Private higher education policy in Ohio, 1980--2006: A case study demonstrating the usefulness of Zumeta's public policy posture model

This study describes the case of private higher education in Ohio between 1980 and 2006 using Zumeta's (1996) model of state policy and private higher education. More specifically, this study used case study methodology and multiple sources to demonstrate the usefulness of Zumeta's model and illustrate its limitations. Ohio served as the subject state and data for 67 private, 4-year, degree-granting, Higher Learning Commission-accredited institutions were collected. Data sources for this study included the National Center for Education Statistics Integrated Postsecondary Data System as well as database information and documents from various state agencies in Ohio, including the Ohio Board of Regents. ^ The findings of this study indicated that the general state context for higher education in Ohio during the study time period was shaped by deteriorating economic factors, stagnating population growth coupled with a rapidly aging society, fluctuating state income and increasing expenditures in areas such as corrections, transportation and social services. However, private higher education experienced consistent enrollment growth, an increase in the number of institutions, widening involvement in state-wide planning for higher education, and greater fiscal support from the state in a variety of forms such as the Ohio Choice Grant. This study also demonstrated that private higher education in Ohio benefited because of its inclusion in state-wide planning and the state's decision to grant state aid directly to students. ^ Taken together, this study supported Zumeta's (1996) classification of Ohio as having a hybrid market-competitive/central-planning policy posture toward private higher education. Furthermore, this study demonstrated that Zumeta's model is a useful tool for both policy makers and researchers for understanding a state's relationship to its private higher education sector. However, this study also demonstrated that Zumeta's model is less useful when applied over an extended time period. Additionally, this study identifies a further limitation of Zumeta's model resulting from his failure to define "state mandate" and the "level of state mandates" that allows for inconsistent analysis of this component. ^

A Static Traffic Assignment Model Combined with an Artificial Neural Network Delay Model

As traffic congestion continues to worsen in large urban areas, solutions are urgently sought. However, transportation planning models, which estimate traffic volumes on transportation network links, are often unable to realistically consider travel time delays at intersections. Introducing signal controls in models often result in significant and unstable changes in network attributes, which, in turn, leads to instability of models. Ignoring the effect of delays at intersections makes the model output inaccurate and unable to predict travel time. To represent traffic conditions in a network more accurately, planning models should be capable of arriving at a network solution based on travel costs that are consistent with the intersection delays due to signal controls. This research attempts to achieve this goal by optimizing signal controls and estimating intersection delays accordingly, which are then used in traffic assignment. Simultaneous optimization of traffic routing and signal controls has not been accomplished in real-world applications of traffic assignment. To this end, a delay model dealing with five major types of intersections has been developed using artificial neural networks (ANNs). An ANN architecture consists of interconnecting artificial neurons. The architecture may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The ANN delay model has been trained using extensive simulations based on TRANSYT-7F signal optimizations. The delay estimates by the ANN delay model have percentage root-mean-squared errors (%RMSE) that are less than 25.6%, which is satisfactory for planning purposes. Larger prediction errors are typically associated with severely oversaturated conditions. A combined system has also been developed that includes the artificial neural network (ANN) delay estimating model and a user-equilibrium (UE) traffic assignment model. The combined system employs the Frank-Wolfe method to achieve a convergent solution. Because the ANN delay model provides no derivatives of the delay function, a Mesh Adaptive Direct Search (MADS) method is applied to assist in and expedite the iterative process of the Frank-Wolfe method. The performance of the combined system confirms that the convergence of the solution is achieved, although the global optimum may not be guaranteed.

Static and dynamic mechanical testing of a polymer with potential use as heart valve material

Synthetic tri-leaflet heart valves generally fail in the long-term use (more than 10 years). Tearing and calcification of the leaflets usually cause failure of these valves as a consequence of high tensile and bending stresses borne on the material. The primary purpose of this study was to explore the possibilities of a new polymer composite to be used as synthetic tri-leaflet heart valve material. This composite was comprised of polystyrene-polyisobutylene-polystyrene (Quatromer), a proprietary polymer, embedded with continuous polypropylene (PP) fibers. Quatromer had been found to be less likely to degrade in vivo than polyurethane. Moreover, it was postulated that a decrease in tears and perforations might result from fiber-reinforced leaflets reducing high stresses on the leaflets. The static and dynamic mechanical properties of the Quatromer/PP composite were compared with those of an implant-approved polyurethane (PU) for cardiovascular applications. Results show that the reinforcement of Quatromer with PP fibers improves both its static and dynamic properties as compared to the PU. Hence, this composite has the potential to be a more suitable material for synthetic tri-leaflet heart valves.

(Table 1) Comparisons of dynamic and static Young's moduli of basalt at DSDP Holes 69-504B, 70-504B and 83-504B

Compressional- and shear-wave velocity logs (Vp and Vs, respectively) that were run to a sub-basement depth of 1013 m (1287.5 m sub-bottom) in Hole 504B suggest the presence of Layer 2A and document the presence of layers 2B and 2C on the Costa Rica Rift. Layer 2A extends from the mudline to 225 m sub-basement and is characterized by compressional-wave velocities of 4.0 km/s or less. Layer 2B extends from 225 to 900 m and may be divided into two intervals: an upper level from 225 to 600 m in which Vp decreases slowly from 5.0 to 4.8 km/s and a lower level from 600 to about 900 m in which Vp increases slowly to 6.0 km/s. In Layer 2C, which was logged for about 100 m to a depth of 1 km, Vp and Vs appear to be constant at 6.0 and 3.2 km/s, respectively. This velocity structure is consistent with, but more detailed than the structure determined by the oblique seismic experiment in the same hole. Since laboratory measurements of the compressional- and shear-wave velocity of samples from Hole 504B at Pconfining = Pdifferential average 6.0 and 3.2 km/s respectively, and show only slight increases with depth, we conclude that the velocity structure of Layer 2 is controlled almost entirely by variations in porosity and that the crack porosity of Layer 2C approaches zero. A comparison between the compressional-wave velocities determined by logging and the formation porosities calculated from the results of the large-scale resistivity experiment using Archie's Law suggest that the velocity- porosity relation derived by Hyndman et al. (1984) for laboratory samples serves as an upper bound for Vp, and the noninteractive relation derived by Toksöz et al. (1976) for cracks with an aspect ratio a = 1/32 serves as a lower bound.

Control of posture and stability of the double-joint (Shoulder/Elbow) arm

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

When static meets dynamic: Comparing cone-beam computed tomography and acoustic reflection for upper airway analysis

INTRODUCTION: Upper airway measurement can be important for the diagnosis of breathing disorders. Acoustic reflection (AR) is an accepted tool for studying the airway. Our objective was to investigate the differences between cone-beam computed tomography (CBCT) and AR in calculating airway volumes and areas. METHODS: Subjects with prescribed CBCT images as part of their records were also asked to have AR performed. A total of 59 subjects (mean age, 15 ± 3.8 years) had their upper airway (5 areas) measured from CBCT images, acoustic rhinometry, and acoustic pharyngometry. Volumes and minimal cross-sectional areas were extracted and compared with software. RESULTS: Intraclass correlation on 20 randomly selected subjects, remeasured 2 weeks apart, showed high reliability (r >0.77). Means of total nasal volume were significantly different between the 2 methods (P = 0.035), but anterior nasal volume and minimal cross-sectional area showed no differences (P = 0.532 and P = 0.066, respectively). Pharyngeal volume showed significant differences (P = 0.01) with high correlation (r = 0.755), whereas pharyngeal minimal cross-sectional area showed no differences (P = 0.109). The pharyngeal volume difference may not be considered clinically significant, since it is 758 mm3 for measurements showing means of 11,000 ± 4000 mm3. CONCLUSIONS: CBCT is an accurate method for measuring anterior nasal volume, nasal minimal cross-sectional area, pharyngeal volume, and pharyngeal minimal cross-sectional area.

Control of posture and stability of the double-joint (Shoulder/Elbow) arm

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

An analysis of facial description in static images and video streams

[ES]This paper describes an analysis performed for facial description in static images and video streams. The still image context is first analyzed in order to decide the optimal classifier configuration for each problem: gender recognition, race classification, and glasses and moustache presence. These results are later applied to significant samples which are automatically extracted in real-time from video streams achieving promising results in the facial description of 70 individuals by means of gender, race and the presence of glasses and moustache.

Proprioception in ankle sprain from exercises and bandages

Ankle sprains are the most common injuries in sports, usually causing damage to the lateral ligaments. Recurrence has as usual result permanent instability, and thus loss of proprioception. This fact, together with residual symptoms, is what is known as chronic ankle instability, CAI, or FAI, if it is functional. This problem tries to be solved by improving musculoskeletal stability and proprioception by the application of bandages and performing exercises. The aim of this study has been to review articles (meta-analisis, systematic reviews and revisions) published in 2009-2015 in PubMed, Medline, ENFISPO and BUCea, using keywords such as “sprain instability”, “sprain proprioception”, “chronic ankle instability”. Evidence affirms that there does exist decreased proprioception in patients who suffer from CAI. Rehabilitation exercise regimen is indicated as a treatment because it generates a subjective improvement reported by the patient, and the application of bandages works like a sprain prevention method limiting the range of motion, reducing joint instability and increasing confidence during exercise. As podiatrists we should recommend proprioception exercises to all athletes in a preventive way, and those with CAI or FAI, as a rehabilitation programme, together with the application of bandages. However, further studies should be generated focusing on ways of improving proprioception, and on the exercise patterns that provide the maximum benefit.

Updating of Finite Element Models using static and dynamic optical strains with application to damage assessment

In the recent years, vibration-based structural damage identification has been subject of significant research in structural engineering. The basic idea of vibration-based methods is that damage induces mechanical properties changes that cause anomalies in the dynamic response of the structure, which measures allow to localize damage and its extension. Vibration measured data, such as frequencies and mode shapes, can be used in the Finite Element Model Updating in order to adjust structural parameters sensible at damage (e.g. Young’s Modulus). The novel aspect of this thesis is the introduction into the objective function of accurate measures of strains mode shapes, evaluated through FBG sensors. After a review of the relevant literature, the case of study, i.e. an irregular prestressed concrete beam destined for roofing of industrial structures, will be presented. The mathematical model was built through FE models, studying static and dynamic behaviour of the element. Another analytical model was developed, based on the ‘Ritz method’, in order to investigate the possible interaction between the RC beam and the steel supporting table used for testing. Experimental data, recorded through the contemporary use of different measurement techniques (optical fibers, accelerometers, LVDTs) were compared whit theoretical data, allowing to detect the best model, for which have been outlined the settings for the updating procedure.

IntelliChair: a non-intrusive sitting posture and sitting activity recognition system

Current Ambient Intelligence and Intelligent Environment research focuses on the interpretation of a subject’s behaviour at the activity level by logging the Activity of Daily Living (ADL) such as eating, cooking, etc. In general, the sensors employed (e.g. PIR sensors, contact sensors) provide low resolution information. Meanwhile, the expansion of ubiquitous computing allows researchers to gather additional information from different types of sensor which is possible to improve activity analysis. Based on the previous research about sitting posture detection, this research attempts to further analyses human sitting activity. The aim of this research is to use non-intrusive low cost pressure sensor embedded chair system to recognize a subject’s activity by using their detected postures. There are three steps for this research, the first step is to find a hardware solution for low cost sitting posture detection, second step is to find a suitable strategy of sitting posture detection and the last step is to correlate the time-ordered sitting posture sequences with sitting activity. The author initiated a prototype type of sensing system called IntelliChair for sitting posture detection. Two experiments are proceeded in order to determine the hardware architecture of IntelliChair system. The prototype looks at the sensor selection and integration of various sensor and indicates the best for a low cost, non-intrusive system. Subsequently, this research implements signal process theory to explore the frequency feature of sitting posture, for the purpose of determining a suitable sampling rate for IntelliChair system. For second and third step, ten subjects are recruited for the sitting posture data and sitting activity data collection. The former dataset is collected byasking subjects to perform certain pre-defined sitting postures on IntelliChair and it is used for posture recognition experiment. The latter dataset is collected by asking the subjects to perform their normal sitting activity routine on IntelliChair for four hours, and the dataset is used for activity modelling and recognition experiment. For the posture recognition experiment, two Support Vector Machine (SVM) based classifiers are trained (one for spine postures and the other one for leg postures), and their performance evaluated. Hidden Markov Model is utilized for sitting activity modelling and recognition in order to establish the selected sitting activities from sitting posture sequences.2. After experimenting with possible sensors, Force Sensing Resistor (FSR) is selected as the pressure sensing unit for IntelliChair. Eight FSRs are mounted on the seat and back of a chair to gather haptic (i.e., touch-based) posture information. Furthermore, the research explores the possibility of using alternative non-intrusive sensing technology (i.e. vision based Kinect Sensor from Microsoft) and find out the Kinect sensor is not reliable for sitting posture detection due to the joint drifting problem. A suitable sampling rate for IntelliChair is determined according to the experiment result which is 6 Hz. The posture classification performance shows that the SVM based classifier is robust to “familiar” subject data (accuracy is 99.8% with spine postures and 99.9% with leg postures). When dealing with “unfamiliar” subject data, the accuracy is 80.7% for spine posture classification and 42.3% for leg posture classification. The result of activity recognition achieves 41.27% accuracy among four selected activities (i.e. relax, play game, working with PC and watching video). The result of this thesis shows that different individual body characteristics and sitting habits influence both sitting posture and sitting activity recognition. In this case, it suggests that IntelliChair is suitable for individual usage but a training stage is required.

Comments on: Static and dynamic source locations in undirected networks

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