Restaurant managers complete certification in food safety in order to ensure that food is handled and prepared in a manner that decreases risk factors associated with food-borne illness. However, the literature has been inconclusive concerning the connection between manager certification and the incidence of critical food-safety violations. The purpose of this quantitative study was to examine the relationship between the presence or absence of a certified food safety manager (CFSM) and the number of risk factors cited on food inspection reports and the food safety score. In addition, this study was designed to determine whether operation type (i.e., chain vs. independently owned) has an impact on the number of risk factors and food safety score. This study was an analysis of 2013 data from 1,547 restaurants in North, Central, and South Georgia health districts using a 2-tailed independent-sample t test. Restaurants with a CFSM had significantly more risk factors cited on food safety inspections and lower food safety scores than restaurants without a CFSM. There was also a significant difference among chain and independent restaurants. Chain restaurants had fewer risk factors cited on restaurant inspections and had higher food safety scores. In the epidemiological triangle model, breaking the chain of transmission disrupts the link among agent, host, and environment. Thus, CFSMs have the responsibility to implement food safety training programs to break the chain of transmission by identifying and correcting unsafe food practices among food workers. This study has the potential to assist managers in understanding the importance of food safety and implementing food safety training programs that decrease risk factors associated with food-borne illness. Further research is needed to explore the effectiveness of manager certification in reducing critical violations.

The purpose of this study was to make a critical evaluation of the school plant of Union Grove High School in Upshur County, Texas, in regards to health and safety environment.

Vid projektering av ytterväggar ställs höga krav på funktion och utformning. Ytterväggar har en mängd olika konstruktionslösningar beroende på stomsystem, fasadmaterial och andra förutsättningar. Uppdragsgivaren WSP, vill underlätta kvalitetssäkringen och minska tidsåtgången vid projektering med hjälp av framtagna typdetaljer. Typdetaljerna är utformade med hänsyn till aspekterna fuktsäkerhet, robusthet och kostnad. Intervjuer med konstruktörer, litteraturstudier samt kontakt med produktleverantörer, ligger till grund för arbetet. Utifrån analys av insamlat material har konstruktionslösningar tagits fram utifrån en maximal livslängd. Arbetet har resulterat i två typdetaljer för tung stomme och tre för lätt stomme, med fasadmaterialen puts, tegel samt skivmaterial. Tillhörande teknisk beskrivning, U-värde och kostnadsbild har tagits fram för vardera typdetalj. / Moisture saftey; Robustness; Cost; Exterior wall; Light frame; Heavy frame; Construction details   Abstract på engelska: Function and design have always been critically important when designing exterior walls. Several designs can be possible but are largely dependent upon the framework system, facade material as well as various other considerations. The client, WSP, wish to guarantee quality whilst reducing planning time but also maintaining factors such as moisture safety, robustness and cost. The basis of the work consisted of interviews with designers, revision of literature and product supplier liaison. Based upon analysis of collected material, the designs have been developed to ensure a maximum life span. The result has produced both heavy and light frame designs by utilising plaster, brick and sheet materials for the facade construction. The relevant technical descriptions, U-values and overall cost estimates have been developed for each construction details.

<div>Hot surface ignition is a safety design concern for serval industries including mining, aviation, automotive, boilers, and maritime applications. Bleed air ducts, exhaust pipes, combustion liners, and machine tools that are operated at elevated temperatures may be a source of ignition that needs to be accounted for during design. An apparatus for the measurements of minimum hot surface ignition temperature (MHSIT) of 3 aviation fluids (Jet-A, Hydraulic Oil (MIL-PRF-5606) and Lubrication Oil (MIL-PRF-23699)) has been developed. This study expands a widely utilized database of values of MHSIT. The study will expand the current range of design parameters including air temperature, crossflow velocity, fluid temperature, global equivalence ratio, injection method, and the effects of pressure. The expanded data are utilized to continue the development of a physics-anchored data dependent system and machine learning model for the estimation of MHSIT.</div><div><br></div><div>The aviation industry, including Rolls Royce, currently use a database of MHSIT values resulting from experiments conducted in 1988 at the Air Force Research Laboratory (AFRL) within the Wright Patterson Air Force Base in Dayton, OH. Over the three decades since these experiments, the range of operating conditions have significantly broadened in most applications including high performance aircraft engines. For example, the cross-stream air velocities (V) have increased by a factor of two (from ~3.4 m/s to ~6.7 m/s). Expanding the known database to document MHSIT for a range of fuel temperatures (TF), air temperatures (TA), pressure (P) and air velocities (V) is of great interest to the aviation industry. MHSIT data for current aviation fluids such as Jet-A and MIL-PRF-23699 (lubrication oil) and their relation to the design parameters have recently been under investigation in a generic experimental apparatus. </div><div><br></div><div>The current work involves utilization of this generic experimental apparatus to further the understanding of MHSIT through the investigation of intermediate air velocities, global equivalence ratios, injection method, and the effects of pressure. This study investigates the effects of air velocity in a greater degree of granularity by utilizing 0.6 m/s increments. This is done to capture the uncertainty seen in MHSIT values above 3.0 m/s. Furthermore, this study also expands the understanding of the effects of injection method on the MHSIT value with the inclusion of spray injected lubrication oil (MIL-PRF-23699) and stream injected Jet-A. The effects of global equivalence ratio are examined for spray injected Jet-A by modulating the aviation fluid injection rate and the crossflow air velocity in tandem. </div><div><br></div><div>During previous experimental campaigns, it was found that MHSIT did not monotonically increase with crossflow air velocity as previously believed. This new finding inspired a set of experiments that found MHSIT in crossflow to have four proposed ignition regimes: conduction, convective cooling, turbulent mixing, and advection. The current study replicates the results from the initial set of experiments at new conditions and to determine the effects of surface temperature on the regimes. </div><div><br></div><div>The MHSIT of flammable liquids depends on several factors including leak type (spray or stream), liquid temperature, air temperature, velocity, and pressure. ASTM standardized methods for ignition are limited to stagnant and falling drops downward (autoignition) at atmospheric pressure (ASTM E659, ASTM D8211, and ASTM E1491) and at pressures from 218 to 203 kPa (ASTM G72). Past studies have shown that MHSIT decreases with increasing pressure, but the available databases lack results of extensive experimental investigation. Therefore, such data for pressures between 101 to 203 kPa are missing or inadequate. As such the generic experimental apparatus was modified to produce the 101 to 203 kPa air duct pressure levels representative of a typical turbofan engine. </div><div><br></div><div>Machine learning (ML) and deep learning (DL) have become widely available in recent years. Open-source software packages and languages have made it possible to implement complex ML based data analysis and modeling techniques on a wide range of applications. The application of these techniques can expedite existing models or reduce the amount of physical lab investigation time required. Three data sets were utilized to examine the effectiveness of multiple ML techniques to estimate experimental outcomes and to serve as a substitute for additional lab work. To achieve this complex multi-variant regressions and neural networks were utilized to create estimating models. The first data sets of interest consist of a pool fire experiment that measured the flame spread rate as a function of initial fuel temperature for 8 different fuels, including Jet-A, JP-5, JP-8, HEFA-50, and FT-PK. The second data set consists of hot surface ignition data for 9 fuels including 4 alternative piston engine fuels for which properties were not available. The third data set is the MHSIT data generated by the generic experimental apparatus during the investigations conducted to expand the understanding of minimum hot surface ignition temperatures. When properties were not available multiple imputation by chained equations (MICE) was utilized to estimate fluid properties. Training and testing data sets were split up to 70% and 30% of the respective data set being modeled. ML techniques were implemented to analyze the data and R-squared values as high as 92% were achieved. The limitation of machine learning models is also discussed along with the advantages of physics-based approaches. The current study has furthered the application of ML in combustion through use of the MHSIT database.</div>

Prognostiziert durch Konzepte der Industrie 4.0 und den Cyber-Physischen-Systemen, können autonome Systeme zukünftig dynamisch auf Datenquellen in ihrer Umgebung zugreifen. Während die gemeinsame Nutzung solcher Datenquellen ein enormes Performanzpotenzial bietet, stellt die benötigte Systemarchitektur vorherrschende Sicherheitsprozesse vor neue Herausforderungen. Die vorliegende Arbeit motiviert zunächst, dass diese nur zur Laufzeit des Systems adressiert werden könne, bevor sie daraus zwei zentrale Ziele ableitet und verfolgt. Zum einen wird ein Beschreibungsmodel für die Darstellung von Fehlercharakteristika gemeinsam genutzter Daten vorgestellt. Dieses generische Fehlermodell erlaubt es zum anderen eine Sicherheitsanalyse zu definieren, die eine spezifische, dynamische Systemkomposition zur Laufzeit mit Hinblick auf die zu erwartenden Unsicherheiten bewerten kann. Die als Region of Safety betitelte Analysestrategie erlaubt, in Kombination mit dem generischen Fehlermodell, die Sicherheit der auf gemeinsam genutzten Daten basierenden Kollisionsvermeidungsstrategie zweier Roboter noch zur Designzeit zu garantieren, obwohl die spezifischen Fehlercharakteristika der Daten erst zur Laufzeit bekannt werden.:List of Acronyms List of Theorems List of Definitions List of Figures List of Tables 1. Introduction – Safety in Future Smart Industries 1.1. The Example of Smart Warehouses 1.2. Functional Safety Standards 1.2.1. Overview of Functional Safety Standards 1.2.2. IEC 61508 1.3. Scope of this Thesis 1.3.1. Objectives 1.3.2. Contributions 1.3.3. Outline 1.4. Related Publications by the Author 1.5. Mathematical Notation 2. State of the Art 2.1. State of the Art in Run-Time Safety Assessment 2.1.1. Approaches at the Functional Level 2.1.2. Approaches at the Technical Level 2.1.3. Conclusions 2.2. State of the Art in Failure Modeling 2.2.1. The Definition of (Sensor) Failure Model 2.2.2. Interval-Based Failure Modeling 2.2.3. Distribution-Based Failure Modeling 2.2.4. Failure-Type-Based Failure Modeling 2.2.5. Conclusions 2.3. Conclusions from the State of the Art 3. Generic Failure Model 3.1. Defining the Generic Failure Model 3.1.1. Time- and Value-Correlated Random Distribution 3.1.2. A Failure Type’s Failure Amplitudes 3.1.3. A Failure Type’s State Function 3.1.4. Polynomial Representation of a Failure Type 3.1.5. Discussion on the Fulfillment of the Predefined Criteria 3.2. Converting a Generic Failure Model to an Interval 3.2.1. Converting a Time- and Value-Correlated Random Distribution 3.2.2. A Failure Type’s Interval 3.3. Processing Chain for Generating Generic Failure Models 3.3.1. Identifying Failure Types 3.3.2. Parameterizing Failure Types 3.3.3. Confidence Calculation 3.4. Exemplary Application to Artificial Failure Characteristics 3.4.1. Generating the Artificial Data Set – Manually Designing GFMs 3.4.2. Identifying Failure Types 3.4.3. Parameterizing Failure Types 3.4.4. Confidence Calculation 3.4.5. Comparison to State-of-the-Art Models 3.5. Summary 4. Region of Safety 4.1. Explicitly Modeling Uncertainties for Dynamically Composed Systems 4.2. Regions of Safety for Dynamically Composed Systems 4.2.1. Estimating Regions of Attraction in Presence of Uncertainty 4.2.2. Introducing the Concept of Region of Safety 4.2.3. Discussion on the Fulfillment of the Predefined Criteria 4.3. Evaluating the Concept of Region of Safety 4.3.1. Defining the Scenario and Considered Uncertainties 4.3.2. Designing a Control Lyapunov Function 4.3.3. Determining an Appropriate Value for λc 4.3.4. The Effect of Varying Sensor Failures on Regions of Safety 4.4. Summary 5. Evaluation and Integration 5.1. Multi-Robot Collision Avoidance 5.1.1. Assumptions 5.1.2. Design of the Circle and Navigation Scenarios 5.1.3. Kinematics 5.1.4. Control Policy 5.1.5. Intention Modeling by Model Uncertainty 5.1.6. Fusing Regions of Safety of Multiple Stability Points 5.2. Failure Modeling for Shared Data – A Marker Detection Failure Model 5.2.1. Data Acquisition 5.2.2. Failure Model Generation 5.2.3. Evaluating the Quality of the Failure Model 5.3. Safe Handling of Shared Data in a Collision Avoidance Strategy 5.3.1. Configuration for Region of Safety Estimation 5.3.2. Estimating Regions of Safety 5.3.3. Evaluation Using the Circle Scenario 5.3.4. Evaluation Using the Navigation Scenario 5.4. Summary 6. Conclusions and Future Work 6.1. Summary 6.2. Limitations and Future Work 6.2.1. Limitations and Future Work on the Generic Failure Model 6.2.2. Limitations and Future Work on Region of Safety 6.2.3. Future Work on Safety in Dynamically Composed Systems Appendices A. Defining Factors of Risk According to IEC 61508 B. Evaluation Results for the Identification Stage C. Overview of Failure Amplitudes of Marker Detection Results Bibliography / The concepts of Cyber-Physical-Systems and Industry 4.0 prognosticate autonomous systems to integrate sources of shared data dynamically at their run-time. While this promises substantial increases in their performance, the openness of the required system architecture poses new challenges to processes guaranteeing their safety. This thesis firstly motivates that these can be addressed only at their run-time, before it derives and pursues two corresponding goals. Firstly, a model for describing failure characteristics of shared data is presented. Secondly, this Generic Failure Model is built upon to define a run-time safety assessment methodology that enables analyzing dynamic system compositions integrating shared data with respect to the expected uncertainties at run-time. This analysis strategy, entitled Region of Safety, allows in combination with the generic failure model to guarantee the safety of robots sharing position data for collision avoidance already at design-time, although specific failure characteristics become available only at run-time.:List of Acronyms List of Theorems List of Definitions List of Figures List of Tables 1. Introduction – Safety in Future Smart Industries 1.1. The Example of Smart Warehouses 1.2. Functional Safety Standards 1.2.1. Overview of Functional Safety Standards 1.2.2. IEC 61508 1.3. Scope of this Thesis 1.3.1. Objectives 1.3.2. Contributions 1.3.3. Outline 1.4. Related Publications by the Author 1.5. Mathematical Notation 2. State of the Art 2.1. State of the Art in Run-Time Safety Assessment 2.1.1. Approaches at the Functional Level 2.1.2. Approaches at the Technical Level 2.1.3. Conclusions 2.2. State of the Art in Failure Modeling 2.2.1. The Definition of (Sensor) Failure Model 2.2.2. Interval-Based Failure Modeling 2.2.3. Distribution-Based Failure Modeling 2.2.4. Failure-Type-Based Failure Modeling 2.2.5. Conclusions 2.3. Conclusions from the State of the Art 3. Generic Failure Model 3.1. Defining the Generic Failure Model 3.1.1. Time- and Value-Correlated Random Distribution 3.1.2. A Failure Type’s Failure Amplitudes 3.1.3. A Failure Type’s State Function 3.1.4. Polynomial Representation of a Failure Type 3.1.5. Discussion on the Fulfillment of the Predefined Criteria 3.2. Converting a Generic Failure Model to an Interval 3.2.1. Converting a Time- and Value-Correlated Random Distribution 3.2.2. A Failure Type’s Interval 3.3. Processing Chain for Generating Generic Failure Models 3.3.1. Identifying Failure Types 3.3.2. Parameterizing Failure Types 3.3.3. Confidence Calculation 3.4. Exemplary Application to Artificial Failure Characteristics 3.4.1. Generating the Artificial Data Set – Manually Designing GFMs 3.4.2. Identifying Failure Types 3.4.3. Parameterizing Failure Types 3.4.4. Confidence Calculation 3.4.5. Comparison to State-of-the-Art Models 3.5. Summary 4. Region of Safety 4.1. Explicitly Modeling Uncertainties for Dynamically Composed Systems 4.2. Regions of Safety for Dynamically Composed Systems 4.2.1. Estimating Regions of Attraction in Presence of Uncertainty 4.2.2. Introducing the Concept of Region of Safety 4.2.3. Discussion on the Fulfillment of the Predefined Criteria 4.3. Evaluating the Concept of Region of Safety 4.3.1. Defining the Scenario and Considered Uncertainties 4.3.2. Designing a Control Lyapunov Function 4.3.3. Determining an Appropriate Value for λc 4.3.4. The Effect of Varying Sensor Failures on Regions of Safety 4.4. Summary 5. Evaluation and Integration 5.1. Multi-Robot Collision Avoidance 5.1.1. Assumptions 5.1.2. Design of the Circle and Navigation Scenarios 5.1.3. Kinematics 5.1.4. Control Policy 5.1.5. Intention Modeling by Model Uncertainty 5.1.6. Fusing Regions of Safety of Multiple Stability Points 5.2. Failure Modeling for Shared Data – A Marker Detection Failure Model 5.2.1. Data Acquisition 5.2.2. Failure Model Generation 5.2.3. Evaluating the Quality of the Failure Model 5.3. Safe Handling of Shared Data in a Collision Avoidance Strategy 5.3.1. Configuration for Region of Safety Estimation 5.3.2. Estimating Regions of Safety 5.3.3. Evaluation Using the Circle Scenario 5.3.4. Evaluation Using the Navigation Scenario 5.4. Summary 6. Conclusions and Future Work 6.1. Summary 6.2. Limitations and Future Work 6.2.1. Limitations and Future Work on the Generic Failure Model 6.2.2. Limitations and Future Work on Region of Safety 6.2.3. Future Work on Safety in Dynamically Composed Systems Appendices A. Defining Factors of Risk According to IEC 61508 B. Evaluation Results for the Identification Stage C. Overview of Failure Amplitudes of Marker Detection Results Bibliography