The immediate effects of atlanto-occipital joint manipulation and suboccipital muscle inhibition technique on active mouth opening and pressure pain sensitivity over latent myofascial trigger points in the masticatory muscles

DESIGN: A randomized controlled trial.OB JECTIVE: To investigate the immediate effects on pressure pain thresholds over latent trigger points (TrPs) in the masseter and temporalis muscles and active mouth opening following atlanto-occipital joint thrust manipulation or a soft tissue manual intervention targeted to the suboccipital muscles. BACKGROUND : Previous studies have described hypoalgesic effects of neck manipulative interventions over TrPs in the cervical musculature. There is a lack of studies analyzing these mechanisms over TrPs of muscles innervated by the trigeminal nerve. METHODS: One hundred twenty-two volunteers, 31 men and 91 women, between the ages of 18 and 30 years, with latent TrPs in the masseter muscle, were randomly divided into 3 groups: a manipulative group who received an atlanto-occipital joint thrust, a soft tissue group who received an inhibition technique over the suboccipital muscles, and a control group who did not receive an intervention. Pressure pain thresholds over latent TrPs in the masseter and temporalis muscles, and active mouth opening were assessed pretreatment and 2 minutes posttreatment by a blinded assessor. Mixed-model analyses of variance (ANOVA) were used to examine the effects of interventions on each outcome, with group as the between-subjects variable and time as the within-subjects variable. The primary analysis was the group-by-time interaction. RESULTS: The 2-by-3 mixed-model ANOVA revealed a significant group-by-time interaction for changes in pressure pain thresholds over masseter (P<.01) and temporalis (P =.003) muscle latent TrPs and also for active mouth opening (P<.001) in favor of the manipulative and soft tissue groups. Between-group effect sizes were small. CONCLUSIONS: The application of an atlanto-occipital thrust manipulation or soft tissue technique targeted to the suboccipital muscles led to an immediate increase in pressure pain thresholds over latent TrPs in the masseter and temporalis muscles and an increase in maximum active mouth opening. Nevertheless, the effects of both interventions were small and future studies are required to elucidate the clinical relevance of these changes. LEVEL OF EVIDENCE : Therapy, level 1b. J Orthop Sports Phys Ther 2010;40(5):310-317. doi:10.2519/jospt.2010.3257. KEYWORDSDS: cervical manipulation, muscle trigger points, neck, TMJ, upper cervical.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Application-specific modified particle swarm optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding he management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Electrochemical sensor for simultaneous determination of herbicide MCPA and its metabolite 4-chloro-2-methylphenol. Application to photodegradation environmental monitoring

The development and application of a polyaniline/carbon nanotube (CNT) cyclodextrin matrix (PANI-β-CD/MWCNT)-based electrochemical sensor for the quantitative determination of the herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) and its main transformation product 4-chloro-2-methylphenol in natural waters are described. A simple cyclic voltammetry-based electrochemical methodology, in phosphate buffer solution at pH 6.0, was used to develop a method to determine both MCPA and 4-chloro-2-methylphenol, without any previous extraction or derivatization steps. A linear concentration range (10 to 50 μmol L−1) and detection limits of 1.1 and 1.9 μmol L−1, respectively, were achieved using optimized cyclic voltammetric parameters. The proposed method was successfully applied to the determination of MCPA and 4-chloro-2-methylphenol in natural water samples with satisfactory recoveries (94 to 107 %) and in good agreement with the results obtained by an established high-performance liquid chromatography technique, no significant differences being found between the methods. Interferences from ionic species and other herbicides used for broad-leaf weed control were shown to be small. The newly developed methodology was also successfully applied to MCPA photodegradation environmental studies.