Landings are an integral part of New Zealand Harvest operations where extracted trees are processed into logs and loaded out onto trucks. Forest owners need to balance the cost and environmental considerations when designing and constructing landings, with the productivity and safety of the harvesting crew that will use the landing. The objective of this study is to gain a greater understanding of landing size and how they affect forest harvest operations. This study investigates the relationship between landing size and processing delays. A time study was carried out for ten harvest operations predominantly in the lower North Island. The time study recorded all delays on the processing task of measuring and cutting stems into logs. The delays were then categorised so that only processing delays that are influenced by the size of the landing remained. These processing delays were then expressed per m³ and used as the response variable in regression analysis to test their correlation against landing size and a range of other predictor variables. A very strong, linear relationship between processing delays per m3 and actual landing size was found. This indicates that harvest operations on smaller landings exhibited higher delays per m3 than those on larger landings. Loading of the deck was the most significant processing delay; this is a direct result of not having enough room for surge piles as delimbing was not able to be carried out during loader downtime. The significance of the relationships developed in this study can help forest owners realise the implications of building landings that are too small for the intended purpose. Not only will small landings affect productivity, but have the potential to financially affect the forest owner also.

This research is addressing the causes and effects of project delays in the coal mining industry in South Africa. A literature review was conducted and it was found that the causes of delays are extensively researched in the construction industry with only limited reference to delays in the mining industry which are mainly risk based. The effects of delays are predominantly listed as timing and cost effects.

The main objective of this dissertation is to develop the second generation of Distributed Digital Radio (DDR) technology. A DDR II modem provides an integrated voice/data service platform, higher data rates and better throughput performance as compared to a DDR I modem. In order to improve the physical layer performance of DDR modems an analytical framework is first developed to model the Bit Error Rate (BER) performance of Orthogonal Frequency Division Multiplexing over Frequency Modulation (OFDM/FM) systems. The use of OFDM provides a spectrally efficient method of transmitting data over LMR channels. However, the high Peak-to-Average (PAR) of OFDM signals results in either a low Signal-to-Noise Ratio (SNR) at FM receiver or a high non-linear distortion of baseband signal in the FM transmitter. This dissertation presents an analytical framework to highlight the impact of high PAR of OFDM signal on OFDM/FM systems. A novel technique for reduction of PAR of OFDM called Linear Scaling Technique (LST) is developed. The use of LST mitigates the signal distortion occurring in OFDM over FM systems. Another important factor which affects the throughput of LMR networks is the Push-to-Talk (PTT) delay. A PTT delay refers to the delay between the instant when a PTT switch on a conventional LMR radio is keyed/unkeyed and a response is observed at the radio output. It can be separated into a Receive-To-Transmit Switch Interval (RTSI) or a Transmit-To-Receive Switch Interval (TRSI). This dissertation presents the typical RTSI delay values, distributions and their impact on throughput performance of LMR networks. An analytical model is developed to highlight the asymmetric throughput problem and the unintentional denial of service (UDOS) occurring in heterogeneous LMR networks consisting of radios with different PTT delay profiles. This information will be useful in performance and capacity planning of LMR networks in future.

Home visiting services have become an important and common component of early intervention for children with developmental delays. Currently, this group of children is the most frequent target of home visiting programs throughout the nation, although research evaluating the effectiveness of these programs is less pervasive. For this reason, a comprehensive analysis of studies within the home visiting literature that specifically focused on children with developmental delays and their families has been conducted. This effort has been accomplished through the process of meta-analysis. In addition to the computation of standardized mean difference effect sizes, emphasis was also placed on identifying the salient sample, intervention, and outcome characteristics of this group of studies. Results are discussed in terms of ecological validity or how well the findings of the study generalize to the world of practice, policy, additional research, and training in the field of home visiting. Overall, much of what we are observing in the research is applicable to current practice, specifically in regard to the home visiting procedure. However, discrepancies between research and practice have been found in regard to the target of the intervention and the training of the home visitor.

We study the stability of power control algorithms applied to optical networks in the presence of both time-delays and uncertainties. The objective of power control algorithms acting on optical networks is to ensure each signal channel attains an optimal optical signal-to-noise ratio (OSNR) value such that transmission errors are minimized. The inputs to the optical network are the transmitter powers and the outputs of the optical network are the OSNR values. The primal control algorithms adjust the channel powers at the transmitters using the channel OSNR values as feedbacks to attain OSNR optimality. We also present the dual control algorithm located at the links which transmits a channel price as an additional feedback to the primal control algorithms. Together, these are called primal-dual control algorithms. We present robust OSNR models for optical networks with multiple time-delays. Specifically, we consider additive system uncertainties, input multiplicative uncertainties on the signal powers, and transmitter noise uncertainties, all within a norm-bounded uncertainty framework. We analyze and modify both central cost based algorithms and game-theoretic based algorithms, with an emphasis on the latter, to ensure the stability of the closed-loop system. We apply time-delay stability analyses to exploit the structures of the closed-loop systems for each type of control algorithm. These techniques include frequency analyses, Lyapunov-Razumikhin techniques, and Lyapunov-Krasovskii techniques. Due to nonlinearities in the closed-loop system models, and their time-scale separated dynamics, we apply singular perturbation theory modified to handle either Lyapunov-Razumikhin theory or Lyapunov-Krasovskii theory. Singular perturbation theory, modified for time-delays, allows us to decouple complicated closed-loop systems into two simpler subsystems, one on a "slow" time-scale, and the other on a "fast" time-scale. We develop stability conditions for primal algorithms applied to arbitrary networks with delays. We also develop stability conditions for primal-dual algorithms applied to single-links, single-sink networks, two channel networks, and multi-link networks with both time-delays and uncertainties. The main results are presented as either LMI conditions and algebraic criteria. Simulations verify the stability of the closed-loop systems in the presence of time-delays. In addition, the simulations show the stabilization of perturbed systems at the expense of transient convergence time.

Computational gene regulation models provide a means for scientists to draw biological inferences from large-scale gene expression data. The expression data used in the models usually are obtained in a time series in response to an initial perturbation. The common objective is to reverse engineer the internal structure and function of the genetic network from observing and analyzing its output in a time-based fashion. In many studies (Wang [39], Resendis-Antonio [31]), each gene is considered to have a regulatory effect on another gene. A network association is created based on the correlation of expression data. Highly correlated genes are thought to be co-regulated by similar (if not the same) mechanism. Gene co-regulation network models disregard the cascading effects of regulatory genes such as transcription factors, which could be missing in the expression data or are expressed at very low concentrations and thus undetectable by the instrument. As an alternative to the former methods, some authors (Wu et al. [40], Rangel et al. [28], Li et al. [20]) have proposed treating expression data solely as observation values of a state-space system and derive conceptual internal regulatory elements, i.e. the state-variables, from these measurements. This approach allows one to model unknown biological factors as hidden variables and therefore can potentially reveal more complex regulatory relations.<p>In a preliminary portion of this work, two state-space models developed by Rangel et al. and Wu et al. respectively were compared. The Rangel model provides a means for constructing a statistically reliable regulatory network. The model is demonstrated on highly replicated Tcell activation data [28]. On the other hand, Wu et al. develop a time-delay module that takes transcriptional delay dynamics into consideration. The model is demonstrated on non-replicated yeast cell-cycle data [40]. Both models presume time-invariant expression data. Our attempt to use the Wu model to infer small gene regulatory network in yeast was not successful. Thus we develop a new modeling tool incorporating a time-lag module and a novel method for constructing regulatory networks from non-replicated data. The latter involves an alternative scheme for determining network connectivity. Finally, we evaluate the networks generated from the original and extended models based on a priori biological knowledge.

We study the stability of power control algorithms applied to optical networks in the presence of both time-delays and uncertainties. The objective of power control algorithms acting on optical networks is to ensure each signal channel attains an optimal optical signal-to-noise ratio (OSNR) value such that transmission errors are minimized. The inputs to the optical network are the transmitter powers and the outputs of the optical network are the OSNR values. The primal control algorithms adjust the channel powers at the transmitters using the channel OSNR values as feedbacks to attain OSNR optimality. We also present the dual control algorithm located at the links which transmits a channel price as an additional feedback to the primal control algorithms. Together, these are called primal-dual control algorithms. We present robust OSNR models for optical networks with multiple time-delays. Specifically, we consider additive system uncertainties, input multiplicative uncertainties on the signal powers, and transmitter noise uncertainties, all within a norm-bounded uncertainty framework. We analyze and modify both central cost based algorithms and game-theoretic based algorithms, with an emphasis on the latter, to ensure the stability of the closed-loop system. We apply time-delay stability analyses to exploit the structures of the closed-loop systems for each type of control algorithm. These techniques include frequency analyses, Lyapunov-Razumikhin techniques, and Lyapunov-Krasovskii techniques. Due to nonlinearities in the closed-loop system models, and their time-scale separated dynamics, we apply singular perturbation theory modified to handle either Lyapunov-Razumikhin theory or Lyapunov-Krasovskii theory. Singular perturbation theory, modified for time-delays, allows us to decouple complicated closed-loop systems into two simpler subsystems, one on a "slow" time-scale, and the other on a "fast" time-scale. We develop stability conditions for primal algorithms applied to arbitrary networks with delays. We also develop stability conditions for primal-dual algorithms applied to single-links, single-sink networks, two channel networks, and multi-link networks with both time-delays and uncertainties. The main results are presented as either LMI conditions and algebraic criteria. Simulations verify the stability of the closed-loop systems in the presence of time-delays. In addition, the simulations show the stabilization of perturbed systems at the expense of transient convergence time.

This study described the effect of a non-augmented (Spoken Communication, SC) and two augmented language interventions (Augmented Communication-Input, AC-I or Augmented Communication-Output, AC-O) on the upper-body, gross and fine motor skills of toddlers at the onset and conclusion of the intervention. The data presented are from a longitudinal study by Romski, Sevcik, Adamson, Cheslock, Smith, Barker, & Bakeman (2010). Three standardized assessments and five observational measures examined the participants' motor skills used to activate the speech generating device (SGD), language abilities and outcomes. The AC-O intervention decreased physical prompting, increased error-free symbol activations, and increased developmentally appropriate gross and fine motor use. An augmented intervention that utilizes a SGD may facilitate both language and motor development through the combination of the communicative goals and increased motor learning opportunities when accessing the SGD device.

Computational gene regulation models provide a means for scientists to draw biological inferences from large-scale gene expression data. The expression data used in the models usually are obtained in a time series in response to an initial perturbation. The common objective is to reverse engineer the internal structure and function of the genetic network from observing and analyzing its output in a time-based fashion. In many studies (Wang [39], Resendis-Antonio [31]), each gene is considered to have a regulatory effect on another gene. A network association is created based on the correlation of expression data. Highly correlated genes are thought to be co-regulated by similar (if not the same) mechanism. Gene co-regulation network models disregard the cascading effects of regulatory genes such as transcription factors, which could be missing in the expression data or are expressed at very low concentrations and thus undetectable by the instrument. As an alternative to the former methods, some authors (Wu et al. [40], Rangel et al. [28], Li et al. [20]) have proposed treating expression data solely as observation values of a state-space system and derive conceptual internal regulatory elements, i.e. the state-variables, from these measurements. This approach allows one to model unknown biological factors as hidden variables and therefore can potentially reveal more complex regulatory relations.<p>In a preliminary portion of this work, two state-space models developed by Rangel et al. and Wu et al. respectively were compared. The Rangel model provides a means for constructing a statistically reliable regulatory network. The model is demonstrated on highly replicated Tcell activation data [28]. On the other hand, Wu et al. develop a time-delay module that takes transcriptional delay dynamics into consideration. The model is demonstrated on non-replicated yeast cell-cycle data [40]. Both models presume time-invariant expression data. Our attempt to use the Wu model to infer small gene regulatory network in yeast was not successful. Thus we develop a new modeling tool incorporating a time-lag module and a novel method for constructing regulatory networks from non-replicated data. The latter involves an alternative scheme for determining network connectivity. Finally, we evaluate the networks generated from the original and extended models based on a priori biological knowledge.

This study examined indigenous infants, children, and youth in rural central Mexico for developmental delays by using a mixed methods approach. A two-person team consisting of a researcher and a translator completed this study. They conducted observations of 665 minors and interviews with their caregivers. A self-designed rubric was the guide to evaluate children using standards previously researched, developed and tested during the researcher's employment as director of a bilingual school in the United States (USA). This tool was used to evaluate the children, ninety-five percent of which were of Mexican heritage. The tool was modified to meet the needs of indigenous participants within this study in Central Mexico. This study found that the identification of developmental delays or special needs using traditional assessments might fail to take into account sociocultural factors (Baez, 2002; Cattey, 1980; Kelly, Sacker, Schoon, and Nazroo, 2006; Ogbu, 1992b; Rogoff, 2003) present in indigenous rural Mexican farm or ranch communities (`campos'). Data suggested that Native Indian or indigenous (`campesino') parents or other caregivers struggle with self-esteem issues related to class status thus failing to advocate for themselves or their children. Data additionally implied that caregivers lack access to social services including those aimed at working with developmental delays, special needs, (Fletcher, 1999; Forlin, Cedillo, Romero-Contreras, Fletcher, and Rodriguez, in press) and children without disabilities. Cultural factors maintained the status quo: the upper class held power, the middle class struggled, and the `campesino' continued to lose vital assets necessary to meet basic human needs. The findings identified a disproportionate number of developmentally delayed children, mainly due to malnutrition or other environmental influences; resulting from class status and/or lack of access to information. `Campesino' participants from in eighteen rural Central Mexican facilities were studied. Participants represented over one hundred `campos.' This study utilized an emic ethnographic approach (researcher being an "insider" researcher-participant) employing both qualitative and quantitative methodology. Results indicated that factors related to social status affected rural Mexican children's ability to acquire the basic nutritional intake necessary to achieve academic success and/or social elevation in Mexico or elsewhere due to the caregivers limited ability to improve the quality of life for his/her `campesino' children.