Attention Deficit Hyperactivity Disorder (ADHD) is one of the most commonly diagnosed of child clinical syndromes and is associated with poor academic achievement, poor peer and family relations, and an elevated risk for anxiety, depression, and conduct disorder (Barkley ,1990; Barkley, Guevremont, Anastopoulos, DuPaul, & Shelton, 1993; Barkley, Murphy, & Kwasnik, 1996; Beiderman, Faraone, & Lapey, 1992; Fischer, Fischer, et al., 1990; Hinshaw, 1994; Nadeau 1995; Weiss & Hechtman, 1993). Although ADHD is one of the most commonly diagnosed and widely researched disorders, the diagnostic criteria and defining characteristics of ADHD remain controversial (Wolraich, 1999). The current diagnostic formulation, as specified by the DSM-IV-TR, includes three distinct subtypes: Attention Deficit Hyperactivity Disorder- Predominantly Hyperactive Impulsive Type (ADHD-H), Attention Deficit Hyperactivity Predominantly Inattentive Type (ADHD-PI) and Attention Deficit Hyperactivity Disorder- Combined Typed (ADHD-C). Perhaps the most controversial aspect of the current nosology is the inclusion of the inattention symptoms within the family of ADHD diagnoses (Milich, Balentine, Lynam, 2001). Researchers and clinicians have further posited that ADHD-PI represents a distinct disorder with two subtypes: inattentive-disorganized and sluggish cognitive tempo. This study explored the relation between reading fluency, sluggish cognitive tempo symptoms, disorganized symptoms, processing speed and ADHD diagnosis. This study examined performance of children diagnosed 77 children diagnosed with ADHD (i.e. 53 participants met criteria for ADHD-PI and 24 participants met criteria for ADHD-C) on measures of cognitive functioning, processing speed, behavioral reports, sluggish cognitive tempo, disorganization and reading fluency. Significant group differences did not emerge on measures of processing speed, sluggish cognitive tempo symptoms or disorganized symptoms. Path analysis was employed to examine the simultaneous effects of processing speed on inattention symptoms, hyperactive/impulsive symptoms, SCT symptoms, DO symptoms, reading fluency, and internalizing symptoms. In addition, the paths from SCT symptoms, DO symptoms, inattention symptoms, and hyperactive/impulsive symptoms to reading fluency and internalizing symptoms were also be examined. Processing speed had a significant direct effect on SCT symptoms, Inattention Symptoms and Reading Fluency. In addition, SCT symptoms had a significant direct effect on anxiety symptoms. In summary, findings from the study provide important information about the link between processing speed, attention written, and reading fluency. Limitations of the study and implications for future research and practice are discussed. / text

Adolescents with Sluggish Cognitive Tempo (SCT) show symptoms of slowness, mental confusion, excessive daydreaming, low motivation, and drowsiness/sleepiness. Although many symptoms of SCT reflect internalizing states, no study has evaluated the utility of self-report of SCT in an ADHD sample. Further, it remains unclear whether SCT is best conceptualized as a unidimensional or multidimensional construct. In a sample of 262 adolescents comprehensively diagnosed with ADHD, the present study evaluated the dimensionality of a SCT scale and compared CFA and bifactor model fits for parent- and self-report versions. Analyses revealed the three-factor bifactor model to be the best fitting model. In addition, SCT factors predicted social and academic impairment and internalizing symptoms. Therefore, SCT as a multidimensional construct appears to have clinical utility in predicting impairment. Also, multiple reporters should be used, as they predicted different areas of functioning and were not invariant, suggesting that each rater adds unique information.

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This dissertation investigates the complex defect diffusion behaviors in concentrated solid solution alloys (CSAs) including high-entropy alloys (HEAs), which are critical for understanding their exceptional mechanical and radiation-resistant properties. Through a combination of multiple atomistic-level simulation techniques and novel machine learning methods, this work reveals how the intricacies of local atomic arrangements and chemical heterogeneities influence diffusion processes, thereby offering new insights into alloy design and optimization. The research initially focuses on the vacancy-mediated diffusion employing binary Ni-Fe concentrated alloys as model systems. To evaluate the impact of local chemical short-range orders (SROs) on vacancy diffusion, both random solid solution configurations and alloys with SROs are prepared using hybrid molecular dynamics (MD) and metropolis Monte Carlo (MMC) methods. The results demonstrates that the development of SROs can significantly impede vacancy-mediated diffusion and enhance the chemically biased diffusion between Fe and Ni sites. Such findings suggest that the diffusion behavior in CSAs can be intricately controlled by adjusting the chemical ordering, a principle that could revolutionize alloy design strategies. Moreover, the study establishes a linear correlation between changes in the enthalpy of mixing and the formation of SROs, indicating that the reduction of enthalpy of mixing towards the more negative direction within an alloy system acts as a driving force for the observed diffusional slowdown. Advancing the methodological frontier, this dissertation introduces a state-of-the-art approach that integrates machine learning (ML) with kinetic Monte Carlo (KMC) simulations to efficiently investigate the controversial phenomenon of "sluggish diffusion" in concentrated alloys. As the first step, the Ni-Fe concentrated alloys are used as model systems. The complexity of defect diffusion in varying local atomic environment in CSAs makes it impractical to apply the standard nudged elastic band (NEB) method for on-the-fly determination of defect migration barriers at each step. By developing an artificial neural network (ANN) model trained on a dataset of NEB-computed migration barriers, it enables precise, efficient, and on-the-fly predictions of vacancy migration barriers for arbitrary local atomic environments during KMC simulations, including both random solution configuration and alloys with SROs. The diffusivities derived from this ANN-KMC modeling closely align with those from independent MD and temperature-accelerated dynamics (TAD) simulations at their accessible temperatures. The research delves into the sluggish diffusion mechanisms over the entire composition range of the Ni-Fe alloy system, elucidating them through the lens of ANN-KMC-derived insights at both high and low temperatures. The exploration then extends to quinary FeNiCrCoCu HEAs, utilizing a similar but improved ANN model to predict vacancy migration barriers across a wide compositional range. Due to the challenges of exploring the vast HEA compositional space, to date most experimental and computational studies have been limited to equiatomic compositions. This model, remarkably effective despite being trained solely on equiatomic HEA data, accurately predicts vacancy migration barriers in non-equiatomic compositions and their binary to quinary subsystems. Implementing this ANN model as an on-the-fly barrier calculator for KMC simulations, such ANN-KMC framework derives diffusivities nearly identical to the those from independent MD simulations but with far higher efficiency. This capability facilitates an extensive study of over 1,500 HEA compositions, uncovering the presence of sluggish diffusion in many non-equiatomic compositions. The analysis provides critical insights into the interplay between compositions, complex potential energy landscape, and percolation effect of the faster diffuser (i.e., Cu) on sluggish diffusion behaviors, offering invaluable perspectives for experimental alloy design and development. Lastly, the dissertation delves into interstitial-mediated diffusion in FeNiCrCoCu HEAs, confirming the presence of sluggish interstitial diffusion by comparing the equiatomic HEA with a range of reference systems. To study the non-monotonic concentration dependences in interstitial diffusion, a machine learning KMC (ML-KMC) method has been developed to simulate 〈100〉 dumbbell interstitial diffusion across various HEA compositions. Diverging from conventional KMC (C-KMC) and random sample KMC (RS-KMC) approaches, which approximate transition energies through a mean-field and random sampling methods, respectively, the ML-KMC predicts dumbbell formation energy on-the-fly based on local atomic configurations. This enables it to effectively replicate diffusion patterns from independent MD simulations. This novel ML-KMC approach offers a promising high-throughput method for studying HEAs, avoiding the expensive computational overhead associated with calculating dumbbell migration barriers. The impact of the percolation effect of faster diffusing elements (Cr, Cu) is also analyzed. Insights from this study can advance the understanding of compositional-dependent diffusion and provide valuable insights for the HEA design. Beyond the achievement of these completed works, two promising future projects have been evaluated that could significantly advance the field of diffusion research. The first initiative seeks to broaden the scope of the ANN-KMC framework, aiming to significantly enhance simulation efficiency across a broad range of HEA compositions. An accurate ANN model for predicting interstitial migration barriers has already been developed, and its full integration into the KMC framework could enable more accurate diffusion simulations. The second project aims to develop a comprehensive ML interatomic potential tailored specifically for HEAs, intended to improve the predictive accuracy of MD simulations. Although progress has been made in modeling an equiatomic CoCrFeMnNi HEA, constructing a robust ML potential for HEAs faces substantial challenges, primarily due to the extensive data requirements and computational demands. / Doctor of Philosophy / This dissertation investigates the complex defect diffusion behaviors in concentrated solid solution alloys (CSAs) including high-entropy alloys (HEAs), which are critical to understanding their exceptional mechanical and radiation-resistant properties. Through a combination of multiple simulation techniques and novel machine learning methods, this work reveals how the intricacies of local atomic arrangements and chemical heterogeneities influence diffusion processes, thereby offering new insights into alloy design and optimization. The research initially focuses on the complex vacancy diffusion mechanism in concentrated Ni-Fe alloys, demonstrating that local chemical short-range orders (SROs) significantly impede vacancy-mediated diffusion. Such findings suggest that the diffusion behavior in CSAs can be intricately controlled by adjusting the chemical ordering, a principle that could revolutionize alloy design strategies. Moreover, the study revealed a linear correlation between changes in the enthalpy of mixing and the formation of SROs, indicating that the enthalpy of mixing may be important for the diffusional behavior in CSAs. Advancing the methodological frontier, this dissertation introduces a cutting-edge approach that integrates machine learning (ML) with kinetic Monte Carlo (KMC) simulations to efficiently investigate the controversial "sluggish diffusion" phenomenon using Ni-Fe concentrated alloys as the initial model systems. By developing an artificial neural network (ANN) model trained on pre-calculated migration barriers using the standard nudged elastic band (NEB) method, this approach enables precise, efficient, and on-the-fly predictions of vacancy migration barriers for arbitrary local atomic environments, including both random solution configuration and alloys with SROs. The diffusivities obtained from this ANN-KMC modeling closely align with independent molecular dynamics (MD) and temperature-accelerated dynamics (TAD) simulations at their accessible temperatures, but with a far better efficiency. The ANN-KMC approach is then extended to non-equiatomic FeNiCrCoCu HEAs. An improved ANN model is developed to predict vacancy migration barriers across a wide compositional range. This model, remarkably effective despite being trained solely on equiatomic HEA data, accurately predicts vacancy migration barriers in non-equiatomic compositions and their binary to quinary subsystems. This capability facilitates an extensive study of over 1,500 HEA compositions, uncovering the presence of sluggish diffusion in many non-equiatomic compositions. The analysis provides critical understanding of the diffusion behavior in a vast compositional space, offering invaluable insights for experimental alloy design and development. Lastly, the dissertation delves into interstitial-mediated diffusion in FeNiCrCoCu HEAs, confirming the presence of sluggish interstitial diffusion. A machine learning KMC (ML-KMC) method has been developed to simulate 〈100〉 dumbbell interstitial diffusion across various HEA compositions, closely replicating diffusion patterns as independent MD simulations. This novel ML-KMC approach offers a promising high-throughput method for studying HEAs, avoiding the expensive computational overhead associated with calculating migration barriers. The impact of the percolation effect of faster diffusing elements (Cr, Cu) is also analyzed. Regarding future research directions, two promising projects are evaluated. The first expands the ANN-KMC framework to render more accurate interstitial diffusion simulations, and the second focuses on developing a ML potential for an equiatomic CoCrFeMnNi HEA. The progresses and challenges are discussed.

Sluggish cognitive tempo (SCT) is a construct that includes symptoms of slowness, mental confusion, excessive daydreaming, low motivation, and drowsiness/sleepiness. SCT is often co-morbid with attention-deficit/hyperactivity disorder (ADHD), and SCT symptoms are associated with significant academic and interpersonal impairment above and beyond the influence of ADHD symptoms. Despite the overlap between ADHD and SCT and associated impairments, no studies have evaluated how evidence-based psychosocial interventions for adolescents with ADHD impact symptoms of SCT. This study examined whether SCT symptoms improved in a sample of 274 young adolescents with ADHD who received either an organizational skills or a homework completion intervention. SCT intervention response was evaluated broadly in all participants, and specifically, for participants in the clinical range for SCT symptom severity at baseline. Change in ADHD symptoms of inattention, executive functioning, and motivation was examined as potential predictors of improvement in SCT. Multilevel modeling analyses indicated that SCT symptoms decreased at the same rate for adolescents in both the organizational skills and homework completion interventions when compared to the waitlist group (d = .410). For adolescents with parent-reported clinical levels of SCT, the decrease in symptoms was more pronounced (d = .517), with the interventions decreasing the total score of SCT by 2.91 (one symptom). Additionally, in the high SCT group, behavior regulation executive functioning, metacognitive executive functioning, and inattention predicted change. Clinical implications and future directions are discussed, including development of interventions for adolescents with high levels of SCT.

<p>Årligen slutar 30 procent av eleverna gymnasieskolan utan betygsbehörighet. På individuella program (IV) är siffran 85 procent. För att kartlägga skolsituationen för en elevgrupp på IV-programmet intervjuades 24 elever. Utöver detta gjordes en kognitiv bedömning samt en självskattning av upplevd psykisk hälsa. Huvuddelen av eleverna presterade på en ojämn kognitiv nivå, inom normalvariationen. Arton elever bearbetade information långsamt. Eleverna skattade att de mådde psykiskt sämre än normgruppen. De rapporterade tidiga skolsvårigheter som i vissa fall uppmärksammats,men lämnats utan adekvat åtgärd. Elever med långvariga skolproblem,försvårande additiva faktorer kombinerat med ett begåvningsvärde i normalvariationens nedre spann samt långsamhet vid informationsbearbetning upplever negativa individuella konsekvenser. De negativa effekterna kan mildras med tidiga interventioner på en för eleverna adekvat nivå och beaktat elevernas relativa kognitiva styrkor.</p>

Årligen slutar 30 procent av eleverna gymnasieskolan utan betygsbehörighet. På individuella program (IV) är siffran 85 procent. För att kartlägga skolsituationen för en elevgrupp på IV-programmet intervjuades 24 elever. Utöver detta gjordes en kognitiv bedömning samt en självskattning av upplevd psykisk hälsa. Huvuddelen av eleverna presterade på en ojämn kognitiv nivå, inom normalvariationen. Arton elever bearbetade information långsamt. Eleverna skattade att de mådde psykiskt sämre än normgruppen. De rapporterade tidiga skolsvårigheter som i vissa fall uppmärksammats,men lämnats utan adekvat åtgärd. Elever med långvariga skolproblem,försvårande additiva faktorer kombinerat med ett begåvningsvärde i normalvariationens nedre spann samt långsamhet vid informationsbearbetning upplever negativa individuella konsekvenser. De negativa effekterna kan mildras med tidiga interventioner på en för eleverna adekvat nivå och beaktat elevernas relativa kognitiva styrkor.

Thesis (PhD (Viticulture and Oenology. Wine Biotechnology))--Stellenbosch University, 2008. / The occurrence of sluggish and stuck fermentations continues to be a serious problem in the global wine industry, leading to loss of product, low quality wines, cellar management problems and consequently to significant financial losses. Comprehensive research has shown that many different factors can act either in isolation, or more commonly synergistically, to negatively affect fermentative activity of wine yeast strains of the species Saccharomyces cerevisiae. The individual factors most commonly referred to in the literature are various nutrient and oxygen limitations. However, other factors have been shown to contribute to the problem. Because of the mostly synergistic nature of the impacts, no single factor can usually be identified as the primary cause of stuck fermentation. In this study, several strategies to evolutionarily engineer wine yeast strains that are expected to reduce the occurrence of stuck and sluggish fermentations are investigated. In particular, the investigations focus on improving the ability of wine yeast to better respond to two of the factors that commonly contribute to the occurrence of such fermentations, nitrogen limitation and the development of an unfavorable ratio of glucose and fructose during fermentation. The evolutionary engineering strategies relied on mass-mating or mutagenesis of successful commercial wine yeast strains to generate yeast populations of diverse genetic backgrounds. These culture populations were then exposed to enrichment procedures either in continuous or sequential batch cultivation conditions while applying specific evolutionary selection pressures. In one of the stragegies, yeast populations were subjected to continuous cultivation under hexose, and especially fructose, limitation. The data show that the strains selected after this procedure were usually able to out-compete the parental strains in these selective conditions. However, the improved phenotype was not detectable when strains were evaluated in laboratory scale wine fermentations. In contrast, the selection procedure in continuous cultivation in nitrogen limiting conditions proved to be highly efficient for the generation of yeast strains with higher total fermentative capacity in low nitrogen musts. Furthermore, yeast strains selected after mutagenesis and sequential batch cultivation in synthetic musts with a very low glucose on fructose ratio showed a fructose specific improvement in fermentative capacity. This phenotype, which corresponds to the desired outcome, was also present in laboratory scale wine fermentations, where the discrepancy between glucose and fructose utilization of the selected strains was significantly reduced when compared to the parents. Finally, a novel strategy for the rectification of stuck fermentations was adjusted to industrial conditions. The strategy is based on the use of a natural isolate of the yeast species Zygosaccharomyces bailii, which is known for its preference of fructose. This process was successfully established and implemented in the wine industry.