Relaxation Strategies Research Articles

Mental health of undergraduate students and its influencing factors

Two hundred undergraduate students between the age group of 18-23 yrs were selected from randomly chosen five colleges of Parbhani town of Marathwada region, Maharashtra State between the age group of 18-23 yrs by simple random sampling method with an intention to have the youth of diverse background in the sample. The data pertaining to the study were collected by personally interviewing the sample undergraduate students based on open ended interview schedule and by administering on them Mental Health Inventory, Self-Esteem Inventory and Socio-Economic Status Scale. No significant differences were recorded with respect to the mental health status of the students based on their socio-economic status and gender. With respect to the various dimensions of mental health of the sample students and its magnitude based on their SES, significantly a higher percentage of the male students in low SES families assessed to have good group oriented attitude than their counterparts female students, while it was vice-versa with regard to the poor environmental mastery. On the other hand, in middle SES group, significantly a higher percentage of the female students were assessed to have good integration of personality, fair positive self-evaluation and group oriented attitude than their counterparts male students. However, significantly a higher percentage of the male students were recorded to have good group oriented attitude and fair autonomy as compared to the female students. Hence, there is a great need to provide strong and positive supportive social network of family, peers, lecturers and neighbourers to youth for helping them to have good mental health in addition to training them on relaxation strategies and techniques for combating with stress to empower them so as to lead a quality life. How to cite this paper : Bangale, Jaya and Patnam, Vishala (2014). Mental health of undergraduate students and its influencing factors. Internat. J. Med. Sci., 7(1 & 2) : 38-42. See end of the paper for authors’ affiliation Correspondence to : JAYA BANGALE Department of Human Development and Family Studies, College of Home Science, Vasantrao Naik Marathwada Krishi Vidyapeeth, PRABHANI (M.S.) INDIA Email: bangalej@gmail.com RESEARCH PAPER International Journal of Medical Sciences Volume 7 | Issue 1&2 | October, 2014 | 38-42 e ISSN-0976-7932 | Visit us www.researchjournal.co.in DOI : 10.15740/HAS/IJMS/7.1and2/38-42 Click www.researchjournal.co.in/online/subdetail.html to purchase.

Yoga in Pediatrics

Yoga is a form of mind-body medicine that uses poses, breathing techniques, and relaxation strategies (Galantino, Galbavy, and Quinn, 2008). Yoga is becoming increasingly popular as a tool for heal...

What Strategies Do Patients With Irritable Bowel Syndrome Continue to Use Following a 9-Week Comprehensive Self-Management Program?

Introduction: The aims of this study were to identify strategies patients with irritable bowel syndrome (IBS) continued to use following a 9-week comprehensive self-management (CSM) program. Methods: This secondary analysis includes data collected at the last CSM session and 6 and 12 months post-randomization from adult with IBS randomized to receive CSM intervention. During the last CSM session, subjects identified CSM strategies they intended to continue using in each of the following categories: diet, relaxation, and alternative thinking. For each identified strategy, subjects were asked at the 6- and 12-month follow-up times to rate how often they used it on a 5-point scale (1 = not at all to 5 = almost always). Individual strategies were categorized into 9 themes: 1) meal timing/frequency, 2) trigger food reduction/elimination, 3) eating behaviors (e.g., avoid eating out, eat more slowly, etc.), 4) balanced diet (e.g., fiber, fluids, etc.), 5) specific relaxation strategies (e.g., abdominal breathing, body scan, etc.), 6) lifestyle behaviors (e.g., exercise, hobbies, etc.), 7) thought distortions (e.g., all or nothing thinking, overgeneralization of negative, etc.), 8) challenging beliefs (e.g., perfectionism, self-esteem, etc.) and 9) problem-solving skills (e.g., find support, refocus, etc.).Table 1Results: The majority of subjects were female (N=88, 87%) and the mean age was 44 years (SD=14). The first column of the table shows, for each theme, the percentage of subjects who reported at their last CSM session their intention to continue using strategies from that theme. The second column shows the percentage of subjects who reported at 12 months post-randomization that they have continued to use strategies from that theme at least 2 days a week. Conclusion: Our 9-week CSM program resulted in sustainable behavioral changes for IBS symptom management in the majority of our subjects. An advantage of the multi-component CSM intervention is that patients can determine which strategies are most effective at alleviating their IBS symptoms and continue to use them.

Robust hub network design problem

This paper presents a robust formulation for the uncapacitated single and multiple allocation hub location problem where demand is uncertain and its distribution is not fully specified. The proposed robust model is formulated as a mixed integer nonlinear program and then transformed into a mixed integer conic quadratic program. An efficient linear relaxation strategy is proposed which is found to deliver the optimal solutions for all the cases considered in this paper. Numerical experiments suggest location of more number of hubs when accounting for demand uncertainty using robust optimization compared to the deterministic setting.

A convex relaxation framework for a class of semi-supervised learning methods and its application in pattern recognition

Semi-supervised learning has been an attractive research tool for using unlabeled data in pattern recognition. Applying a novel semi-definite programming (SDP) relaxation strategy to a class of continuous semi-supervised support vector machines (S3VMs), a new convex relaxation framework for the S3VMs is proposed based on SDP. Compared with other SDP relaxations for S3VMs, the proposed methods only require solving the primal problems and can implement L1-norm regularization. Furthermore, the proposed technique is applied directly to recognize the purity of hybrid maize seeds using near-infrared spectral data, from which we find that the proposed method achieves equivalent performance to the exact solution algorithm for solving the S3VM in different spectral regions. Experiments on several benchmark data sets demonstrate that the proposed convex technique is competitive with other SDP relaxation methods for solving semi-supervised SVMs in generalization.

Dynamic ng-Path Relaxation for the Delivery Man Problem

The ng-path relaxation was introduced by Baldacci, Mingozzi, and Roberti [Baldacci R, Mingozzi A, Roberti R (2011) New route relaxation and pricing strategies for the vehicle routing problem. Oper. Res. 59(5):1269–1283] for computing tight lower bounds to vehicle routing problems by solving a relaxation of the set-partitioning formulation, where routes are not necessarily elementary and can contain predefined subtours. The strength of the achieved lower bounds depends on the subtours that routes can perform. In this paper, we introduce a new general bounding procedure called dynamic ng-path relaxation that enhances the one of Baldacci, Mingozzi, and Roberti (2011) by iteratively redefining the subtours that routes can perform. We apply the bounding procedure on the well-known delivery man problem, which is a generalization of the traveling salesman problem where costs for traversing arcs depend on their positions along the tour. The proposed bounding procedure is based on column generation and computes a sequence of nondecreasing lower bounds to the problem. The final lower bound is used to solve the problem to optimality with a simple dynamic programming recursion. An extensive computational analysis on benchmark instances from the TSPLIB shows that the new bounding procedure yields better lower bounds than those provided by the method of Baldacci, Mingozzi, and Roberti (2011). Furthermore, the proposed exact method outperforms other exact methods recently presented in the literature and is able to close five open instances with up to 150 vertices.

Using a sparse promoting method in linear programming approximations to schedule parallel jobs

SummaryIn this paper, we tackle the well‐known problem of scheduling a collection of parallel jobs on a set of processors either in a cluster or in a multiprocessor computer. For the makespan objective, that is, the completion time of the last job, this problem has been shown to be NP‐hard, and several heuristics have already been proposed to minimize the execution time. In this paper, we consider both rigid and moldable jobs. Our main contribution is the introduction of a new approach to the scheduling problem, based on the recent discoveries in the field of compressed sensing. In the proposed approach, all possible positions and shapes of the jobs are encoded into a matrix, and the scheduling is performed by selecting the best columns under natural constraints. Thus, the solution to the new scheduling formulation is naturally sparse, and we may use appropriate relaxations to achieve the optimization task in the quickest possible way. Among many possible relaxation strategies, we choose to minimize the ℓp‐quasi‐norm for p∈(0,1). Minimization of the ℓp‐quasi‐norm is implemented via a successive linear programming approximation heuristic. We propose several new algorithms based on this approach, and we assess their efficiency through simulations. The experiments show that the scheme outperforms the classic Largest Task First list based algorithm for scheduling small to medium instances but needs improvements to compete on larger numbers of jobs. Copyright © 2014 John Wiley & Sons, Ltd.

Multi-objective unsupervised feature selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis

The multi-objective evolutionary algorithm (MOEA) has shown remarkable capability of selecting feature subset. Most MOEAs use the cardinality of the feature subset as one of its objectives and adopt a strict Pareto dominance relationship to select individuals. However, these techniques limit available solutions and may omit several appropriate but dominated solutions. A multi-objective unsupervised feature selection algorithm (MOUFSA) is proposed to solve these issues. A new objective, which incorporates the correlation coefficient and cardinality of the feature subset, not only evaluates the redundancy of selected features but also provides several objective values for each particular size of feature subset. A relaxed archiving strategy based on negative epsilon-dominance and the box-based method is designed to preserve promising solutions even if they are dominated. Three new mutation operators of different abilities are also presented to enhance the algorithm. Nine UCI datasets and five fault recognition datasets are employed as test objects, and the obtained feature subsets are then used for subsequent classification and clustering. Experimental results show that MOUFSA outperforms several other multi-objective and traditional single-objective methods.

A Precision-Recall Criterion Based Consensus Model For Fusing Multiple Segmentations

This paper presents a general framework for seamlessly combining multiple low cost and inaccurate estimated segmentation maps (with an arbitrary number of regions) of the same scene to achieve a final improved segmentation. The proposed fusion model is derived from the well-known precision-recall criterion, specially dedicated to the specific clustering problem of any spatially indexed data and which is also efficient and widely used in the vision community for evaluating both a region-based segmentation and the quality of contours produced by this segmentation map compared to one or multiple ground-truth segmentations of the same image. The proposed combination framework is here specifically designed to be robust with respect to outlier segmentations (that appear to be inconsistent with the remainder of the segmentation ensemble) and includes an explicit internal regularization factor reflecting the inherent ill-posed nature of the segmentation problem. We propose also a hierarchical and efficient way to optimize the consensus energy function related to this fusion model that exploits a simple and deterministic iterative relaxation strategy combining the different segments or individual regions belonging to the segmentation ensemble in the final solution. The experimental results on the Berkeley database with manual ground truth segmentations show the effectiveness of our combination model.

Examining the Effectiveness of Psychological Strategies on Physiologic Markers: Evidence-Based Suggestions for Holistic Care of the Athlete

Current holistic rehabilitation blends both physical and psychological techniques. However, validation of the usefulness of psychological strategies is limited in the literature. To quantify the effects of psychological strategies on both physiologic (salivary cortisol) and subjective assessments of stress. Randomized controlled clinical trial. Laboratory. A total of 97 college-aged students (age = 20.65 ± 4.38 years), most with little to no experience with psychological strategies. A 15-minute script via an iPod led the participant through visual imagery (cognitive relaxation) or deep breathing exercises (somatic relaxation) cues. The control group listened to 15 minutes of ambient nature sounds. Two samples (pretest, posttest) of salivary cortisol were analyzed using an enzyme immunoassay kit; the average was used for statistical analysis. Descriptive statistics and correlations were conducted to examine group differences in time of day, salivary cortisol, sex, Stress-O-Meter values, and Perceived Stress Scale scores. Salivary cortisol levels were lower in the treatment group than the control group (F2,97 = 15.62, P < .001). Females had higher scores on both the pretest Stress-O-Meter (5.15 ± 1.796) and the Perceived Stress Scale (18.31 ± 5.833) than males (4.25 ± 1.741 and 15.272 ± 5.390, respectively). Both cognitive and somatic relaxation strategies reduced cortisol levels. Participants who received verbal guidance achieved a larger cortisol reduction. However, the change in cortisol level was uncorrelated with the change in report of acute stress; females reported higher levels of stress. Clinical implications include attention to sex when assessing stress and providing coping skills during the rehabilitation process.

Nonzero-sum stochastic differential games with additive structure and average payoffs

This paper deals with nonzero-sum stochastic differential games with an additive structure and long-run average payoffs. Our main objective is to give conditions for the existence of Nash equilibria in the set of relaxed stationary strategies. Such conditions also ensure the existence of a Nash equilibrium within the set of stationary Markov (deterministic) strategies, and that the values of the average payoffs for these equilibria coincide almost everywhere with respect to Lebesgue's measure. This fact generalizes the results in the controlled (single player game) case found by Raghavan [47] and Rosenblueth [48]. We use relaxation theory and standard dynamic programming techniques to achieve our goals. We illustrate our results with an example motivated by a manufacturing system.

Fast Partitioning of Vector-Valued Images

We propose a fast splitting approach to the classical variational formulation of the image partitioning problem, which is frequently referred to as the Potts or piecewise constant Mumford--Shah model. For vector-valued images, our approach is significantly faster than the methods based on graph cuts and convex relaxations of the Potts model which are presently the state-of-the-art. The computational costs of our algorithm only grow linearly with the dimension of the data space which contrasts the exponential growth of the state-of-the-art methods. This allows us to process images with high-dimensional codomains such as multispectral images. Our approach produces results of a quality comparable with that of graph cuts and the convex relaxation strategies, and we do not need an a priori discretization of the label space. Furthermore, the number of partitions has almost no influence on the computational costs, which makes our algorithm also suitable for the reconstruction of piecewise constant (color or vect...

Effects of a brief cognitive defusion task on chocolate craving

This study investigated the effects of a brief mindfulness-based cognitive defusion task on chocolate craving. Sixty chocolate cravers were assigned to one of three conditions: cognitive defusion, relaxation control, or no-task control. All participants underwent a craving induction before being asked to sit in front of five chocolate products for 10 min and let their mind wander. Those in the defusion group were also asked to use a defusion strategy (see thoughts as ‘just thoughts’) whenever they experienced a thought that they wanted to eat chocolate. Those in the relaxation group were also asked to use a relaxation strategy (tense and relax hand muscles) whenever they experienced a thought that they wanted to eat chocolate. Every 60 s an alarm sounded and participants told the experimenter what they were thinking at that moment. After the 10 min participants completed two measures assessing chocolate cravings during the previous 10 min (CEQ_Strength10m and CEQ_Frequency10m). Results showed no significant differences in chocolate cravings between the three groups (defusion, M = 6.87, SD = 1.62 and M = 6.26, SD = 1.69 respectively; relaxation, M = 6.39, SD = 1.91 and M = 5.85, SD = 1.88; no-task, M = 6.73, SD = 2.15, M = 5.81, SD = 2.24). Additionally, there were no significant differences in the number of chocolate-related thoughts experienced in the three groups (defusion, M = 5.30, SD = 2.23; relaxation, M = 4.33, SD = 2.55; no-task, M = 6.47, SD = 2.39). Thus we found no evidence for an effect of cognitive defusion on cravings.

On the Computational Studies of Deterministic Global Optimization of Head Dependent Short-Term Hydro Scheduling

This paper addresses the global optimization of the short term scheduling for hydroelectric power generation. A tailored deterministic global optimization approach, denominated sHBB, is developed and its performance is analyzed. This approach is applied to the optimization of a mixed integer nonlinear programming (MINLP) model for cascades of hydro plants, each one with multiple turbines, and characterized by a detailed representation of the net head of water, and a nonlinear hydropower generation function. A simplified model is also considered where only the linear coefficients of the forebay and tailrace polynomial functions are retained. For comparison purposes, four case studies are addressed with the proposed global optimization strategy and with a commercial solver for global optimization. The results show that the proposed approach is more efficient than the commercial solver in terms of finding a better solution with a smaller optimality gap, using less CPU time. The proposed method can also find alternative and potentially more profitable power production schedules. Significant insights were also obtained regarding the effectiveness of the proposed relaxation strategies.

Evolutionary Algorithms With Segment-Based Search for Multiobjective Optimization Problems

This paper proposes a variation operator, called segment-based search (SBS), to improve the performance of evolutionary algorithms on continuous multiobjective optimization problems. SBS divides the search space into many small segments according to the evolutionary information feedback from the set of current optimal solutions. Two operations, micro-jumping and macro-jumping, are implemented upon these segments in order to guide an efficient information exchange among "good" individuals. Moreover, the running of SBS is adaptive according to the current evolutionary status. SBS is activated only when the population evolves slowly, depending on general genetic operators (e.g., mutation and crossover). A comprehensive set of 36 test problems is employed for experimental verification. The influence of two algorithm settings (i.e., the dimensionality and boundary relaxation strategy) and two probability parameters in SBS (i.e., the SBS rate and micro-jumping proportion) are investigated in detail. Moreover, an empirical comparative study with three representative variation operators is carried out. Experimental results show that the incorporation of SBS into the optimization process can improve the performance of evolutionary algorithms for multiobjective optimization problems.

A Framework for Globally Optimizing Mixed-Integer Signomial Programs

Mixed-integer signomial optimization problems have broad applicability in engineering. Extending the Global Mixed-Integer Quadratic Optimizer, GloMIQO (Misener, Floudas in J. Glob. Optim., 2012. doi: 10.1007/s10898-012-9874-7 ), this manuscript documents a computational framework for deterministically addressing mixed-integer signomial optimization problems to ε-global optimality. This framework generalizes the GloMIQO strategies of (1) reformulating user input, (2) detecting special mathematical structure, and (3) globally optimizing the mixed-integer nonconvex program. Novel contributions of this paper include: flattening an expression tree towards term-based data structures; introducing additional nonconvex terms to interlink expressions; integrating a dynamic implementation of the reformulation-linearization technique into the branch-and-cut tree; designing term-based underestimators that specialize relaxation strategies according to variable bounds in the current tree node. Computational results are presented along with comparison of the computational framework to several state-of-the-art solvers.

Analyse des cas cliniques du JTCC : entre clinique pure et recherche clinique

Resume Depuis sa creation en 1991, le Journal de therapie comportementale et cognitive (JTCC) recoit regulierement des cas cliniques dont l’interet est d’apporter le regard du clinicien au-dela de celui du chercheur meme s’ils sont parfois confondus. Cet article est une synthese de l’ensemble des cas cliniques publies dans le JTCC entre 1991 et 2012 qui concerne 54 documents. Chaque article est etudie sous differents angles : le ou les auteurs, population concernee (enfant, adulte, dyade), pathologies concernees ou situation probleme, referencement a des classifications nosographiques, type d’echelles psychometriques utilisees, presence ou non d’une analyse fonctionnelle et son modele de reference, type de therapie mise en place, evolution du trouble, reevaluations clinique et psychometrique et nombre de references bibliographiques. Il ressort de ces lectures que les presentations sont tres diversifiees tant sur les pathologies concernees que sur le recours aux tres nombreuses echelles de psychometrie ou encore sur les analyses fonctionnelles ou pour finir sur le recours aux nombreuses composantes des therapies comportementales, cognitives et emotionnelles. Cet article amene a poser la question sur l’interet de presenter des cas cliniques, de partager des experiences cliniques et de recherche, mais aussi de preconiser l’etablissement d’un guide referencant l’ensemble des pratiques afin qu’un langage commun soit utilise. Il souligne aussi l’interet des cas cliniques, comme complements indispensables a la recherche.

Accelerated single-beam wavefront reconstruction techniques based on relaxation and multiresolution strategies

A previous Letter by Pedrini et al. [Opt. Lett. 30, 833 (2005)] proposed an iterative single-beam wavefront reconstruction algorithm that uses a sequence of interferograms recorded at different planes. In this Letter, the use of relaxation and multiresolution strategies is investigated in terms of accuracy and computational effort. It is shown that the convergence rate of the conventional iterative algorithm can be significantly improved with the use of relaxation techniques combined with a hierarchy of downsampled intensities that are used within a preconditioner. These techniques prove to be more robust, to achieve a higher accuracy, and to overcome the stagnation problem met in the iterative wavefront reconstruction.

A robustness study of parametric and non-parametric tests in model-based multifactor dimensionality reduction for epistasis detection

BackgroundApplying a statistical method implies identifying underlying (model) assumptions and checking their validity in the particular context. One of these contexts is association modeling for epistasis detection. Here, depending on the technique used, violation of model assumptions may result in increased type I error, power loss, or biased parameter estimates. Remedial measures for violated underlying conditions or assumptions include data transformation or selecting a more relaxed modeling or testing strategy. Model-Based Multifactor Dimensionality Reduction (MB-MDR) for epistasis detection relies on association testing between a trait and a factor consisting of multilocus genotype information. For quantitative traits, the framework is essentially Analysis of Variance (ANOVA) that decomposes the variability in the trait amongst the different factors. In this study, we assess through simulations, the cumulative effect of deviations from normality and homoscedasticity on the overall performance of quantitative Model-Based Multifactor Dimensionality Reduction (MB-MDR) to detect 2-locus epistasis signals in the absence of main effects.MethodologyOur simulation study focuses on pure epistasis models with varying degrees of genetic influence on a quantitative trait. Conditional on a multilocus genotype, we consider quantitative trait distributions that are normal, chi-square or Student’s t with constant or non-constant phenotypic variances. All data are analyzed with MB-MDR using the built-in Student’s t-test for association, as well as a novel MB-MDR implementation based on Welch’s t-test. Traits are either left untransformed or are transformed into new traits via logarithmic, standardization or rank-based transformations, prior to MB-MDR modeling.ResultsOur simulation results show that MB-MDR controls type I error and false positive rates irrespective of the association test considered. Empirically-based MB-MDR power estimates for MB-MDR with Welch’s t-tests are generally lower than those for MB-MDR with Student’s t-tests. Trait transformations involving ranks tend to lead to increased power compared to the other considered data transformations.ConclusionsWhen performing MB-MDR screening for gene-gene interactions with quantitative traits, we recommend to first rank-transform traits to normality and then to apply MB-MDR modeling with Student’s t-tests as internal tests for association.

Cognitive-Behavioral–Based Physical Therapy to Improve Surgical Spine Outcomes: A Case Series

Fear of movement is a risk factor for poor postoperative outcomes in patients following spine surgery. The purposes of this case series were: (1) to describe the effects of a cognitive-behavioral-based physical therapy (CBPT) intervention in patients with high fear of movement following lumbar spine surgery and (2) to assess the feasibility of physical therapists delivering cognitive-behavioral techniques over the telephone. Eight patients who underwent surgery for a lumbar degenerative condition completed the 6-session CBPT intervention. The intervention included empirically supported behavioral self-management, problem solving, and cognitive restructuring and relaxation strategies and was conducted in person and then weekly over the phone. Patient-reported outcomes of pain and disability were assessed at baseline (6 weeks after surgery), postintervention (3 months after surgery), and at follow-up (6 months after surgery). Performance-based outcomes were tested at baseline and postintervention. The outcome measures were the Brief Pain Inventory, Oswestry Disability Index, 5-Chair Stand Test, and 10-Meter Walk Test. Seven of the patients demonstrated a clinically significant reduction in pain, and all 8 of the patients had a clinically significant reduction in disability at 6-month follow-up. Improvement on the performance-based tests also was noted postintervention, with 5 patients demonstrating clinically meaningful change on the 10-Meter Walk Test. The findings suggest that physical therapists can feasibly implement cognitive-behavioral skills over the telephone and may positively affect outcomes after spine surgery. However, a randomized clinical trial is needed to confirm the results of this case series and the efficacy of the CBPT intervention. Clinical implications include broadening the availability of well-accepted cognitive-behavioral strategies by expanding implementation to physical therapists and through a telephone delivery model.