Miracle-dz/UniFAR-FTUs huggingface dataset:
Title of Thesis: EXPLORING THE RELATIONSHIP BETWEEN PROFIT- SQUEEZE AND OCCUPATIONAL SAFETY AND HEALTH

Title of Thesis: EXPLORING THE RELATIONSHIP BETWEEN PROFITSQUEEZE AND OCCUPATIONAL SAFETY AND HEALTH VIOLATIONS Natalie Marie Schell, Master of Arts, 2005 Thesis Directed By: Professor Sally Simpson Department of Criminology and Criminal Justice One challenge for corporate crime scholars has been to distinguish criminal firms from non-criminal firms. A common explanation for this distinction is that offending companies are more economically troubled than firms that offend less or not at all. This study examines the relationship between firm characteristics and crime by focusing on Occupational Safety and Health Administration (OSHA) violations. Specifically, this research applies a profit-squeeze explanation to account for OSHA violations using a cross-sectional analysis of 77 companies for the time period 1993-2000. Results indicate mixed support for the profit-squeeze argument at the firm level using a variety of financial performance measures. Analyses also reveal that union membership was related to OSHA violations. EXPLORING THE RELATIONSHIP BETWEEN PROFIT-SQUEEZE AND OCCUPATIONAL SAFETY AND HEALTH VIOLATIONS by Natalie Marie Schell Thesis submitted to the Faculty of the Graduate School of the University of Maryland in partial fulfillment of the requirements for the degree of Master of Arts 2005 Advisory Committee: Professor Sally Simpson Chair/Advisor Professor Shawn Bushway Professor Gary LaFree Table of
A numerical investigation of the effect of crosswinds on the slipstream of a model-scale freight train and associated effects

A numerical investigation is presented in which the effect of crosswinds on the slipstream of a fully-loaded model-scale freight train is studied. The work used delayed detached-eddy simulation in order to produce accurate time-averaged data and also allowed for the instantaneous flow to be analysed. Significant slipstream amplification is shown for both pressure and velocity transients on the leeward side of the train. The most significant slipstream amplification is observed at the 30° yaw angle case where high velocities remain almost constant at the furthest measurement position from train side. Instantaneous slipstream velocities on the leeward side of the train were inputted into a mathematical model which was used to predict the effect of wind gusts on a representative sample of the population. It was found that at 4 m from train side the person instability due to slipstream amplification for the 30° case is nearly double that from the 10° case. The results presented highlight the potential risk associated with slipstream amplification around freight trains, although due to the immaturity of the field, no amendments are made to the codes of practice.
Condition monitoring of railway track and driver using in-service vehicle

This paper summarizes the development of "probe-vehicle" system for advanced railway condition monitoring. We developed a portable condition monitoring system for track which is easily set on in-service vehicle. In this system, irregularities of rail are estimated from vertical and lateral acceleration of car body. A roll angle of car body, which is calculated using a rate gyroscope, is used to distinguish irregularity of line from irregularity of level. Rail corrugation can be detected from cabin noise with spectral peek calculation. GPS system and map matching algorithm localizes the fault on track. Experiments using in-service vehicle were carried out to evaluate the developed system. The results show that the condition of rail irregularity and rail corrugation can be estimated effectively. It is also shown that the system can be applicable for monitoring driver's operation condition.
Model Predictive Control of Permanent Magnet Synchronous Motor Based on State Transition Constraint Method

Permanent magnet synchronous motors are widely used and have sufficient development prospects in the drive systems of electric vehicles. Traditional model predictive control (MPC) methods are shown to achieve good control performance by tracking the d- and q-axis current as well as limiting the current amplitude. However, the dynamic response performance and current harmonics during the switching process are not considered in the traditional MPC. Therefore, this paper proposes an MPC that can effectively improve control performance, where the switch transfer sequence in the switch constraint module is considered in the improved model. The state transition error is obtained from the switch constraint module according to the current switch state and the transition probability, after which, the integration into the cost function in which the driving error, tracking error, and constraint error are considered. A reinforcement learning (RL) algorithm is used to obtain the weight coefficient of the transition error term in the constraint module for automatically determining the best switch state for the next control period using the cost function. Simulation tests show that the total harmonic distortion of the phase current based on the improved MPC is 978.4%, less than 2843.0% of the traditional MPC method under 20 Nm at 1000 rpm. The torque response time of the motor is reduced by 0.026 s, whereas the simulation results indicate that the 100 km acceleration performance of an electric vehicle is improved by 9.9%.
End-to-end on-line rescheduling from Gantt chart images using deep reinforcement learning

With the advent of the socio-technical manufacturing paradigm, the way in which rescheduling decisions are taken at the shop floor has radically changed in order to guarantee highly efficient production under increasingly dynamic conditions. To cope with uncertain production environments, a drastic increase in the type and degree of automation used at the shop floor for handling unforeseen events and unplanned disturbances is required. In this work, the on-line rescheduling task is modelled as a closed-loop control problem in which an artificial autonomous agent implements a control policy generated off-line using a schedule simulator to learn schedule repair policies directly from high-dimensional sensory inputs. The rescheduling control policy is stored in a deep neural network, which is used to select repair actions in order to achieve a small set of repaired goal states. The rescheduling agent is trained using Proximal Policy Optimisation based on a wide variety of simulated transitions between schedule states using colour-rich Gantt chart images and negligible prior knowledge as inputs. An industrial example is discussed to highlight that the proposed approach enables end-to-end deep learning of successful rescheduling policies to encode task-specific control knowledge that can be understood by human experts.
Toxoplasma gondii prevalence and risk factors in owned domestic cats from Nakhon Pathom Province, Thailand

Domestic cats are a potential source of Toxoplasma gondii infection for humans. This study was conducted to determine the seroprevalence and risk factors for T. gondii infection in domestic cats. Cat sera (n = 182) were tested for T. gondii IgG antibodies using the latex agglutination test (LAT) and the GRA7 of T. gondii (TgGRA7)-indirect enzyme-linked immunosorbent assay (iELISA). Univariable and multivariable logistic regression analyses were used to identify the factors associated with T. gondii infection. The overall prevalence rates were 18.1% (33/182) according to LAT, 19.2% (35/182) according to the iELISA and 17.0% (31/182) according to LAT and iELISA. Univariable analyses identified, outdoor access (P = 0.006), being a former stray cat (P = 0.001) and successful hunting behaviors (P = 0.04) as risk factors for T. gondii infection. Outdoor access (OR 2.63, 95% confidence interval (CI) 1.03–6.72) and is a former stray cat (OR 3.69, 95% CI 1.52–8.96) remained significant risk factors in multivariable analyses. This study indicated a relatively high seroprevalence of T. gondii among domestic cats. Cat owners can reduce the risk for T. gondii infection by not allowing their cats to roam free. Furthermore, education about the transmission of the parasite should be provided to prevent infection to the owners.
Dynamic Decoupling and Intelligent Optimal PID Controller Tuning of Multivariable Qua-drones

This paper deals with dynamic decoupling and its intelligent PID control method of multivariable qua-drone. Up to this time, many sophisticated intelligent algorithms and control methods for drone systems have been mentioned. However, almost many cases have been focusing on single loop control methods and general multivariable systems. Therefore, we cannot guarantee its stability and optimal response by PID control used in multivariable qua-drone. Herein, this paper suggests a novel control method for PID control for multivariable qua-drone. As first step, this paper decouples dynamic of multivariable qua-drone using diagonal method of system matrix and then applies intelligent method PSO and GA to single loop obtained by decoupling method to obtain optimal response.
Toxoplasma gondii and Neospora caninum prevalence and risk factors on goat farms in Kanchanaburi province, Thailand

Toxoplasma gondii and Neospora caninum are apicomplexan protozoan parasites that have been associated with reproductive problems in ruminants. Despite the high seroprevalence of T. gondii and N. caninum reported in goats worldwide, little information about the seroprevalence of these parasites in goats in Thailand. In this study, we investigated the seroprevalence of T. gondii and N. caninum in 389 goat samples from five districts of Kanchanaburi province, Thailand. An indirect enzyme-linked immunosorbent assay (iELISA) using recombinant TgGRA7 and NcSAG1 antigens was used for the detection of anti-T.gondii and anti-N.caninum antibodies, respectively. Risk factors for the occurrence of these protozoan parasites on goat farms were also assessed. Specific IgG against T. gondii and N. caninum was detected in 28.5% and 16.7% samples, respectively. Co-infections were observed in 11.8% of samples. The risk factors significantly associated with T. gondii seroprevalence was the presence of cats (odds ratio [OR]= 2.55, 95% confidential interval [CI]=1.67- 3.89). The presence of other domestic animals on farm contributed to the risk of T. gondii and N. caninum infection on goat farms (OR=1.692, 95% CI= 1.08- 2.63). The present study provides data on the seroprevalence and risk of T. gondii and N. caninum infections among goats in Kanchanaburi province, Thailand. Integrated and efficient measures are required to prevent and control infections on goat farms.
Quantification skills in the construction industry

Measurement/quantification is a core skill of the quantity surveyor and such skills need to be placed within a more appropriate educational framework to ensure their continuing relevance. This paper reports on a study that sought first, to identify the characteristics associated with measurement/quantification skills and second, to weight or rank their relative importance. A total of 37 characteristics associated with a person undertaking the measurement task were identified following a search of available literature and a series of taped interviews with practitioners. These were then classified and grouped together into a model structured according to the fundamental requirements of educational soundness, technical soundness and personal soundness. Twenty-one representative characteristics of this model were then rated for importance by 77 undergraduate quantity surveying students and 30 qualified surveyors working in private practice. The resulting analysis enabled the subsets of the characteristics of .
BER performance of multiwavelength optical cross-connected networks with deflection routing

The transmission performance of regular two-connected multihop transparent optical networks in uniform traffic under hot-potato, single-buffer deflection routing schemes is presented. Manhattan Street Network (MS) and ShuffleNet (SN) are compared in terms of bit error rate (BER) and packet error rate (PER) both analytically and by simulation. The authors implement a novel strategy of analysis, in which the transmission performance evaluation is linked to the traffic randomness of the networks. Amplifier spontaneous emission (ASE) noise, and device-induced crosstalk severely limit the characteristics of the network, such as propagation distance, sustainable traffic and bit rate. The results indicate that under the same load the BER performance of single-buffer deflection routing is worse than hot-potato. However, at BER = 10 -9 single-buffer has a higher throughput than hot-potato. It is shown that the feasibility of deflection routing in transparent networks with MS and SN topologies heavily depends on the power coupling coefficient of the routing space switch used in each node.
Measurement and Functional Invariance of Psychological Individuation Constructs Across Cultures: Initial Evidence From Taiwan and the United States

Abstract Using cross-cultural data (n Taiwan = 394 and n U.S. = 246), we tested psychological individuation constructs for measurement and functional invariance. Scalar and partial metric invariance held for psychological independence and dysfunctional individuation, respectively. Most associations with mental illness appeared functionally invariant while most associations with substance use were U.S. specific.
A tunable iodine stabilized He-Ne laser at wavelengths 543 nm, 605 nm and 612 nm

A tunable iodine stabilized He-Ne laser has been constructed. The laser radiation has only one polarization state which removes the polarization flipping problem. The single mode output power of the laser is higher than 250 /spl mu/M at 543 nm. The frequency of the laser has been stabilized on the hyperfine absorption components of iodine molecular transitions R(12) 26-0, R(106) 28-0 at 543 nm, P(62) 11-2 at 605 nm and R(47) 9-2, P(48) 11-3 at 612 nm.
A GPS integrity channel based fault detection and exclusion algorithm using maximum solution separation

The paper presents a new GPS integrity channel (GIC) based fault detection and exclusion (GFDE) algorithm. The algorithm employs the maximum solution separation technique augmented by data broadcast over the GIC to ensure GPS integrity. Furthermore, GFDE improves on current snapshot RAIM techniques by reducing the effect of the accuracy and availability trade-off, thereby increasing system availability. After a brief description of the algorithm. , the expected performance of GFDE is analyzed. Simulation results are presented which show availability that approaches the 0.99999 level required for sole means navigation for the enroute, terminal, and non-precision approach phases of flight over the conterminous United States ((CONUS). <<ETX>>
Niche overlap of benthic macrofauna in a tropical estuary: diurnal variation

The complexity of estuaries allows for the establishment of diverse communities composed of species with different survival strategies. The vertical migration of animals in the sediment is linked to competition, escape from predators and adaptations to diurnal physio-chemical changes related to variations in water levels. The present study aimed to evaluate niche overlap and amplitude, as well as the composition and structure, of communities of polychaetes and molluscs between sediment aliquots during the day and at night. Data sampling was performed in the Tubarão River estuary. The highest individual occurrence was registered during the diurnal period. Communities of polychaetes varied significantly between sediment aliquots during the day and at night, while molluscs did not show diurnal variation. Niche overlap results for polychaetes showed higher values between aliquots during the night, while molluscs showed greater overlap during the day. This indicates that polychaetes and molluscs have different mechanisms of coexistence. This may be related to different attributes of species allowing for the division of resources among individuals. Examination of niche overlap provides insights into coexistence of mechanisms within benthic macroinvertebrate communities.
Determination of the Risk Factors for Breast Cancer Survival using the Bayesian Method, Yazd, Iran

Background: There are numerous sophisticated studies which have investigated risk factors of breast cancer (BC). The purpose of this paper is to use benefits of Bayesian modeling to involve such prior information in determining factors affecting the survival of women with BC in Yazd city. Materials and Methods: The checklist included the characteristics of the patients and the factors studied. Then, from the records of patients referred to Radiotherapy Center of Shahid Ramezanzadeh, who had BC, from April 2005 to March 2012, the survival of 538 persons was recorded in the census. Data were analyzed by R software version 3.4. 2, and 0.05 was considered the significance level. Results: The mean age of BC diagnosis was 48.03 ± 11016 years. The Bayesian Cox regression showed that surgery (hazard ratio [HR] =1.631 95% PI; 1.102–2.422), ki67 (HR = 3.260. 95% PI; 1.6308–6.372), stage (HR = 5.620, 95% PI; 4.079–7.731), lymph node (HR = 1.765, 95% PI; 1.127–2.790), and ER (HR = 2. 600 95% PI; 2.023–3.354) were significantly related to survival time. Conclusion: The parametric and cox models were compared with standard error, and Cox model was selected as an optimal model. Accordingly, stage, ki67, lymph node, ER, and surgery variables had a positive effect on death hazard.
Enhance Wi-Fi Fingerprinting Indoor-Positioning by Error Flag Framework

This research aims to purpose the new method, which is called Error Flag Framework (EFF) to enhance accuracy fingerprinting indoor positioning of wireless device by using machine learning algorithms. EFF is compared with well-known machine learning classifiers; i.e. Decision Tree, Naive Bayes, and Artificial Neural Networks, by exploiting the signal strength from limited information. The performance comparison is done in terms of accuracy of classification of positions, precision of distance classified, and effects of classification of positions on results from quantity of learning data. The result of this study can suggest that EFF can increase performance for indoor positioning of every well-known classifier, especially when the quantity of learning data is large enough. Hence, EFF is the alternate way for implementing in positioning software by using the fingerprinting method.
HYDROGEN FUELLED IC ENGINE – AN OVERVIEW

Hydrogen as a fuel in Internal Combustion engines is a solution for the near future to realize zero CO 2 emissions for traffic applications. The hydrogen fuelled IC engine is ready for that. The storage and production of hydrogen, and to build the necessary infrastructure, are the real shortcomings in the general use of hydrogen in IC engines. This paper gives an overview of the development of hydrogen fuelled IC engines by the most important car manufactures (Ford, BMW etc.). This overview indicates the evolution in the development of hydrogen fuelled engines (different generation of engines). This evolution is also made at Ghent University. Ghent University has been working for nearly 15 years on the development and optimization of hydrogen engines. Several test rigs are in function (all with electronic control management systems and sequential multi-point injection).
Analysis of buried-channel waveguides and couplers: scalar solution and polarization correction

Buried-channel waveguides and couplers are analyzed by perturbations and variational methods under the scalar approximation. Propagation constants of the first- and higher-order modes are calculated. When far from cutoff, both methods give nearly identical results. Near cutoff, the variational method predicts the dispersion characteristics more accurately. The methods can be easily implemented on a PC to produce the dispersion characteristics as well as the field patterns. Correction to the scalar solutions that accounts for the polarization effects due to the refractive index discontinuity and the material birefringence is included in the analysis. >
RELIABILITY OF 3D FRONTAL PLANE KNEE AB/ADDUCTION RANGE OF MOTION DURING RUNNING IN YOUNG ATHLETES

This study quantified within-session and between-session reliability of 3D frontal plane knee ab/adduction range of motion during the stance phase of running gait calculated for 18 long term athlete development programme participants (10 males and 8 females, 11.5 ±1.4 years) during two testing sessions (spaced 10 weeks apart). Average mean differences in frontal plane knee ab/adduction between running trials (for the right or left side) within a session (week 1 or week 10) ranged from 0.2 to 7.2% (ES 0.01–0.26) which were acceptable differences. However, average mean differences between sessions for running trials (for the right or left side) ranged from 0.1 to 20% (ES 0.01–0.6). The mixed model resulted in estimates of knee ab/adduction range of motion for effects of limb side (3.6°), session (2.8°), run trial (0.2°) and subjects (4.5°). Within-session ICCs ranged from 0.80 to 0.92 and between-session ICCs ranged from 0.51 to 0.73. Based on these ICCs, within-session reliability of frontal plane knee ab/adduction is good and between-session reliability is average to good.
Systematic Digital Forensic Investigation Model

Law practitioners are in a uninterrupted battle with criminals in the application of digital/computer technologies, and require the development of a proper methodology to systematically search digital devices for significant evidence. Computer fraud and digital crimes are growing day by day and unfortunately less than two percent of the reported cases result in confidence. This paper explores the development of the digital forensics process model, compares digital forensic methodologies, and finally proposes a systematic model of the digital forensic procedure. This model attempts to address some of the shortcomings of previous methodologies, and provides the following advantages: a consistent, standardized and systematic framework for digital forensic investigation process; a framework which work systematically in team according the captured evidence; a mechanism for applying the framework to according the country digital forensic investigation technologies; a generalized methodology that judicial members can use to relate technology to non-technical observers. This paper present a brief overview of previous forensic models and propose a new model inspired from the DRFWS Digital Investigation Model, and finally compares it with other previous model to show relevant of this model. The proposed model in this paper explores the different processes involved in the investigation of cyber crime and cyber fraud in the form of an eleven-stage model. The Systematic digital forensic investigation model (SDFIM) has been developed with the aim of helping forensic practitioners and organizations for setting up appropriate policies and procedures in a systematic manner.