Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows

This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term relationships in query language modeling, which takes into account terms implicitly associated with different subsets of query terms. Existing approaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associations and also suffer from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback documents that are segmented into variable length chunks via multiple sliding windows of different sizes. Extensive experiments have been conducted on various TREC collections and our approach significantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.

Modeling nitrous oxide emissions from irrigated agriculture : testing DayCent with high-frequency measurements

A unique high temporal frequency dataset from an irrigated cotton-wheat rotation was used to test the agroecosystem model DayCent to simulate daily N2O emissions from sub-tropical vertisols under different irrigation intensities. DayCent was able to simulate the effect of different irrigation intensities on N2O fluxes and yield, although it tended to overestimate seasonal fluxes during the cotton season. DayCent accurately predicted soil moisture dynamics and the timing and magnitude of high fluxes associated with fertilizer additions and irrigation events. At the daily scale we found a good correlation of predicted vs. measured N2O fluxes (r2 = 0.52), confirming that DayCent can be used to test agricultural practices for mitigating N2O emission from irrigated cropping systems. A 25 year scenario analysis indicated that N2O losses from irrigated cotton-wheat rotations on black vertisols in Australia can be substantially reduced by an optimized fertilizer and irrigation management system (i.e. frequent irrigation, avoidance of excessive fertiliser application), while sustaining maximum yield potentials.

Factorial validity and invariance of questionnaires measuring social-cognitive determinants of physical activity among adolescent girls

Background There are few theoretically derived questionnaires of physical activity determinants among youth, and the existing questionnaires have not been subjected to tests of factorial validity and invariance, The present study employed confirmatory factor analysis (CFA) to test the factorial validity and invariance of questionnaires designed to be unidimensional measures of attitudes, subjective norms, perceived behavioral control, and self-efficacy about physical activity. Methods Adolescent girls in eighth grade from two cohorts (N = 955 and 1,797) completed the questionnaires at baseline; participants from cohort 1 (N = 845) also completed the questionnaires in ninth grade (i.e., 1-year follow-up). Factorial validity and invariance were tested using CFA with full-information maximum likelihood estimation in AMOS 4.0, Initially, baseline data from cohort 1 were employed to test the fit and, when necessary, to modify the unidimensional models. The models were cross-validated using a multigroup analysis of factorial invariance on baseline data from cohorts 1 and 2, The models then were subjected to a longitudinal analysis of factorial invariance using baseline and follow-up data from cohort i, Results The CFAs supported the fit of unidimensional models to the four questionnaires, and the models were cross-validated, as indicated by evidence of multigroup factorial invariance, The models also possessed evidence of longitudinal factorial invariance. Conclusions Evidence was provided for the factorial validity and the invariance of the questionnaires designed to be unidimensional measures of attitudes, subjective norms, perceived behavioral control, and self-efficacy about physical activity among adolescent girls, (C) 2000 American Health Foundation and academic Press.

Modeling and sensorless direct torque and flux control of a dual-airgap axial flux permanent-magnet machine with field-weakening operation

This paper presents the modeling and motion-sensorless direct torque and flux control of a novel dual-airgap axial-flux permanent-magnet machine optimized for use in flywheel energy storage system (FESS) applications. Independent closed-loop torque and stator flux regulation are performed in the stator flux ( x-y) reference frame via two PI controllers. This facilitates fast torque dynamics, which is critical as far as energy charging/discharging in the FESS is concerned. As FESS applications demand high-speed operation, a new field-weakening algorithm is proposed in this paper. Flux weakening is achieved autonomously once the y-axis voltage exceeds the available inverter voltage. An inherently speed sensorless stator flux observer immune to stator resistance variations and dc-offset effects is also proposed for accurate flux and speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a machine prototype.

Modeling and position-sensorless control of a dual-airgap axial flux permanent magnet machine for flywheel energy storage systems

This paper presents the modeling and position-sensorless vector control of a dual-airgap axial flux permanent magnet (AFPM) machine optimized for use in flywheel energy storage system (FESS) applications. The proposed AFPM machine has two sets of three-phase stator windings but requires only a single power converter to control both the electromagnetic torque and the axial levitation force. The proper controllability of the latter is crucial as it can be utilized to minimize the vertical bearing stress to improve the efficiency of the FESS. The method for controlling both the speed and axial displacement of the machine is discussed. An inherent speed sensorless observer is also proposed for speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a prototype machine.

Modeling and analysis of a novel variable-speed cage induction generator

This paper introduces a novel cage induction generator and presents a mathematical model, through which its behavior can be accurately predicted. The proposed generator system employs a three-phase cage induction machine and generates single-phase and constant-frequency electricity at varying rotor speeds without an intermediate inverter stage. The technique uses any one of the three stator phases of the machine as the excitation winding and the remaining two phases, which are connected in series, as the power winding. The two-series-connected-and-one-isolated (TSCAOI) phase winding configuration magnetically decouples the two sets of windings, enabling independent control. Electricity is generated through the power winding at both sub- and super-synchronous speeds with appropriate excitation to the isolated single winding at any frequency of generation. A dynamic mathematical model, which accurately predicts the behavior of the proposed generator, is also presented and implemented in MATLAB/Simulink. Experimental results of a 2-kW prototype generator under various operating conditions are presented, together with theoretical results, to demonstrate the viability of the TSCAOI power generation. The proposed generator is simple and capable of both storage and retrieval of energy through its excitation winding and is expected to be suitable for applications, such as small wind turbines and microhydro systems.

Can physical activity prevent physical and cognitive decline in postmenopausal women? A systematic review of the literature

Background Participation in regular physical activity is among the most promising and cost effective strategies to reduce physical and cognitive decline and premature death. However, confusion remains about the amount, frequency, and duration of physical activity that is likely to provide maximum benefit as well as the way in which interventions should be delivered. Aims This paper aimed to review research on the impact of leisure-time and general physical activity levels on physical and cognitive decline in postmenopausal women. In a systematic review of the literature, empirical literature from 2009-2013 is reviewed to explore the potential impact of either commencing or sustaining physical activity on older women’s health. Results All studies found that physical activity was associated with lower rates of cognitive and physical decline and a significant reduction in all-cause mortality. In this review we found that exercise interventions (or lifestyle activities) that improved cardiorespiratory exercise capacity showed the most positive impact on physical health. Conclusions Findings suggest that programs should facilitate and support women to participate in regular exercise by embedding physical activity programs in public health initiatives, by developing home-based exercise programs that require few resources and by creating interventions that can incorporate physical activity within a healthy lifestyle. The review also suggests that clinicians should consider prescribing exercise in a tailored manner for older women to ensure that it is of a high enough intensity to obtain the positive sustained effects of exercise.

Relationship between expectation management and client retention in online Cognitive Behavioural Therapy

Background Engaging clients from the outset of psychotherapy is important for therapeutic success. However, there is little research evaluating therapists’ initial attempts to engage clients. This article reports retrospective analysis of data from a trial of online Cognitive Behavioural Therapy (CBT) for depression. Qualitative and quantitative methods were used to evaluate how therapists manage clients’ expectations at the outset of therapy and its relationship with client retention in the therapeutic intervention. Aims To develop a system to codify expectation management in initial sessions of online CBT and evaluate its relationship with retention. Method Initial qualitative research using conversation analysis identified three different communication practices used by therapists at the start of first sessions: no expectation management, some expectation management, and comprehensive expectation management. These findings were developed into a coding scheme that enabled substantial inter-rater agreement (weighted Kappa = 0.78; 95% CI: 0.52 to 0.94) and was applied to all trial data. Results Adjusting for a range of client variables, primary analysis of data from 147 clients found comprehensive expectation management was associated with clients remaining in therapy for 1.4 sessions longer than those who received no expectation management (95% CI: -0.2 to 3.0). This finding was supported by a sensitivity analysis including an additional 21 clients (1.6 sessions, 95% CI: 0.2 to 3.1). Conclusions Using a combination of qualitative and quantitative methods, this study suggests a relationship between expectation management and client retention in online CBT for depression, which has implications for professional practice. A larger prospective study would enable a more precise estimate of retention.

Managing clients’ expectations at the outset of online Cognitive Behavioural Therapy (CBT) for depression

Background Engaging clients in psychotherapy by managing their expectations is important for therapeutic success. Initial moments in first sessions of therapy are thought to afford an opportunity to establish a shared understanding of how therapy will proceed. However there is little evidence from analysis of actual sessions of therapy to support this. Objective This study utilised recordings to examine how therapists manage clients’ expectations during the first two sessions of online Cognitive Behavioural Therapy (CBT). Methods Expectation management was investigated through conversation analysis of sessions from 176 client-therapist dyads involved in online CBT. The primary focus of analysis was expectation management during the initial moments of first sessions, with a secondary focus on expectations at subsequent points. Analysis Clients’ expectations for therapy were most commonly managed during the initial moments of first sessions of therapy. At this point, most therapists either outlined the tasks of the first and subsequent sessions (n=36), or the first session only (n=108). On other occasions (n = 32), no attempt was made to manage clients’ expectations by outlining what would happen in therapy. Observations of the interactional consequences of such an absence suggest clients may struggle to engage with the therapeutic process in the absence of appropriate expectation management by therapists. Conclusion Clients may more readily engage from the outset of therapy when provided with an explanation that manages their expectation of what is involved. Therapists can accomplish this by projecting how therapy will proceed, particularly beyond the initial session.

Graphene and carbon nanotubes from arc plasmas : experiment and plasma modeling

Graphene and carbon nanotubes are the most promising nanomaterials for application in various modern nanodevices. The successful production of the nanotubes and graphene in a single process was achieved by using a magnetically enhanced arc discharge in helium atmosphere between carbon and metal electrodes. A 3-D fluid model has been used to investigate the discharge parameters.

Mathematical modeling of the cancer cell’s control circuitry : paving the way to individualized therapeutic strategies

Cancer is a disease of signal transduction in which the dysregulation of the network of intracellular and extracellular signaling cascades is sufficient to thwart the cells finely-tuned biochemical control mechanisms. A keen interest in the mathematical modeling of cell signaling networks and the regulation of signal transduction has emerged in recent years, and has produced a glimmer of insight into the sophisticated feedback control and network regulation operating within cells. In this review, we present an overview of published theoretical studies on the control aspects of signal transduction, emphasizing the role and importance of mechanisms such as ‘ultrasensitivity’ and feedback loops. We emphasize that these exquisite and often subtle control strategies represent the key to orchestrating ‘simple’ signaling behaviors within the complex intracellular network, while regulating the trade-off between sensitivity and robustness to internal and external perturbations. Through a consideration of these apparent paradoxes, we explore how the basic homeostasis of the intracellular signaling network, in the face of carcinogenesis, can lead to neoplastic progression rather than cell death. A simple mathematical model is presented, furnishing a vivid illustration of how ‘control-oriented’ models of the deranged signaling networks in cancer cells may enucleate improved treatment strategies, including patient-tailored combination therapies, with the potential for reduced toxicity and more robust and potent antitumor activity.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

Disentangling the cognitive components supporting Austin Maze performance in left versus right temporal lobe epilepsy

Neuropsychological tests requiring patients to find a path through a maze can be used to assess visuospatial memory performance in temporal lobe pathology, particularly in the hippocampus. Alternatively, they have been used as a task sensitive to executive function in patients with frontal lobe damage. We measured performance on the Austin Maze in patients with unilateral left and right temporal lobe epilepsy (TLE), with and without hippocampal sclerosis, compared to healthy controls. Performance was correlated with a number of other neuropsychological tests to identify the cognitive components that may be associated with poor Austin Maze performance. Patients with right TLE were significantly impaired on the Austin Maze task relative to patients with left TLE and controls, and error scores correlated with their performance on the Block Design task. The performance of patients with left TLE was also impaired relative to controls; however, errors correlated with performance on tests of executive function and delayed recall. The presence of hippocampal sclerosis did not have an impact on maze performance. A discriminant function analysis indicated that the Austin Maze alone correctly classified 73.5% of patients as having right TLE. In summary, impaired performance on the Austin Maze task is more suggestive of right than left TLE; however, impaired performance on this visuospatial task does not necessarily involve the hippocampus. The relationship of the Austin Maze task with other neuropsychological tests suggests that differential cognitive components may underlie performance decrements in right versus left TLE.

The effect of prior visual information on recognition of speech and sounds

To identify and categorize complex stimuli such as familiar objects or speech, the human brain integrates information that is abstracted at multiple levels from its sensory inputs. Using cross-modal priming for spoken words and sounds, this functional magnetic resonance imaging study identified 3 distinct classes of visuoauditory incongruency effects: visuoauditory incongruency effects were selective for 1) spoken words in the left superior temporal sulcus (STS), 2) environmental sounds in the left angular gyrus (AG), and 3) both words and sounds in the lateral and medial prefrontal cortices (IFS/mPFC). From a cognitive perspective, these incongruency effects suggest that prior visual information influences the neural processes underlying speech and sound recognition at multiple levels, with the STS being involved in phonological, AG in semantic, and mPFC/IFS in higher conceptual processing. In terms of neural mechanisms, effective connectivity analyses (dynamic causal modeling) suggest that these incongruency effects may emerge via greater bottom-up effects from early auditory regions to intermediate multisensory integration areas (i.e., STS and AG). This is consistent with a predictive coding perspective on hierarchical Bayesian inference in the cortex where the domain of the prediction error (phonological vs. semantic) determines its regional expression (middle temporal gyrus/STS vs. AG/intraparietal sulcus).