Adaptive autobiographical memory in younger and older adults : the indirect association of integrative and instrumental reminiscence with depressive symptoms

Despite the established effectiveness of reminiscence-based interventions for depression, little research exists into the pathways through which specific reminiscence functions are related to depressive symptoms. Drawing on theory of the mechanisms of change in cognitive-reminiscence therapy, the current study tests the hypothesised indirect associations of adaptive integrative and instrumental reminiscence functions with depressive symptoms and whether these relationships might differ among younger and older adults. Questionnaires were completed by a large community sample of the Australian population. Multiple mediation models were tested in two groups: younger adults (n=730, M age=52.24, SD=9.84) and older adults (n=725, M age= 73.59, SD=6.29). Results were consistent across age groups, indicating that there was direct relationship between these reminiscence functions and depressive symptoms, but that integrative reminiscence is indirectly associated with depressive symptoms through meaning in life, self-esteem, and optimism, and that instrumental reminiscence is indirectly associated with depressive symptoms through primary control and self-efficacy. This study provides support for the relationships between constructs underlying the proposed mechanisms of change in cognitive-reminiscence therapy for the treatment of depression, and suggests these relationships are similar for younger and older adults.

Investigating nanoparticle-substrate interaction in LSPR biosensing using the image-charge theory

Localized surface plasmon resonance (LSPR) has been used to develop optical biosensors. Tuning the resonance wavelength to detect target biomolecules with a particular dipolar resonance is essential when designing LSPR biosensors. In this paper, the interaction of nanoparticles (NPs) with glass substrate (SiO2) for LSPR wavelength is investigated using the concept of the image-charge theory. Using the FDTD method, it is shown how the NP and substrate size change the plasmon wavelength. Next, this phenomenon is interpreted using the analytical electrostatic eigenvalue method.

Retracted: Experimental evidence that maternal corticosterone controls adaptive offspring sex ratios

1. Sex allocation theory has received considerable attention, yet the mechanism(s) by which mothers skew offspring sex ratios remain unknown. In birds, females are the heterogametic sex, which potentially gives them control of whether gametes will be male or female. How females might control the sex of the gamete is unclear, but one possibility is that variation in steroid hormones may mediate this process. 2. We experimentally altered circulating levels of corticosterone in female Gouldian finches (Erythrura gouldiae), a species that demonstrates both extreme stress responses and extreme offspring sex ratio biases when breeding with a low-quality (genetically incompatible) partner. 3. During egg production, individual females received both corticosterone and metyrapone (a corticosterone-synthesis inhibitor) implants, in random order, to induce both high and low levels of circulating stress hormones (within physiological limits). 4. We found that females with elevated corticosterone levels produced male-biased sex ratios, but when the same females were treated with metyrapone they produced female-biased offspring sex ratios. 5. These stress responses are adaptive because females constrained to breeding with low-quality males can substantially increase their fitness by overproducing sons. Changes in maternal corticosterone levels during stressful situations, such as the quality of a breeding partner, may provide an endocrine mechanism that can be exploited for strategic sex allocation.

Adaptive-multi-reference least means squares filter

Adaptive filters are now becoming increasingly studied for their suitability in application to complex and non-stationary signals. Many adaptive filters utilise a reference input, that is used to form an estimate of the noise in the target signal. In this paper we discuss the application of adaptive filters for high electromyography contaminated electroencephalography data. We propose the use of multiple referential inputs instead of the traditional single input. These references are formed using multiple EMG sensors during an EEG experiment, each reference input is processed and ordered through firstly determining the Pearson’s r-squared correlation coefficient, from this a weighting metric is determined and used to scale and order the reference channels according to the paradigm shown in this paper. This paper presents the use and application of the Adaptive-Multi-Reference (AMR) Least Means Square adaptive filter in the domain of electroencephalograph signal acquisition.

Adaptive translational cueing motion algorithm using fuzzy based tilt coordination

Driving simulators have become useful research tools for the institution and laboratories which are studying in different fields of vehicular and transport design to increase road safety. Although classical washout filters are broadly used because of their short processing time, simplicity and ease of adjust, they have some disadvantages such as generation of wrong sensation of motions, false cue motions, and also their tuning process which is focused on the worst case situations leading to a poor usage of the workspace. The aim of this study is to propose a new motion cueing algorithm that can accurately transform vehicle specific force into simulator platform motions at high fidelity within the simulator’s physical limitations. This method is proposed to compensate wrong cueing motion caused by saturation of tilt coordination rate limit using an adaptive correcting signal based on added fuzzy logic into translational channel to minimize the human sensation error and exploit the platform more efficiently.

Adaptive washout algorithm based fuzzy tuning for improving human perception

The aim of this paper is to provide a washout filter that can accurately produce vehicle motions in the simulator platform at high fidelity, within the simulators physical limitations. This is to present the driver with a realistic virtual driving experience to minimize the human sensation error between the real driving and simulated driving situation. To successfully achieve this goal, an adaptive washout filter based on fuzzy logic online tuning is proposed to overcome the shortcomings of fixed parameters, lack of human perception and conservative motion features in the classical washout filters. The cutoff frequencies of highpass, low-pass filters are tuned according to the displacement information of platform, workspace limitation and human sensation in real time based on fuzzy logic system. The fuzzy based scaling method is proposed to let the platform uses the workspace whenever is far from its margins. The proposed motion cueing algorithm is implemented in MATLAB/Simulink software packages and provided results show the capability of this method due to its better performance, improved human sensation and exploiting the platform more efficiently without reaching the motion limitation.

A new computerized adaptive test advancing the measurement of health-related quality of life (HRQoL) in children: the Kids-CAT

Purpose: Assessing health-related quality of life (HRQoL) via Computerized Adaptive Tests (CAT) provides greater measurement precision coupled with a lower test burden compared to conventional tests. Currently, there are no European pediatric HRQoL CATs available. This manuscript aims at describing the development of a HRQoL CAT for children and adolescents: the Kids-CAT, which was developed based on the established KIDSCREEN-27 HRQoL domain structure. Methods: The Kids-CAT was developed combining classical test theory and item response theory methods and using large archival data of European KIDSCREEN norm studies (n = 10,577–19,580). Methods were applied in line with the US PROMIS project. Item bank development included the investigation of unidimensionality, local independence, exploration of Differential Item Functioning (DIF), evaluation of Item Response Curves (IRCs), estimation and norming of item parameters as well as first CAT simulations. Results: The Kids-CAT was successfully built covering five item banks (with 26–46 items each) to measure physical well-being, psychological well-being, parent relations, social support and peers, and school well-being. The Kids-CAT item banks proved excellent psychometric properties: high content validity, unidimensionality, local independence, low DIF, and model conform IRCs. In CAT simulations, seven items were needed to achieve a measurement precision between.8 and.9 (reliability). It has a child-friendly design, is easy accessible online and gives immediate feedback reports of scores. Conclusions: The Kids-CAT has the potential to advance pediatric HRQoL measurement by making it less burdensome and enhancing the patient–doctor communication.

Erratum to: A new computerized adaptive test advancing the measurement of health-related quality of life (HRQoL) in children: the Kids-CAT

Abstract
Purpose
Assessing health-related quality of life (HRQoL) via Computerized Adaptive Tests (CAT) provides greater measurement precision coupled with a lower test burden compared to conventional tests. Currently, there are no European pediatric HRQoL CATs available. This manuscript aims at describing the development of a HRQoL CAT for children and adolescents: the Kids-CAT, which was developed based on the established KIDSCREEN-27 HRQoL domain structure.
Methods
The Kids-CAT was developed combining classical test theory and item response theory methods and using large archival data of European KIDSCREEN norm studies (n=10,577–19,580). Methods were applied in line with the US PROMIS project. Item bank development included the investigation of unidimensionality, local independence, exploration of Differential Item Functioning (DIF), evaluation of Item Response Curves (IRCs), estimation and norming of item parameters as well as first CAT simulations.
Results
The Kids-CAT was successfully built covering five item banks (with 26–46 items each) to measure physical well-being, psychological well-being, parent relations, social support and peers, and school well-being. The Kids-CAT item banks proved excellent psychometric properties: high content validity, unidimensionality, local independence, low DIF, and model conform IRCs. In CAT simulations, seven items were needed to achieve a measurement precision between .8 and .9 (reliability). It has a child-friendly design, is easy accessible online and gives immediate feedback reports of scores.
Conclusions
The Kids-CAT has the potential to advance pediatric HRQoL measurement by making it less burdensome and enhancing the patient–doctor communication.

Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing

Cloud service selection in a multi-cloud computing environment is receiving more and more attentions. There is an abundance of emerging cloud service resources that makes it hard for users to select the better services for their applications in a changing multi-cloud environment, especially for online real time applications. To assist users to efficiently select their preferred cloud services, a cloud service selection model adopting the cloud service brokers is given, and based on this model, a dynamic cloud service selection strategy named DCS is put forward. In the process of selecting services, each cloud service broker manages some clustered cloud services, and performs the DCS strategy whose core is an adaptive learning mechanism that comprises the incentive, forgetting and degenerate functions. The mechanism is devised to dynamically optimize the cloud service selection and to return the best service result to the user. Correspondingly, a set of dynamic cloud service selection algorithms are presented in this paper to implement our mechanism. The results of the simulation experiments show that our strategy has better overall performance and efficiency in acquiring high quality service solutions at a lower computing cost than existing relevant approaches.

Reduced mu suppression and altered motor resonance in euthymic bipolar disorder: evidence for a dysfunctional mirror system?

Social cognitive difficulties are common in the acute phase of bipolar disorder and, to a lesser extent, during the euthymic stage, and imaging studies of social cognition in euthymic bipolar disorder have implicated mirror system brain regions. This study aimed to use a novel multimodal approach (i.e., including both transcranial magnetic stimulation (TMS) and electroencephalogram (EEG)) to investigate mirror systems in bipolar disorder. Fifteen individuals with euthymic bipolar disorder and 16 healthy controls participated in this study. Single-pulse TMS was applied to the optimal site in the primary motor cortex (M1), which stimulates the muscle of interest during the observation of hand movements (goal-directed or interacting) designed to elicit mirror system activity. Single EEG electrodes (C3, CZ, C4) recorded mu rhythm modulation concurrently. Results revealed that the patient group showed significantly less mu suppression compared to healthy controls. Surprisingly, motor resonance was not significantly different overall between groups; however, bipolar disorder participants showed a pattern of reduced reactivity on some conditions. Although preliminary, this study indicates a potential mirror system deficit in euthymic bipolar disorder, which may contribute to the pathophysiology of the disorder.

AMC: an adaptive multi-level cache algorithm in hybrid storage systems

Hybrid storage systems that consist of flash-based solid state drives (SSDs) and traditional disks are now widely used. In hybrid storage systems, there exists a two-level cache hierarchy that regard dynamic random access memory (DRAM) as the first level cache and SSD as the second level cache for disk storage. However, this two-level cache hierarchy typically uses independent cache replacement policies for each level, which makes cache resource management inefficient and reduces system performance. In this paper, we propose a novel adaptive multi-level cache (AMC) replacement algorithm in hybrid storage systems. The AMC algorithm adaptively adjusts cache blocks between DRAM and SSD cache levels using an integrated solution. AMC uses combined selective promote and demote operations to dynamically determine the level in which the blocks are to be cached. In this manner, the AMC algorithm achieves multi-level cache exclusiveness and makes cache resource management more efficient. By using real-life storage traces, our evaluation shows the proposed algorithm improves hybrid multi-level cache performance and also increases the SSD lifetime compared with traditional multi-level cache replacement algorithms.

Multivariate adaptive autoregressive modeling and kalman filtering for motor imagery BCI

Adaptive autoregressive (AAR) modeling of the EEG time series and the AAR parameters has been widely used in Brain computer interface (BCI) systems as input features for the classification stage. Multivariate adaptive autoregressive modeling (MVAAR) also has been used in literature. This paper revisits the use of MVAAR models and propose the use of adaptive Kalman filter (AKF) for estimating the MVAAR parameters as features in a motor imagery BCI application. The AKF approach is compared to the alternative short time moving window (STMW) MVAAR parameter estimation approach. Though the two MVAAR methods show a nearly equal classification accuracy, the AKF possess the advantage of higher estimation update rates making it easily adoptable for on-line BCI systems.

Design of distributed adaptive neural traffic signal timing controller by cuckoo search optimization

This paper focuses on designing an adaptive controller for controlling traffic signal timing. Urban traffic is an inevitable part in modern cities and traffic signal controllers are effective tools to control it. In this regard, this paper proposes a distributed neural network (NN) controller for traffic signal timing. This controller applies cuckoo search (CS) optimization methods to find the optimal parameters in design of an adaptive traffic signal timing control system. The evaluation of the performance of the designed controller is done in a multi-intersection traffic network. The developed controller shows a promising improvement in reducing travel delay time compared to traditional fixed-time control systems.

Complex adaptive systems as a valid framework for understanding community level development

Complex adaptive systems (CAS) theory is gaining mainstream recognition in development policy and management. This article looks to the correlations between development theory and CAS theory to support an argument for the validity of community level development as a complex adaptive system. The article describes some theoretical and practical implications of using CAS theory as a framework for community level development. This includes a call for researchers and practitioners to understand more thoroughly the contextualised nature of development, and the communities in which development interventions are implemented.

Supporting adaptive tour with high level petri nets

One of the issues for tour planning applications is to adaptively provide personalized advices for different types of tourists and tour activities. This paper proposes a high level Petri Nets based approach to providing some level of adaptation by implementing adaptive navigation in a tour node space. The new model supports dynamic reordering or removal of tour nodes along a tour path; it supports multiple travel modes and incorporates multimodality within its tour planning logic to derive adaptive tour. Examples are given to demonstrate how to realize adaptive interfaces and personalization. Future directions are also discussed at the end of this paper.