Rule-Based Category Learning in Children: The Role of Inhibitory Control

The present study examined category learning in relation to inhibitory control and working memory in children and adults. Results revealed that categorization performance improved with age. Young children struggled with rule learning, many older children were successful at rule learning, and most adults had no difficulty with the task. Model-based analyses suggested that performance differences were due to young children’s inability to inhibit the salient, but irrelevant rule. Interestingly, when the analyses focused only on older children and adults who used the task appropriate strategy, the age-related rule-based deficit disappeared. Also, results revealed that successful performance on the categorization task was associated with better inhibitory control for older children, whereas successful performance on the categorization task was associated with greater working memory in young children. These findings suggest that the ability to learn categories varies with age and it may be partially dependent on inhibitory control and working memory

Category Learning in Older Adulthood: Understanding and Reducing Age-Related Deficits

Executive functions are important for learning rule-based (RB) categories, as well as non-rule-based (NRB) categories (e.g., categories learned implicitly, without a verbal rule). However, executive functioning is known to decline with age, leading to age-related deficits in category learning. The current thesis examines RB and NRB category learning in older adults using category sets that vary in difficulty (e.g., rule complexity, number of stimulus dimensions, salience of stimulus dimensions). In Chapter 2, older adults and younger adults completed three category sets (simple single-dimensional RB, disjunctive RB, and NRB). Older adults learned the simple, single-dimensional rules quite well. In contrast to younger adults, older adults found complex disjunctive RB categories harder to learn than NRB categories because of the executive functioning demands associated with complex rule learning. In Chapter 3, I introduced a pre-training procedure prior to the disjunctive RB and NRB categorization task used in Chapter 2. This was done in an effort to reduce task demands, as to minimize age-related categorization deficits. Both RB and NRB category learning improved among older adults following pre-training, but the improvements to RB learning were more drastic, suggesting that executive functioning plays a heavier role in RB learning. In Study 1 of Chapter 4 I used a difficult, single-dimensional RB category set (i.e., the correct rule is based on the less salient stimulus dimension) and a NRB category set to further examine category learning in normal aging and to better understand the types of strategies used by older adults. Relative to younger adults, older adults struggled with learning both the RB and NRB category set because they used suboptimal rules during the RB task and a RB strategy during the NRB task. In Study 2 of Chapter 4, I used a pre-training procedure to familiarize older adults with the stimulus dimensions of the RB category set, reducing the executive function demands of the task. Pre-training improved RB accuracy and the consistency with which older adults applied the rule. Across all studies, executive functioning abilities were associated with RB and NRB category learning. Overall, the results from this thesis help to better understand the locus of age-related categorization deficits and offer a method of reducing these deficits

Terapi Musik Sebagai Lowering Depresi Postpartum Pada Wanita Pasca-melahirkan

According to some researches, 85% major depression followed by anxiety. Many research about anxiety especially experimental research shows that mu sic therapy has a significant effect to reduce anxiety and increase positive emotions. Music therapy has two roles or goals that are reinforcement and punishment. In this paper, the writer focus on music therapy as reinforcement or drivers of behavior change toward more positive. Expected that these changes can be settled, attached, and continuous on related individuals. Based on those explanation assumed that music therapy can be used with its role as a reinforcement to be lowering the intensity of postpartum depression in some women postpartum. This paper written with library study methods and techniques of content analysis

Buckling resistance of hot‐finished CHS beam‐columns using FE modelling and machine learning

The use of circular hollow sections (CHS) has increased in recent years owing to its excellent mechanical behaviour including axial compression and torsional resistance as well as its aesthetic appearance. They are popular in a wide range of structural members including beams, columns, trusses and arches. The behaviour of hot-finished CHS beam-columns made from normal and high strength steel is the main focus of this paper. A particular attention is given to predict the ultimate buckling resistance of CHS beam-columns using the recent advancement of the artificial neural network (ANN). FE models were established and validated to generate an extensive parametric study. The ANN model is trained and validated using a total of 3439 data points collected from the generated FE models and experimental tests available in the literature. A comprehensive comparative analysis with the design rules in Eurocode 3 is conducted to evaluate the performance of the developed ANN model. It is shown that the proposed ANN based design formula provides a reliable means for predicting the buckling resistance of the CHS beam-columns. This formula can be easily implemented in any programming software, providing an excellent basis for engineers and designers to predict the buckling resistance resistance of the CHS beam-columns with a straightforward procedure in an efficient and sustainable manner with least computational time

Bond behaviour of austenitic stainless steel reinforced concrete

Stainless steel reinforced concrete has seen a large increase in usage in recent years, in response to the ever-increasing demands for structures and infrastructure to be more durable, efficient and sustainable. Currently, existing design standards advise using the same design rules for stainless steel reinforced concrete as traditional carbon steel reinforced concrete, owing to a lack of alternative information. However, this is not based on test or performance data. As such, there is a real need to develop a full and fundamental understanding of the bond behaviour of stainless steel reinforced concrete, to achieve more sustainable and reliable design methods for reinforced concrete structures. This paper investigates the bond behavior of stainless steel reinforced concrete and compares the performance to traditional carbon steel reinforced concrete, through experimental testing and analysis. It also compares the results to existing design rules in terms of bond strength, anchorage length and lap length. It is shown that stainless steel rebar generally develops lower bond strength with the surrounding concrete compared with equivalent carbon steel reinforcement. Moreover, it is shown that existing design codes are very conservative and generally underestimate the actual bond strength by a significant margin. Therefore, following detailed analysis, it is concluded that current design rules can be safely applied for stainless steel rebar, although more accurate and efficient methods can be achieved. Hence, new design parameters are proposed reflecting the bond behaviour of stainless steel rebar, so that more efficient designs can be achieved. Moreover, a summary of recommendations for the codes of practice is provided

Flexural analysis and design of stainless steel reinforced concrete beams

The use of stainless steel reinforcement in concrete structures has increased in recent years, particularly in applications where corrosion and chemical resistance is desirable such as bridges, retaining walls and tunnels. Stainless steel has a wide range of attractive properties including excellent mechanical strength, fire resistance, durability and also a long life-cycle compared with carbon steel. However, it is also has a higher initial cost, and therefore needs to be used carefully and efficiently. The existing material models provided for the structural analysis of reinforced concrete members in current design standards, such as Eurocode 2, are not appropriate for stainless steel reinforced concrete and lead to overly conservative (or indeed unconservative in some cases) predictions of the section capacity. Generally, there is a lack of data in the public domain regarding the behaviour of concrete beams reinforced with stainless steel, mainly owing to this being a relatively new and novel topic. In this context, the current paper provides a detailed background of the existing information on stainless steel reinforced concrete, as well a discussion on the potential advantages and challenges. Then, attention is given to analysing the behaviour of stainless steel reinforced concrete beams by developing the Continuous Strength Method to predict the bending moment capacity. A finite element model has been develop in order to further assess the performance, and this is also used to conduct a parametric study of the most influential properties. It is concluded that the proposed analytical models provides a reliable solution for predicting the capacity of concrete beams reinforced with stainless steel

Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes

Switching of Geometric Phase in Degenerate Systems

The geometric and open path phases of a four-state system subject to time varying cyclic potentials are computed from the Schr\"{o}dinger equation. Fast oscillations are found in the non-adiabatic case. For parameter values such that the system possesses degenerate levels, the geometric phase becomes anomalous, undergoing a sign switch. A physical system to which the results apply is a molecular dimer with two interacting electrons. Additionally, the sudden switching of the geometric phase promises to be an efficient control in two-qubit quantum computing.Comment: 15 pages, 4 figures,accepted by Physics Letters A (2000

QED for a Fibrillar Medium of Two-Level Atoms

We consider a fibrillar medium with a continuous distribution of two-level atoms coupled to quantized electromagnetic fields. Perturbation theory is developed based on the current algebra satisfied by the atomic operators. The one-loop corrections to the dispersion relation for the polaritons and the dielectric constant are computed. Renormalization group equations are derived which demonstrate a screening of the two-level splitting at higher energies. Our results are compared with known results in the slowly varying envelope and rotating wave approximations. We also discuss the quantum sine-Gordon theory as an approximate theory.Comment: 32 pages, 4 figures, uses harvmac and epsf. In this revised version, infra-red divergences are more properly handle