Does diaphragmatic breathing technique really improve its range of motion? An objective assessment

Purpose - To verify the results of a diaphragmatic breathing technique (DBT) on diaphragmatic range of motion in healthy subjects. Methods - A total of 51 healthy subjects (10 male; 41 female), mean age 20 years old and a body mass index (BMI) ranging from 15.6 to 34.9 kg/m2, were enrolled in this study. Diaphragmatic range of motion was assessed by M-mode ultrasound imaging. Measurements were made before and after the DBT implementation in a standard protocol, based on 3 seconds of inspiration starting from a maximum expiration. Differences between assessments were analyzed by descriptive statistics and t-test (p < 0.05). Results - Mean value range of motion before DBT was 55.3 ± 13.4 mm and after DBT was 63.8 ± 13.2 mm showing a significant improvement of 8.5 ± 14.7 mm (p < 0.001). A strong correlation between the slope and the range of motion was found (r = 0.71, p < 0.001). Conclusions - Based on ultrasound measurements, it has been proved that DBT really contributes to a higher diaphragmatic range of motion. Future studies are needed in order to understand the influence of protocol parameters (e.g. inspiration time). Clinical implications - In the contest of evidence-based practice in physiotherapy, it has been showed by objective measurements that DBT improves the diaphragm range of motion, translating into a more efficient ventilatory function and thus can be used in clinical setting. To our knowledge this is the first study to assess the effects of DBT on range of motion of diaphragm muscle with ultrasound imaging.

Entropy diversity in multi-objective particle swarm optimization

Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyzethe MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.

A multi-objective approach for the motion planning of redundant manipulators

Kinematic redundancy occurs when a manipulator possesses more degrees of freedom than those required to execute a given task. Several kinematic techniques for redundant manipulators control the gripper through the pseudo-inverse of the Jacobian, but lead to a kind of chaotic inner motion with unpredictable arm configurations. Such algorithms are not easy to adapt to optimization schemes and, moreover, often there are multiple optimization objectives that can conflict between them. Unlike single optimization, where one attempts to find the best solution, in multi-objective optimization there is no single solution that is optimum with respect to all indices. Therefore, trajectory planning of redundant robots remains an important area of research and more efficient optimization algorithms are needed. This paper presents a new technique to solve the inverse kinematics of redundant manipulators, using a multi-objective genetic algorithm. This scheme combines the closed-loop pseudo-inverse method with a multi-objective genetic algorithm to control the joint positions. Simulations for manipulators with three or four rotational joints, considering the optimization of two objectives in a workspace without and with obstacles are developed. The results reveal that it is possible to choose several solutions from the Pareto optimal front according to the importance of each individual objective.

Single-objective spreading algorithm

This paper addresses the problem of finding several different solutions with the same optimum performance in single objective real-world engineering problems. In this paper a parallel robot design is proposed. Thereby, this paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and ε-dominance to promote diversity over the admissible space. The performance of the proposed algorithm is analyzed with three well-known test functions and a function obtained from practical real-world engineering optimization problems. A spreading analysis is performed showing that the solutions drawn by the algorithm are well dispersed.

Logarithmically proportional objective function for planar surfaces recognition in 3D point cloud

3D laser scanning is becoming a standard technology to generate building models of a facility's as-is condition. Since most constructions are constructed upon planar surfaces, recognition of them paves the way for automation of generating building models. This paper introduces a new logarithmically proportional objective function that can be used in both heuristic and metaheuristic (MH) algorithms to discover planar surfaces in a point cloud without exploiting any prior knowledge about those surfaces. It can also adopt itself to the structural density of a scanned construction. In this paper, a metaheuristic method, genetic algorithm (GA), is used to test this introduced objective function on a synthetic point cloud. The results obtained show the proposed method is capable to find all plane configurations of planar surfaces (with a wide variety of sizes) in the point cloud with a minor distance to the actual configurations. © 2014 IEEE.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Trade-Off Performance Regions of Slotted ALOHA Protocol using Multi-Objective Optimization

10th Conference on Telecommunications (Conftele 2015), Aveiro, Portugal.

Multi-Objective and Financial Portfolio Optimization of Carrier-Sense Multiple Access Protocols with Cooperative Diversity

8th International Workshop on Multiple Access Communications (MACOM2015), Helsinki, Finland.

A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context

In the traditional paradigm, the large power plants supply the reactive power required at a transmission level and the capacitors and transformer tap changer were also used at a distribution level. However, in a near future will be necessary to schedule both active and reactive power at a distribution level, due to the high number of resources connected in distribution levels. This paper proposes a new multi-objective methodology to deal with the optimal resource scheduling considering the distributed generation, electric vehicles and capacitor banks for the joint active and reactive power scheduling. The proposed methodology considers the minimization of the cost (economic perspective) of all distributed resources, and the minimization of the voltage magnitude difference (technical perspective) in all buses. The Pareto front is determined and a fuzzy-based mechanism is applied to present the best compromise solution. The proposed methodology has been tested in the 33-bus distribution network. The case study shows the results of three different scenarios for the economic, technical, and multi-objective perspectives, and the results demonstrated the importance of incorporating the reactive scheduling in the distribution network using the multi-objective perspective to obtain the best compromise solution for the economic and technical perspectives.

A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers

In this paper, we formulate the electricity retailers’ short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers’ multi-objective problem.

Radiographic features of pulmonary tuberculosis in patients infected by HIV: is there an objective indicator of co-infection?

This study aimed to compare the radiographic characteristics of patients with pulmonary tuberculosis (TB) and human immunodeficiency virus (HIV) infection with those of HIV-negative patients. In all, 275 TB patients attending the outpatients clinics at the University Hospital/UFPE, were studied from January 1997 to March 1999. Thirty nine (14.2%) of them were HIV+, with a higher frequency of males in this group (p=0.044). Seventy-five percent of the HIV+ patients and 19% of the HIV- had a negative tuberculin test (PPD) (p < 0.001). The proportion of positive sputum smears in the two groups was similar. The radiological finding most strongly associated with co-infection was absence of cavitation (p < 0.001). It may therefore be concluded that the lack of cavitation in patients with pulmonary TB may be considered a useful indicator of the need to investigate HIV infection. This approach could contribute to increasing the effectiveness of local health services, by offering appropriate treatment to co-infected patients.

Subjective versus objective stress in noncritically ill hospitalized and outpatient adult men

A cross-sectional study of 120 subjects was performed with the purpose of evaluating stress hormones and emotional stress (anxiety) in outpatient and hospitalized subjects. The aims were to determine the degree of objective stress, as well as to correlate this finding with subjective findings, estimated using Beck's Anxiety Inventory.. METHOD: Three populations were investigated, namely outpatient clinical cases (Group I, n = 30), hospitalized clinical individuals (Group II, n = 30), and hospitalized surgical candidates (Group III, n = 30). Controls (Group IV, n = 30) were healthy volunteers who were health-care professionals and students. To avoid hormone interactions, only men were enrolled in all groups. All hospitalized subjects were tested on admission and before therapeutic interventions. Fasting epinephrine, norepinephrine, and cortisol were measured in the morning, and Beck's Anxiety Inventory was adminstered by a trained psychologist. RESULTS: The 3 patient groups displayed higher anxiety levels than the controls. Hormone concentrations did not present remarkable changes and did not correlate with subjective stress (anxiety). CONCLUSIONS: 1) Subjective disorders (as determined with Beck's Anxiety Inventory ) were a common finding in both outpatient and hospitalized populations, without differences between the various groups; 2) Objective stress (as determined by elevated hormone levels) was more difficult to confirm-findings rarely exceeded the reference range; 3) Correlation between the two variables could not be demonstrated; 4) Further studies are necessary to define stress quantification and interpretation in patient populations, especially in relationship with nutritional diagnosis and dietetic prescription.

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.