A randomized controlled trial of a 10 percent CPP-ACP cream to reduce mutans streptococci colonization

Purpose The purpose of this study was to investigate the effectiveness of a 10 percent casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) cream to reduce mutans streptococci (MS) colonization and prevent early childhood caries. Methods The cohort was randomized at mean age of 11 days old to receive once-daily CPP-ACP cream (n=102) or no product (comparison group; n=89) from the time of first tooth eruption. All mothers were contacted by telephone at six, 12, and 18 months and advised to brush their children's teeth twice daily with low-dose fluoride toothpaste. At 24 months, all children were examined at a community clinic. Results At 24 months old, one out of 65 (2 percent) children in the CPP-ACP group had caries vs. four out of 58 (seven percent) in the comparison group (difference not statistically significant). There were fewer MS-positive children in the CPP-ACP group (26 percent) vs. the comparison group (47 percent; P=.02). A dose-response effect of CPP-ACP usage on MS was observed, where MS was present in eight percent of regular CPP-ACP users, 28 percent of irregular users, and 47 percent of non-users (P<.02). Conclusions CPP-ACP reduced the percentages of mutans streptococci-positive 24-month-old children, although it did not reduce caries prevalence.

A randomized controlled trial of self-management programme improves health-related outcomes of older people with heart failure

Aims This paper is a report on the effectiveness of a self-management programme based on the self-efficacy construct, in older people with heart failure. Background Heart failure is a major health problem worldwide, with high mortality and morbidity, making it a leading cause of hospitalization. Heart failure is associated with a complex set of symptoms that arise from problems in fluid and sodium retention. Hence, managing salt and fluid intake is important and can be enhanced by improving patients' self-efficacy in changing their behaviour. Design Randomized controlled trial. Methods Heart failure patients attending cardiac clinics in northern Taiwan from October 2006–May 2007 were randomly assigned to two groups: control (n = 46) and intervention (n = 47). The intervention group received a 12-week self-management programme that emphasized self-monitoring of salt/fluid intake and heart failure-related symptoms. Data were collected at baseline as well as 4 and 12 weeks later. Data analysis to test the hypotheses used repeated-measures anova models. Results Participants who received the intervention programme had significantly better self-efficacy for salt and fluid control, self-management behaviour and their heart failure-related symptoms were significantly lower than participants in the control group. However, the two groups did not differ significantly in health service use. Conclusion The self-management programme improved self-efficacy for salt and fluid control, self-management behaviours, and decreased heart failure-related symptoms in older Taiwanese outpatients with heart failure. Nursing interventions to improve health-related outcomes for patients with heart failure should emphasize self-efficacy in the self-management of their disease.

A modified particle swarm optimization for correlated phenomena

The wide applicability of correlation analysis inspired the development of this paper. In this paper, a new correlated modified particle swarm optimization (COM-PSO) is developed. The Correlation Adjustment algorithm is proposed to recover the correlation between the considered variables of all particles at each of iterations. It is shown that the best solution, the mean and standard deviation of the solutions over the multiple runs as well as the convergence speed were improved when the correlation between the variables was increased. However, for some rotated benchmark function, the contrary results are obtained. Moreover, the best solution, the mean and standard deviation of the solutions are improved when the number of correlated variables of the benchmark functions is increased. The results of simulations and convergence performance are compared with the original PSO. The improvement of results, the convergence speed, and the ability to simulate the correlated phenomena by the proposed COM-PSO are discussed by the experimental results.

An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

An efficient hybrid evolutionary optimization algorithm for daily volt/var control at distribution system including DGs

This paper presents a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for daily Volt/Var control in distribution system including Distributed Generators (DGs). Due to the small X/R ratio and radial configuration of distribution systems, DGs have much impact on this problem. Since DGs are independent power producers or private ownership, a price based methodology is proposed as a proper signal to encourage owners of DGs in active power generation. Generally, the daily Volt/Var control is a nonlinear optimization problem. Therefore, an efficient hybrid evolutionary method based on Particle Swarm Optimization and Ant Colony Optimization (ACO), called HPSO, is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. The feasibility of the proposed algorithm is demonstrated and compared with methods based on the original PSO, ACO and GA algorithms on IEEE 34-bus distribution feeder.

A novel algorithm based on honey bee mating optimization for distribution harmonic state estimation including distributed generators

This paper presents a new algorithm based on honey-bee mating optimization (HBMO) to estimate harmonic state variables in distribution networks including distributed generators (DGs). The proposed algorithm performs estimation for both amplitude and phase of each harmonics by minimizing the error between the measured values from phasor measurement units (PMUs) and the values computed from the estimated parameters during the estimation process. Simulation results on two distribution test system are presented to demonstrate that the speed and accuracy of proposed distribution harmonic state estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as weight least square (WLS), genetic algorithm (GA) and tabu search (TS).

Distribution feeder reconfiguration for loss minimization based on modified honey bee mating optimization algorithm

This paper presents an efficient algorithm for multi-objective distribution feeder reconfiguration based on Modified Honey Bee Mating Optimization (MHBMO) approach. The main objective of the Distribution feeder reconfiguration (DFR) is to minimize the real power loss, deviation of the nodes’ voltage. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective. So the metahuristic algorithm has been applied to this problem. This paper describes the full algorithm to Objective functions paid, The results of simulations on a 32 bus distribution system is given and shown high accuracy and optimize the proposed algorithm in power loss minimization.

Optimization of an integrated model for automatic reduction and expansion of long queries

A long query provides more useful hints for searching relevant documents, but it is likely to introduce noise which affects retrieval performance. In order to smooth such adverse effect, it is important to reduce noisy terms, introduce and boost additional relevant terms. This paper presents a comprehensive framework, called Aspect Hidden Markov Model (AHMM), which integrates query reduction and expansion, for retrieval with long queries. It optimizes the probability distribution of query terms by utilizing intra-query term dependencies as well as the relationships between query terms and words observed in relevance feedback documents. Empirical evaluation on three large-scale TREC collections demonstrates that our approach, which is automatic, achieves salient improvements over various strong baselines, and also reaches a comparable performance to a state of the art method based on user’s interactive query term reduction and expansion.

Non-response bias in randomized control experiments in criminology : putting the Queensland Community Engagement Trial (QCET) under a microscope

Objectives The goal of this article is to examine whether or not the results of the Queensland Community Engagement Trial (QCET)-a randomized controlled trial that tested the impact of procedural justice policing on citizen attitudes toward police-were affected by different types of nonresponse bias. Method We use two methods (Cochrane and Elffers methods) to explore nonresponse bias: First, we assess the impact of the low response rate by examining the effects of nonresponse group differences between the experimental and control conditions and pooled variance under different scenarios. Second, we assess the degree to which item response rates are influenced by the control and experimental conditions. Results Our analysis of the QCET data suggests that our substantive findings are not influenced by the low response rate in the trial. The results are robust even under extreme conditions, and statistical significance of the results would only be compromised in cases where the pooled variance was much larger for the nonresponse group and the difference between experimental and control conditions was greatly diminished. We also find that there were no biases in the item response rates across the experimental and control conditions. Conclusion RCTs that involve field survey responses-like QCET-are potentially compromised by low response rates and how item response rates might be influenced by the control or experimental conditions. Our results show that the QCET results were not sensitive to the overall low response rate across the experimental and control conditions and the item response rates were not significantly different across the experimental and control groups. Overall, our analysis suggests that the results of QCET are robust and any biases in the survey responses do not significantly influence the main experimental findings.

Distribution state estimation based on particle swarm and doubly loop mutant optimization (DLM-PSO)

This paper presents a novel algorithm based on particle swarm optimization (PSO) to estimate the states of electric distribution networks. In order to improve the performance, accuracy, convergence speed, and eliminate the stagnation effect of original PSO, a secondary PSO loop and mutation algorithm as well as stretching function is proposed. For accounting uncertainties of loads in distribution networks, pseudo-measurements is modeled as loads with the realistic errors. Simulation results on 6-bus radial and 34-bus IEEE test distribution networks show that the distribution state estimation based on proposed DLM-PSO presents lower estimation error and standard deviation in comparison with algorithms such as WLS, GA, HBMO, and original PSO.

Can ginger ameliorate chemotherapy-induced nausea? Protocol of a randomized double blind, placebo-controlled trial

Background Preliminary research shows ginger may be an effective adjuvant treatment for chemotherapy-induced nausea and vomiting but significant limitations need to be addressed before recommendations for clinical practice can be made. Methods/Design In a double–blinded randomised-controlled trial, chemotherapy-naïve patients will be randomly allocated to receive either 1.2 g of a standardised ginger extract or placebo per day. The study medication will be administrated as an adjuvant treatment to standard anti-emetic therapy and will be divided into four capsules per day, to be consumed approximately every 4 hours (300 mg per capsule administered q.i.d) for five days during the first three cycles of chemotherapy. Acute, delayed, and anticipatory symptoms of nausea and vomiting will be assessed over this time frame using a valid and reliable questionnaire, with nausea symptoms being the primary outcome. Quality of life, nutritional status, adverse effects, patient adherence, cancer-related fatigue, and CINV-specific prognostic factors will also be assessed. Discussion Previous trials in this area have noted limitations. These include the inconsistent use of standardized ginger formulations and valid questionnaires, lack of control for anticipatory nausea and prognostic factors that may influence individual CINV response, and the use of suboptimal dosing regimens. This trial is the first to address these issues by incorporating multiple unique additions to the study design including controlling for CINV-specific prognostic factors by recruiting only chemotherapy-naïve patients, implementing a dosing schedule consistent with the pharmacokinetics of oral ginger supplements, and independently analysing ginger supplements before and after recruitment to ensure potency. Our trial will also be the first to assess the effect of ginger supplementation on cancer-related fatigue and nutritional status. Chemotherapy-induced nausea and vomiting are distressing symptoms experienced by oncology patients; this trial will address the significant limitations within the current literature and in doing so, will investigate the effect of ginger supplementation as an adjuvant treatment in modulating nausea and vomiting symptoms. Trial registration

Optimization of complex QoS-aware service compositions

In Service-oriented Architectures, business processes can be realized by composing loosely coupled services. The problem of QoS-aware service composition is widely recognized in the literature. Existing approaches on computing an optimal solution to this problem tackle structured business processes, i.e., business processes which are composed of XOR-block, AND-block, and repeat loop orchestration components. As of yet, OR-block and unstructured orchestration components have not been sufficiently considered in the context of QoS-aware service composition. The work at hand addresses this shortcoming. An approach for computing an optimal solution to the service composition problem is proposed considering the structured orchestration components, such as AND/XOR/OR-block and repeat loop, as well as unstructured orchestration components.

Optimization approaches for the design of additively manufactured scaffolds

Scaffolds play a pivotal role in tissue engineering, promoting the synthesis of neo extra-cellular matrix (ECM), and providing temporary mechanical support for the cells during tissue regeneration. Advances introduced by additive manufacturing techniques have significantly improved the ability to regulate scaffold architecture, enhancing the control over scaffold shape and porosity. Thus, considerable research efforts have been devoted to the fabrication of 3D porous scaffolds with optimized micro-architectural features. This chapter gives an overview of the methods for the design of additively manufactured scaffolds and their applicability in tissue engineering (TE). Along with a survey of the state of the art, the Authors will also present a recently developed method, called Load-Adaptive Scaffold Architecturing (LASA), which returns scaffold architectures optimized for given applied mechanical loads systems, once the specific stress distribution is evaluated through Finite Element Analysis (FEA).

Randomized trial of a clinic-based, community-supported, lifestyle intervention to improve physical activity and diet : The North Carolina enhanced WISEWOMAN project

Objective To determine if a clinic-based behavioral intervention program for low-income mid-life women that emphasizes use of community resources will increase moderate intensity physical activity (PA) and improve dietary intake. Methods Randomized trial conducted from May 2003 to December 2004 at one community health center in Wilmington, NC. A total of 236 women, ages 40–64, were randomized to receive an Enhanced Intervention (EI) or Minimal Intervention (MI). The EI consisted of an intensive phase (6 months) including 2 individual counseling sessions, 3 group sessions, and 3 phone calls from a peer counselor followed by a maintenance phase (6 months) including 1 individual counseling session and 7 monthly peer counselor calls. Both phases included efforts to increase participants' use of community resources that promote positive lifestyle change. The MI consisted of a one-time mailing of pamphlets on diet and PA. Outcomes, measured at 6 and 12 months, included the comparison of moderate intensity PA between study groups as assessed by accelerometer (primary outcome) and questionnaire, and dietary intake assessed by questionnaire and serum carotenoids (6 months only). Results For accelerometer outcomes, follow-up was 75% at 6 months and 73% at 12 months. Though moderate intensity PA increased in the EI and decreased in the MI, the difference between groups was not statistically significant (p = 0.45; multivariate model, p = 0.08); however, moderate intensity PA assessed by questionnaire (92% follow-up at 6 months and 75% at 12 months) was greater in the EI (p = 0.01; multivariate model, p = 0.001). For dietary outcomes, follow-up was 90% for questionnaire and 92% for serum carotenoids at 6 months and 74% for questionnaire at 12 months. Dietary intake improved more in the EI compared to the MI (questionnaire at 6 and 12 months, p < 0.001; serum carotenoid index, p = 0.05; multivariate model, p = 0.03). Conclusion The EI did not improve objectively measured PA, but was associated with improved self-reported and objective measures of dietary intake.

The ENHANCES study--Enhancing Head and Neck Cancer patients' Experiences of Survivorship: study protocol for a randomized controlled trial

Background Few cancers pose greater challenges than head and neck (H&N) cancer. Residual effects following treatment include body image changes, pain, fatigue and difficulties with appetite, swallowing and speech. Depression is a common comorbidity. There is limited evidence about ways to assist patients to achieve optimal adjustment after completion of treatment. In this study, we aim to examine the effectiveness and feasibility of a model of survivorship care to improve the quality of life of patients who have completed treatment for H&N cancer. Methods This is a preliminary study in which 120 patients will be recruited. A prospective randomised controlled trial of the H&N Cancer Survivor Self-management Care Plan (HNCP) involving pre- and post-intervention assessments will be used. Consecutive patients who have completed a defined treatment protocol for H&N cancer will be recruited from two large cancer services and randomly allocated to one of three study arms: (1) usual care, (2) information in the form of a written resource or (3) the HNCP delivered by an oncology nurse who has participated in manual-based training and skill development in patient self-management support. The trained nurses will meet patients in a face-to-face interview lasting up to 60 minutes to develop an individualised HNCP, based on principles of chronic disease self-management. Participants will be assessed at baseline, 3 and 6 months. The primary outcome measure is quality of life. The secondary outcome measures include mood, self-efficacy and health-care utilisation. The feasibility of implementing this intervention in routine clinical care will be assessed through semistructured interviews with participating nurses, managers and administrators. Interviews with patients who received the HNCP will explore their perceptions of the HNCP, including factors that assisted them in achieving behavioural change. Discussion In this study, we aim to improve the quality of life of a patient population with unique needs by means of a tailored self-management care plan developed upon completion of treatment. Delivery of the intervention by trained oncology nurses is likely to be acceptable to patients and, if successful, will be a model of care that can be implemented for diverse patient populations.