Ageing and learning as conceptualized by senior adults in two cultures: Hong Kong and Australia

This paper is about a study aimed to understand what successful ageing and later life learning mean to older adults in two cultures: Hong Kong and Australia. Findings from the study were reported in this paper to shed light on: (1) the meaning of ageing and learning as conceptualized by elders in Hong Kong and Australia; (2) the reasons for participation in later life learning, as well as, barriers for non-participation; (3) their learning interests and instructional preferences, and finally (4) the correlation between learning and successful ageing, and between learning and other well-being variables, including health, happiness and satisfaction of elders in Hong Kong and Australia. Two large samples of elders from Hong Kong (n=519) and Queensland, Australia (n=421) participated in the study. Within group analysis of the data from the two locations indicated that there are more similarities, rather than differences, between elders in Hong Kong and Australia with respect to background characteristics, meanings of ageing and learning, reasons for participation, barriers for non-participation, learning interests and instructional preferences.

Very Low Birth Weight Infants as Young Adults : Focus on aspects of cognition, behavior and sleep

Major advances in the treatment of preterm infants have occurred during the last three decades. Survival rates have increased, and the first generations of preterm infants born at very low birth weight (VLBW; less than 1500 g) who profited from modern neonatal intensive care are now in young adulthood. The literature shows that VLBW children achieve on average lower scores on cognitive tests, even after exclusion of individuals with obvious neurosensory deficits. Evidence also exists for an increased risk in VLBW children for various neuropsychiatric disorders such as attention-deficit hyperactivity disorder (ADHD) and related behavioral symptoms. Up till now, studies extending into adulthood are sparse, and it remains to be seen whether these problems persist into adulthood. The aim of this thesis was to study ADHD-related symptoms and cognitive and executive functioning in young adults born at VLBW. In addition, we aimed to study sleep disturbances, known to adversely affect both cognition and attention. We hypothesized that preterm birth at VLBW interferes with early brain development in a way that alters the neuropsychological phenotype; this may manifest itself as ADHD symptoms and impaired cognitive abilities in young adulthood. In this cohort study from a geographically defined region, we studied 166 VLBW adults and 172 term-born controls born from 1978 through 1985. At ages 18 to 27 years, the study participants took part in a clinic study during which their physical and psychological health was assessed in detail. Three years later, 213 of these individuals participated in a follow-up. The current study is part of a larger research project (The Helsinki Study of Very Low Birth Weight Adults), and the measurements of interest for this particular study include the following: 1) The Adult Problem Questionnaire (APQ), a self-rating scale of ADHD-related symptoms in adults; 2) A computerized cognitive test battery designed for population studies (CogState®) which measures core cognitive abilities such as reaction time, working memory, and visual learning; 3) Sleep assessment by actigraphy, the Basic Nordic Sleep Questionnaire, and the Morningness-Eveningness Questionnaire. Actigraphs are wrist-worn accelerometers that separate sleep from wakefulness by registering body movements. Contrary to expectations, VLBW adults as a group reported no more ADHD-related behavioral symptoms than did controls. Further subdivision of the VLBW group into SGA (small for gestational age) and AGA (appropriate for gestational age) subgroups, however, revealed more symptoms on ADHD subscales pertaining to executive dysfunction and emotional instability among those born SGA. Thus, it seems that intrauterine growth retardation (for which SGA served as a proxy) is a more essential predictor for self-perceived ADHD symptoms in adulthood than is VLBW birth as such. In line with observations from other cohorts, the VLBW adults reported less risk-taking behavior in terms of substance use (alcohol, smoking, and recreational drugs), a finding reassuring for the VLBW individuals and their families. On the cognitive test, VLBW adults free from neurosensory deficits had longer reaction times than did term-born peers on all tasks included in the test battery, and lower accuracy on the learning task, with no discernible effect of SGA status over and above the effect of VLBW. Altogether, on a group level, even high-functioning VLBW adults show subtle deficits in psychomotor processing speed, visual working memory, and learning abilities. The sleep studies provided no evidence for differences in sleep quality or duration between the two groups. The VLBW adults were, however, at more than two-fold higher risk for sleep-disordered breathing (in terms of chronic snoring). Given the link between sleep-disordered breathing and health sequelae, these results suggest that VLBW individuals may benefit from an increased awareness among clinicians of this potential problem area. An unexpected finding from the sleep studies was the suggestion of an advanced sleep phase: The VLBW adults went to bed earlier according to the actigraphy registrations and also reported earlier wake-up times on the questionnaire. In further study of this issue in conjunction with the follow-up three years later, the VLBW group reported higher levels of morningness propensity, further corroborating the preliminary findings of an advanced sleep phase. Although the clinical implications are not entirely clear, the issue may be worth further study, since circadian rhythms are closely related to health and well-being. In sum, we believe that increased understanding of long-term outcomes after VLBW, and identification of areas and subgroups that are particularly vulnerable, will allow earlier recognition of potential problems and ultimately lead to improved prevention strategies.

An Estimator Algorithm For Learning Automata With Changing Number Of Actions

In many problems of decision making under uncertainty the system has to acquire knowledge of its environment and learn the optimal decision through its experience. Such problems may also involve the system having to arrive at the globally optimal decision, when at each instant only a subset of the entire set of possible alternatives is available. These problems can be successfully modelled and analysed by learning automata. In this paper an estimator learning algorithm, which maintains estimates of the reward characteristics of the random environment, is presented for an automaton with changing number of actions. A learning automaton using the new scheme is shown to be e-optimal. The simulation results demonstrate the fast convergence properties of the new algorithm. The results of this study can be extended to the design of other types of estimator algorithms with good convergence properties.

Peritoneal dialysis and neurological outcome in infants and small children

Although improved outcomes for children on peritoneal dialysis (PD) have been seen in recent years, the youngest patients continue to demonstrate inferior growth, more frequent infections, more neurological sequelae, and higher mortality compared to older children. Also, maintain-ing normal intravascular volume status, especially in anuric patients, has proven difficult. This study was designed to treat and monitor these youngest PD patients, which are relatively many due to the high prevalence of congenital nephrotic syndrome of the Finnish type (CNF, NPHS1) in Finland, with a strict protocol, to evaluate the results and to improve metabolic balance, growth, and development. A retrospective analysis of 23 children under two years of age at onset of PD, treated between 1995 and 2000, was performed to obtain a control population for our prospective PD study. Respectively, 21 patients less than two years of age at the beginning of PD were enrolled in prospective studies between 2001 and 2005. Medication for uremia and nutrition were care-fully adjusted during PD. Laboratory parameters and intravascular volume status were regu-larly analyzed. Growth was analyzed and compared with midparental height. In a prospective neurological study, the risk factors for development and the neurological development was determined. Brain images were surveyed. Hearing was tested. In a retrospective neurological study, the data of six NPHS1 patients with a congruent neurological syndrome was analyzed. All these patients had a serious dyskinetic cerebral palsy-like syndrome with muscular dysto-nia and athetosis (MDA). They also had a hearing defect. Metabolic control was mainly good in both PD patient groups. Hospitalization time shortened clearly. The peritonitis rate diminished. Hypertension was a common problem. Left ventricular hypertrophy decreased during the prospective study period. None of the patients in either PD group had pulmonary edema or dialysis-related seizures. Growth was good and catch-up growth was documented in most patients in both patient groups during PD. Mortality was low (5% in prospective and 9% in retrospective PD patients). In the prospective PD patient group 11 patients (52%) had some risk factor for their neuro-development originating from the predialysis period. The neurological problems, detected be-fore PD, did not worsen during PD and none of the patients developed new neurological com-plications during PD. Brain infarcts were detected in four (19%) and other ischemic lesions in three patients (14%). At the end of this study, 29% of the prospectively followed patients had a major impairment of their neurodevelopment and 43% only minor impairment. In the NPHS1+MDA patients, no clear explanation for the neurological syndrome was found. The brain MRI showed increased signal intensity in the globus pallidus area. Kernic-terus was contemplated to be causative in the hypoproteinemic newborns but it could not be proven. Mortality was as high as 67%. Our results for young PD patients were promising. Metabolic control was acceptable and growth was good. However, the children were significantly smaller when compared to their midparental height. Although many patients were found to have neurological impairment at the end of our follow-up period, PD was a safe treatment whereby the neurodevelopment did not worsen during PD.

Learning Challenges in Biogas Production for Sustainability : An activity theoretical study of a network from a swine industry chain

This study is about the challenges of learning in the creation and implementation of new sustainable technologies. The system of biogas production in the Programme of Sustainable Swine Production (3S Programme) conducted by the Sadia food processing company in Santa Catarina State, Brazil, is used as a case example for exploring the challenges, possibilities and obstacles of learning in the use of biogas production as a way to increase the environmental sustainability of swine production. The aim is to contribute to the discussion about the possibilities of developing systems of biogas production for sustainability (BPfS). In the study I develop hypotheses concerning the central challenges and possibilities for developing systems of BPfS in three phases. First, I construct a model of the network of activities involved in the BP for sustainability in the case study. Next, I construct a) an idealised model of the historically evolved concepts of BPfS through an analysis of the development of forms of BP and b) a hypothesis of the current central contradictions within and between the activity systems involved in BP for sustainability in the case study. This hypothesis is further developed through two actual empirical analyses: an analysis of the actors senses in taking part in the system, and an analysis of the disturbance processes in the implementation and operation of the BP system in the 3S Programme. The historical analysis shows that BP for sustainability in the 3S Programme emerged as a feasible solution for the contradiction between environmental protection and concentration, intensification and specialisation in swine production. This contradiction created a threat to the supply of swine to the food processing company. In the food production activity, the contradiction was expressed as a contradiction between the desire of the company to become a sustainable company and the situation in the outsourced farms. For the swine producers the contradiction was expressed between the contradictory rules in which the market exerted pressure which pushed for continual increases in scale, specialisation and concentration to keep the production economically viable, while the environmental rules imposed a limit to this expansion. Although the observed disturbances in the biogas system seemed to be merely technical and localised within the farms, the analysis proposed that these disturbances were formed in and between the activity systems involved in the network of BPfS during the implementation. The disturbances observed could be explained by four contradictions: a) contradictions between the new, more expanded activity of sustainable swine production and the old activity, b) a contradiction between the concept of BP for carbon credits and BP for local use in the BPfS that was implemented, c) contradictions between the new UNFCCC1 methodology for applying for carbon credits and the small size of the farms, and d) between the technologies of biogas use and burning available in the market and the small size of the farms. The main finding of this study relates to the zone of proximal development (ZPD) of the BPfS in Sadia food production chain. The model is first developed as a general model of concepts of BPfS and further developed here to the specific case of the BPfS in the 3S Programme. The model is composed of two developmental dimensions: societal and functional integration. The dimension of societal integration refers to the level of integration with other activities outside the farm. At one extreme, biogas production is self-sufficient and highly independent and the products of BP are consumed within the farm, while at the other extreme BP is highly integrated in markets and networks of collaboration, and BP products are exchanged within the markets. The dimension of functional integration refers to the level of integration between products and production processes so that economies of scope can be achieved by combining several functions using the same utility. At one extreme, BP is specialised in only one product, which allows achieving economies of scale, while at the other extreme there is an integrated production in which several biogas products are produced in order to maximise the outcomes from the BP system. The analysis suggests that BP is moving towards a societal integration, towards the market and towards a functional integration in which several biogas products are combined. The model is a hypothesis to be further tested through interventions by collectively constructing the new proposed concept of BPfS. Another important contribution of this study refers to the concept of the learning challenge. Three central learning challenges for developing a sustainable system of BP in the 3S Programme were identified: 1) the development of cheaper and more practical technologies of burning and measuring the gas, as well as the reduction of costs of the process of certification, 2) the development of new ways of using biogas within farms, and 3) the creation of new local markets and networks for selling BP products. One general learning challenge is to find more varied and synergic ways of using BP products than solely for the production of carbon credits. Both the model of the ZPD of BPfS and the identified learning challenges could be used as learning tools to facilitate the development of biogas production systems. The proposed model of the ZPD could be used to analyse different types of agricultural activities that face a similar contradiction. The findings could be used in interventions to help actors to find their own expansive actions and developmental projects for change. Rather than proposing a standardised best concept of BPfS, the idea of these learning tools is to facilitate the analysis of local situations and to help actors to make their activities more sustainable.

Activation barriers for d.c. conductivity in ionic glasses: Classical calculations using the small-cluster approximation

A small-cluster approximation has been used to calculate the activation barriers for the d.c. conductivity in ionic glasses. The main emphasis of this approach is on the importance of the hitherto ignored polarization energy contribution to the total activation energy. For the first time it has been demonstrated that the d.c. conductivity activation energy can be calculated by considering ionic migration to a neighbouring vacancy in a smali cluster of ions consisting of face-sharing anion polyhedra. The activation energies from the model calculations have been compared with the experimental values in the case of highly modified lithium thioborate glasses.

Bayesian Learning for Joint Sparse OFDM Channel Estimation and Data Detection

The impulse response of a typical wireless multipath channel can be modeled as a tapped delay line filter whose non-zero components are sparse relative to the channel delay spread. In this paper, a novel method of estimating such sparse multipath fading channels for OFDM systems is explored. In particular, Sparse Bayesian Learning (SBL) techniques are applied to jointly estimate the sparse channel and its second order statistics, and a new Bayesian Cramer-Rao bound is derived for the SBL algorithm. Further, in the context of OFDM channel estimation, an enhancement to the SBL algorithm is proposed, which uses an Expectation Maximization (EM) framework to jointly estimate the sparse channel, unknown data symbols and the second order statistics of the channel. The EM-SBL algorithm is able to recover the support as well as the channel taps more efficiently, and/or using fewer pilot symbols, than the SBL algorithm. To further improve the performance of the EM-SBL, a threshold-based pruning of the estimated second order statistics that are input to the algorithm is proposed, and its mean square error and symbol error rate performance is illustrated through Monte-Carlo simulations. Thus, the algorithms proposed in this paper are capable of obtaining efficient sparse channel estimates even in the presence of a small number of pilots.

Machine learning modelling for predicting soil liquefaction susceptibility

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.

Variable Sparsity Kernel Learning

This paper(1) presents novel algorithms and applications for a particular class of mixed-norm regularization based Multiple Kernel Learning (MKL) formulations. The formulations assume that the given kernels are grouped and employ l(1) norm regularization for promoting sparsity within RKHS norms of each group and l(s), s >= 2 norm regularization for promoting non-sparse combinations across groups. Various sparsity levels in combining the kernels can be achieved by varying the grouping of kernels-hence we name the formulations as Variable Sparsity Kernel Learning (VSKL) formulations. While previous attempts have a non-convex formulation, here we present a convex formulation which admits efficient Mirror-Descent (MD) based solving techniques. The proposed MD based algorithm optimizes over product of simplices and has a computational complexity of O (m(2)n(tot) log n(max)/epsilon(2)) where m is no. training data points, n(max), n(tot) are the maximum no. kernels in any group, total no. kernels respectively and epsilon is the error in approximating the objective. A detailed proof of convergence of the algorithm is also presented. Experimental results show that the VSKL formulations are well-suited for multi-modal learning tasks like object categorization. Results also show that the MD based algorithm outperforms state-of-the-art MKL solvers in terms of computational efficiency.

Learning automata in feedforward connectionist systems

This paper analyses the behaviour of a general class of learning automata algorithms for feedforward connectionist systems in an associative reinforcement learning environment. The type of connectionist system considered is also fairly general. The associative reinforcement learning task is first posed as a constrained maximization problem. The algorithm is approximated hy an ordinary differential equation using weak convergence techniques. The equilibrium points of the ordinary differential equation are then compared with the solutions to the constrained maximization problem to show that the algorithm does behave as desired.

Micellar structures of dimeric surfactants with phosphate head groups and wettable spacers: A small-angle neutron scattering study

Dimeric or gemini surfactants consist of two hydrophobic chains and two hydrophilic head groups covalently connected by a hydrophobic or hydrophilic spacer. This paper reports the small-angle neutron scattering (SANS) measurements from aqueous micellar solutions of two different recently developed types of dimeric surfactants: (i) bis-anionic C16H33PO4--(CH2)(m)-PO4-C16H33,2Na(+) dimeric surfactants composed of phosphate head groups and a hydrophobic polymethylene spacer, referred to as 16-m-16,2Na(+), for spacer lengths m = 2, 4, 6, and 10, (ii) bis-cationic C16H33N+(CH3)(2)-CH2-(CH2-O-CH2)(p)-CH2-N+ (CH3)(2)C16H33,2Br(-) dimeric surfactants composed of dimethylammonium head groups and a wettable polyethylene oxide spacer, referred to as 16-CH2-p-CH2-16,2Br(-), for spacer lengths p = 1 - 3. The micellar structures of these surfactants are compared with the earlier studied bis-cationic C16H33N+ (CH3)(2)-(CH2)(m)-N+ (CH3)(2)C16H33,2Br(-) dimeric surfactants composed of dimethylammonium head groups and a hydrophobic polymethylene spacer, referred to as 16-m-16,2Br(-). It is found that 16-m-16,2Na(+), similar to 16-m-16,2Br(-), form various micellar structures depending on the spacer length. Micelles an disklike for rn = 2, rodlike for m = 4, and prolate ellipsoidal fur m = 6 and 10. The micelles of 16-CH2-p-CH2-16,2Br(-) are prolate ellipsoidal for all the values of p = 1 - 3. It is also found that micelles of 16-m-16,2Na(+) and 16-CH2-p-CH2-16,2Br(-) are large in comparison to those of 16-in-16,2Br(-) for similar spacer lengths. This is connected with the fact that both in 16-m-16,2Na(+) and 16-CH2-p-CH2-16,2Br(-), the head group or the spacer is more hydrated as compared to that in the 16-m-16,2Br(-). An increase in the hydration of the spacer or the head group increases the screening of the Coulomb repulsion between the charged head groups. This effect has been found to be more pronounced in the dimeric surfactants having wettable spacers. [S1063-651X(99)00303-7].

Nature and strength of C-H center dot center dot center dot O interactions involving formyl hydrogen atoms: computational and experimental studies of small aldehydes

Structural and electronic properties of C-H center dot center dot center dot O contacts in compounds containing a formyl group are investigated from the perspective of both hydrogen bonding and dipole-dipole interactions, in a systematic and graded approach. The effects of a-substitution and self-association on the nature of the formyl H-atom are studied with the NBO and AIM methodologies. The relative dipole-dipole contributions in formyl C-H center dot center dot center dot O interactions are obtained for aldehyde dimers. The stabilities and energies of aldehyde clusters (dimer through octamer) have been examined computationally. Such studies have an implication in crystallization mechanisms. Experimental X-ray crystal structures of formaldehyde, acrolein and N-methylformamide have been determined in order to ascertain the role of C-H center dot center dot center dot O interactions in the crystal packing of formyl compounds.

Single-machine scheduling with past-sequence-dependent setup times and learning effects: a parametric analysis

In this article, we consider the single-machine scheduling problem with past-sequence-dependent (p-s-d) setup times and a learning effect. The setup times are proportional to the length of jobs that are already scheduled; i.e. p-s-d setup times. The learning effect reduces the actual processing time of a job because the workers are involved in doing the same job or activity repeatedly. Hence, the processing time of a job depends on its position in the sequence. In this study, we consider the total absolute difference in completion times (TADC) as the objective function. This problem is denoted as 1/LE, (Spsd)/TADC in Kuo and Yang (2007) ('Single Machine Scheduling with Past-sequence-dependent Setup Times and Learning Effects', Information Processing Letters, 102, 22-26). There are two parameters a and b denoting constant learning index and normalising index, respectively. A parametric analysis of b on the 1/LE, (Spsd)/TADC problem for a given value of a is applied in this study. In addition, a computational algorithm is also developed to obtain the number of optimal sequences and the range of b in which each of the sequences is optimal, for a given value of a. We derive two bounds b* for the normalising constant b and a* for the learning index a. We also show that, when a < a* or b > b*, the optimal sequence is obtained by arranging the longest job in the first position and the rest of the jobs in short processing time order.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

Inhibition of the Interaction Between NS3 Protease and HCV IRES With a Small Peptide: A Novel Therapeutic Strategy

Recently, we have demonstrated that the protease domain of NS3 alone can bind specifically to hepatitis C virus (HCV) internal ribosome entry site (IRES) near the initiator AUG, dislodges human La protein and inhibits translation in favor of viral RNA replication. Here, by using a computational approach, the contact points of the protease on the HCV IRES were putatively mapped. A 30-mer NS3 peptide was designed from the predicted RNA-binding region that retained RNA-binding ability and also inhibited IRES-mediated translation. This peptide was truncated to 15 mer and this also demonstrated ability to inhibit HCV RNA-directed translation as well as replication. More importantly, its activity was tested in an in vivo mouse model by encapsulating the peptide in Sendai virus virosomes followed by intravenous delivery. The study demonstrates for the first time that the HCV NS3-IRES RNA interaction can be selectively inhibited using a small peptide and reports a strategy to deliver the peptide into the liver.