaustive testing of a system under all environmental and operational configurations are adaptive adversarial approaches, which primarily aim to expose the vehicle to safety-critical situations, also known as 'Falsification'. This dissertation evaluates the effectiveness of these algorithms, and creates a unified approach for generating adversarial testing algorithms and conducting safety analysis. We contribute to the model-based falsification task by ensuring theoretical guarantees under standard assumptions. This involves considering the environment as a gray-box, where its dynamics are partially known, and approximating the unknown model of the autonomous system. Preliminary works used deterministic and expert models, but this dissertation treats them as stochastic systems by incorporating a naturalistic behavior fitting. We make thee contributions to the safety analysis task. First, a systems' safety engineering approach is proposed for hazard analysis that considers the operational requirements from various safety standards. Second, a dynamic probabilistic assessment approach is presented for risk assessment, involving a Backtracking Process Algorithm (BPA), traditionally based on a discretized cell-to-cell probabilistic state transition mapping, for the probabilistic quantification of hazardous events. We propose using a sticky Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) for estimating system transition probabilities, aiming to reduce computational burden and allow meaningful state and transition identification (open full item for complete abstract)

Committee: Keith Redmill (Advisor); Saeedeh Ziaeefard (Committee Member); Mrinal Kumar (Committee Member); Ümit Özgüner (Committee Member) Subjects: Automotive Engineering; Computer Engineering; Electrical Engineering; Robotics

Psy. D., Antioch University, 2019, Antioch Seattle: Clinical Psychology

Although many evidence-based techniques are outlined in the literature, systems often assess, plan, and mitigate risk for Persons with Serious Mental Illness (PSMI) in significantly divergent ways. For more than 20 years now, the Washington State Department of Corrections has relied on the Offender Reentry Community Safety Program (ORCSP) to appraise dangerousness and presence of mental disorder, utilizing a staged process that considers a wide-ranging set of criminogenic and non-criminogenic variables. A growing body of research suggests that the ORCSP is effectively decreasing recidivism through collaborative reentry planning and mitigation between mental health and criminal justice professionals; however, whether ORCSP participant screening methods are valid or reliable remains untested. Without a cohesive assessment theory or comprehensive exploration of recidivism trends, increased scrutiny must be given to findings. In an effort to clarify these issues, this dissertation evaluates current and historical ORCSP assessment processes, overviews national standards and best-practices for PSMI risk management, and provides a set of practical recommendations to improve selection efficiency.

Committee: Jude Bergkamp PsyD (Committee Chair); Wendi Wachsmuth PhD (Committee Member); Angela Sauer MS (Committee Member); Carl Foreman PhD (Committee Member) Subjects: Criminology; Law; Psychology

Doctor of Psychology (PsyD), Wright State University, 2020, School of Professional Psychology

The availability of behavioral health services within primary care meets the high patient need for mental health care within a familiar setting, which is especially impactful in safety-net settings where patients face higher levels of stress and psychosocial barriers that impact health outcomes (Kamimura et al., 2014). Behavioral health consultants (BHCs) may encounter challenges to successful integration of services, but adapting to the unique clinic environment, assessing needs, and facilitating effect collaboration with providers can lead to greater success (Hunter, Goodie, Oordt, & Dobmeyer, 2017). A needs assessment was conducted with volunteer providers at the safety-net clinic Reach Out of Montgomery County using quantitative (survey) and qualitative (interview) methods. The primary goals of the study were to identify perceptions, needs, and barriers related to collaboration with integrated behavioral health services at the clinic. A review of relevant existing literature is presented to outline the role of BHCs, factors that impact the effective implementation of integrated care, perceptions of providers, and applicability to the safety-net medical setting. Statistical and content analyses were performed, and results were found to be consistent with existing literature. Providers reported a high level of openness and perceived patient benefit related to behavioral health services, but rated BHCs to be significantly more helpful for mental health concerns than medical needs. Recommendations for the clinic and the behavioral health team are then discussed based on these findings.

Committee: Jeffrey Cigrang Ph.D., ABPP (Committee Chair); Larry James Ph.D., ABPP (Committee Member); Sharon Sherlock RN, BSN, MSA, DHA (Committee Member) Subjects: Clinical Psychology; Health Care

Master of Science (MS), Ohio University, 2017, Electrical Engineering & Computer Science (Engineering and Technology)

With approval of UAS for civilian use in the National Airspace System, comes the need for formal integration. Manned and unmanned aircraft will share the same volumes of airspace, for which the safety standards must be upheld. Under manned aircraft operations, certain implicit assumptions exist that must be made explicit and translatable to the unmanned aircraft context. A formal system safety assessment approach through a fault tree analysis was used to identify assumptions contingent on a pilot's presence inside the fuselage and areas of weakness in operational equivalency of UAS. The UAS fault tree framework developed is applicable to unmanned aircraft systems of different sizes and complexity, while maintaining a semblance to the framework accepted within the manned aircraft community. In addition, a database of UAS incidents and accidents occurring internationally 2001-2016 was developed from published materials and databases of various sources. Database events were categorized according to the UAS Fault Tree Framework Level 1 Subsystems, the International Civil Aviation Organization (ICAO) Aviation Occurrence Categories, and the Human Factors Analysis and Classification System (HFACS). ICAO Aviation Occurrence Category specific fault trees were constructed for the three most commonly occurring categories in the database results. Significant sources of risk for UAS operations lie in Aircraft/System and Flight Crew/Human Factors failures. Commonly occurring Occurrence Categories in the results of the UAS database were different than those identified for fatal accidents occurring in manned commercial aviation operations. Increased system reliability and standardization is needed to ensure equivalent levels of safety for UAS operations in the NAS. Additionally, needs of UAS pilots are different than those for manned and model aircraft. Training requirements must be approached independently and formally evaluated for their effectiveness in risk mitigation.

Committee: Frank van Graas Ph.D. (Advisor); Maarten Uijt de Haag Ph.D. (Committee Member); Jeffrey Dill Ph.D. (Committee Member); Robert Stewart Ph.D. (Committee Member) Subjects: Engineering

Doctor of Philosophy, The Ohio State University, 2017, Food, Agricultural and Biological Engineering

The problem in agricultural safety and health education for youth is that there is a lack of evidence-based assessment tools to quantify comprehension of content prior to working in agriculture. Agricultural educators, both those in middle and high school classrooms and teacher educators at colleges and universities need educational resources, easy access to these resources, and assessment strategies for agricultural safety and health content. As role models, these educators play an important role in establishing a culture of safety and instilling safe work behaviors in their learners. The purpose of this research was to increase cognitive assessment strategies for agricultural safety and health content taught in agricultural classrooms. This objective was achieved by evaluating learning outcomes developed to satisfy federal regulation, describing the tasks that youth are completing as part of their Supervised Agricultural Experience (SAE), and developing two databases with 202 total exam items. Chapters two and three provided foundational information that was missing in the literature, and Chapter four of this dissertation built on the USDA-NIFA supported Safety in Agriculture for Youth (SAY) project where a primary objective was to develop a clearinghouse to provide access as a one stop shop for agricultural safety and health content. The U.S. Department of Labor (U.S. DOL) oversees the Agricultural Hazardous Occupations Orders (AgHOs), which identifies specific tasks that youth are prohibited from performing for hire on American farms and ranches. An educational exemption from this public policy is currently in place that allows youth, 14–15 years old, to complete a certification program prior to engaging in agricultural work involving tractors and machinery. However, limited guidance is provided in the legislation regarding the format or content of the tractor and machinery certification exemption. Four AgHOs (tractor and machinery) studies were identifi (open full item for complete abstract)

Committee: S. Dee Jepsen PhD (Advisor); Ann D. Christy PhD (Committee Member); John P. Fulton PhD (Committee Member); M. Susie Whittington PhD (Committee Member) Subjects: Agricultural Engineering

Doctor of Philosophy, The Ohio State University, 2016, Food Science and Technology

Unwanted microorganisms greatly affect the quality and safety of the final food products. For instance, besides foodborne pathogens, quality defects in Swiss cheese result in great economic loss annually to the industry. Ohio has the largest Swiss cheese industry in the U.S., and to reveal microbial causative agents in Swiss cheese with quality defects has become a critical need to solve the problem for the industry. While conventional approaches were insufficient to identify the risk factors promptly and accurately, recent advancements in molecular techniques enabled in-depth investigation of potential causative agents and the development of rapid detection method for safety and quality control. In Chapter 1, an extensive literature review was conducted in terms of cheese quality and safety issues. The Swiss cheese microbiota consist of starter cultures and environmental microorganisms. The major safety risk in cheese is the contamination of pathogenic bacteria from raw milk or post-pasteurization handling. Non-pathogenic bacteria usually cause quality issues in cheese. In Chapter 2, a rapid detection system for Propionibacterium in food matrices were successfully developed. One pair of genus-specific primers targeting the 16S ribosomal RNA of the genus Propionibacterium, and four pairs of species-specific primers targeting different protein coding genes of P. freudenreichii, P. acidipropionici, P. acnes, P. avidum, were designed and evaluated. This detection system showed no cross-reactivity with other dairy-related bacteria, indicating its utility in dairy industry. In Chapter 3, the starter cultures and non-starter microbiota from split Swiss cheese blocks made by two different factories were analyzed. Result showed the relative abundance of Enterobacteriaceae was significantly higher in split Swiss cheese than that of non-split samples, providing evidence that Enterobacteriaceae could be a microbial causing candidate for split defects. A comprehensive (open full item for complete abstract)

Committee: Hua Wang (Advisor); Curtis Knipe (Committee Member); Melvin Pascall (Committee Member); Zhongtang Yu (Committee Member) Subjects: Food Science

PhD, University of Cincinnati, 2013, Engineering and Applied Science: Industrial Engineering

Health and safety are gaining increasing interest across industries, but the business case for workplace health is not necessarily solid, as most operations models focus largely on compliance and risk mitigation. Managers realize that organizational health goes beyond health costs because employees are the primary drivers in achieving the business's desired performance. A more balanced approach should account for both contributions of a healthy workforce and the resources required to support it. This research introduces a financial model to quantify the effect of improvements on health and safety in the workplace, by (1) establishing a numerical relationship between improvements in the work environment and performance outcomes and (2) linking multiple operational outcomes into a common financial indicator. While different methods have been proposed to assess the implementation of health and safety programs, the challenge for any financial approach is the aggregation of the different performance outcomes linked to health and safety improvement. The Economic Assessment of the Work Environment (EAWE) builds upon prior work regarding health assessment in the workplace to forecast the financial benefits of health and safety implementations. The model can be applied by any organizational unit following a five-step process, beginning with a health assessment of the workplace to identify critical elements in the work environment. Based on the health assessment, an action plan is developed to address those gaps in the work environment. Next, performance targets are defined based on a set of internal goals and external benchmarks. The economic model transforms the expected improvements in the health and safety status into expected performance outcomes. The net cash-flow effect on the firm is the result of the combined costs and benefits associated with the implementation plan. The implementation plan should be applied in stages, starting from individual jobs and procee (open full item for complete abstract)

Committee: Henry Spitz Ph.D. (Committee Chair); Amit Bhattacharya Ph.D. (Committee Member); Ernest Hall Ph.D. (Committee Member); Richard Leroy Shell Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Industrial Engineering

Master of Science (MS), Ohio University, 2013, Civil Engineering (Engineering and Technology)

In this thesis, road safety assessment and prediction modeling for U.S. states fatal crashes are addressed. In the first part, a DEA-based Malmquist Index model was developed to assess the relative efficiency and productivity of U.S. states in decreasing the number of road fatalities. Even though the national trend in fatal crashes has reached to the lowest level since 1949 (Traffic Safety Annual Assessment Highlights, 2010), a state-by-state analysis and comparison has not been studied considering other characteristics of the holistic national road safety assessment problem in any work in the literature or organizational reports. The single output, fatal crashes, and five inputs were aggregated into single road safety score and utilized in the DEA-based Malmquist Index mathematical model. The period of 2002-2008 was considered due to data availability for the inputs and the output considered. According to the results, there is a slight negative productivity (an average of -0.2 percent productivity) observed in the U.S. on minimizing the number of fatal crashes along with an average of 2.1 percent efficiency decline and 1.8 percent technological improvement. The productivity in reducing the fatal crashes can only be attributed to the technological growth since there is a negative efficiency growth is occurred. It can be concluded that even though there is a declining trend observed in the fatality rates, the efficiency of states in utilizing societal and economical resources towards the goal of zero fatality is not still efficient. In the second part, a nonparametric prediction model, Artificial Neural Network, was developed to assist policy makers in minimizing fatal crashes across the United States. Seven input variables from four safety performance input domains while fatal crashes was utilized as the single output variable for the scope of the research. Artificial Neural Networks (ANN) was utilized and the best neural network model was developed out of 1000 n (open full item for complete abstract)

Committee: Deborah McAvoy Ph.D. (Advisor); Byung-Cheol Kim Ph.D. (Committee Member); Ken Walsh Ph.D. (Committee Member); M. Khurrum S. Bhutta Ph.D. (Committee Member) Subjects: Civil Engineering; Industrial Engineering; Transportation

Master of Science, The Ohio State University, 2008, Mechanical Engineering

Leg injuries are the most prevalent type of injury associated with vehicle-to-pedestrian collisions. Further, leg injuries can result in long term debilitation of the injured party. The potential for pedestrian leg injury with respect to the front-end design of an automotive vehicle is currently evaluated using a component impactor consisting only of a thigh, knee, and lower leg. The simplicity of this test allows for relatively inexpensive testing and repeatable results. However, the lack of upper body mass (UBM) is known to reduce the biofidelity of the legform's response.This study investigated the effects of the UBM on the measured impact response of the lower extremity. Three-dimensional Madymo simulations were performed using full body pedestrian and leg models, which consisted only of a single leg with the pelvis, torso, head, and other extremities removed. Initially, five output measures were recorded for these models during simulated impacts. These measures, which include femur shear and moment, tibia shear and moment, and proximal tibia acceleration, have been shown to correlate to injury and are therefore important quantities in assessment of injury risk. The full body impact simulations were used to generate a target response. The properties of the UBM, or UBM design, include the mass, moment of inertia, and center of gravity (CG) height. By determining the UBM design which produced a leg model response most similar to the full body response, the optimum UBM design would be obtained. An optimization was performed using Madymizer, one of the programs within the Madymo suite, which determined the optimum design to be a 10.93 kg mass, with a moment of inertia of 0.0698 kg-m2, attached 0.462 m above the knee. The optimum design would present many difficulties if fabricated and used in practice. As a result, a new design was sought which would still produce marked improvement in the legform's ability to assess vehicle aggressiveness while also being experimen (open full item for complete abstract)

Committee: Dennis A. Guenther PhD (Advisor); John F. Wiechel PhD (Committee Member) Subjects: Engineering; Mechanical Engineering

Master of Science (MS), Ohio University, 2007, Environmental Studies (Arts and Sciences)

This project was aimed at developing ways to expertly identify risk that may pose injury and harm to the researcher in a university and to provide and encourage safe-working practices. Many researchers especially students, may tend to neglect or are initially ignorant of the potential risks involved with their projects. The ultimate objective of this thesis is to develop an easy–to–use algorithm aimed at individuals without specialized safety training. The algorithm would help the researchers identify critical risk in a research facility in order to take appropriate actions to minimize the risk and maintain a safe working environment. The algorithm content was developed using various sources such as academic study materials, Occupational Safety and Health Act guidelines, manuals of various industries and national consensus standings. A team of non-experts attempted to identify risk for two Ohio Coal Research Center projects Zusing the algorithm. Subsequently, a group of expert instructors and safety specialist were asked to list all the potential risk for the same two OCRC projects without using the algorithm. Based on the correlation of the results obtained from the nonexperts and the experts a conclusion can be drawn that using the algorithm a naive researcher can identify most of the potential risks that otherwise an expert would determine.

Committee: Timothy Ryan (Advisor) Subjects: