Relaxation Strategies Research Articles

A Narrative Review: Transforming Pediatric Oncology Care Through Virtual Reality - Pain Management and Enhanced Patient Experience

Virtual reality (VR) technology has received considerable interest in the healthcare field, particularly in pediatric oncology. Thepurpose of this study was to examine the existing and future applications of VR in reducing the discomfort associated withpediatric oncology procedures such as bone marrow biopsy, bone marrow aspirate, and lumbar puncture. A comprehensive search was conducted across numerous databases from 2005 to 2023, embracing several study designs to provide a strong evidence foundation. Using diversion and relaxation strategies, VR can help pediatric cancer patients cope with the emotional issues they confront during operations. VR has shown significant potential for lowering pain and anxiety during several pediatric medical procedures. Patient feedback focused on VR’s empowering and anxiety-reducing benefits, while healthcare staff reported increased patient participation and procedural efficiency. VR implementation challenges include the cost of specialized technology, the need for specific virtual settings, and the need for training healthcare workers. VR shows promise in improving the pediatric cancer experience, but more study and cooperation are required to realize its full potential.

Students’ Strategies to Overcome Public Speaking Anxiety

Public speaking anxiety (PSA) is a problem that often occurs in the language learning process therefore this study was conducted to find the strategies students use to overcome the public speaking anxiety they experience using the theory developed by Kondo and Ying Ling (2004). This study used a quantitative method with a descriptive approach. The participants of this research were 40 students of the English education study program of FKIP at Universitas Tanjungpura. The results showed that relaxation strategy is the most chosen strategy by students as the strategy they use to overcome their public speaking anxiety with the percentage 83.2%, followed by the preparation strategy as the second strategy with the percentage 80.1%, then third is peer seeking strategy chosen by 80% students and the last is the positive thinking strategy as much as 78.2%. The limitations of this study are as follows: it does not provide data on students' levels of anxiety, lacks diversity in the tools used, focuses solely on students who have completed the course, and does not explore strategies beyond those suggested in the questionnaire.

Gradient-based wind farm layout optimization with inclusion and exclusion zones

Abstract. Wind farm layout optimization is usually subjected to boundary constraints of irregular shapes. The analytical expressions of these shapes are rarely available, and, consequently, it can be challenging to include them in the mathematical formulation of the problem. This paper presents a new methodology to integrate multiple disconnected and irregular domain boundaries in wind farm layout optimization problems. The method relies on the analytical gradients of the distances between wind turbine locations and boundaries, which are represented by polygons. This parameterized representation of boundary locations allows for a continuous optimization formulation. A limitation of the method, if combined with gradient-based solvers, is that wind turbines are placed within the nearest polygons when the optimization is started in order to satisfy the boundary constraints; thus the allocation of wind turbines per polygon is highly dependent on the initial guess. To overcome this and improve the quality of the solutions, two independent strategies are proposed. A case study is presented to demonstrate the applicability of the method and the proposed strategies. In this study, a wind farm layout is optimized in order to maximize the annual energy production (AEP) in a non-uniform wind resource site. The problem is constrained by the minimum distance between wind turbines and five irregular polygon boundaries, defined as inclusion zones. Initial guesses are used to instantiate the optimization problem, which is solved following three independent approaches: (1) a baseline approach that uses a gradient-based solver; (2) approach 1 combined with the relaxation of the boundaries, which allows for a better design space exploration; and (3) the application of a heuristic algorithm, “smart-start”, prior to the gradient-based optimization, improving the allocation of wind turbines within the inclusion polygons based on the potential wind resource and the available area. The results show that the relaxation of boundaries combined with a gradient-based solver achieves on average +10.2 % of AEP over the baseline, whilst the smart-start algorithm, combined with a gradient-based solver, finds on average +20.5 % of AEP with respect to the baseline and +9.4 % of AEP with respect to the relaxation strategy.

Decremental State-Space Relaxations for the Basic Traveling Salesman Problem with a Drone

Truck-and-drone routing problems have become an important research topic in the last decade because of their applications for last-mile deliveries. Despite the many publications in this area, the most efficient exact algorithms designed thus far struggle to solve the benchmark instances with 39 or more customers. This fact holds even for one of the simplest variants involving one truck and one drone whose routes must synchronize at customers’ locations: the basic traveling salesman problem with a drone (TSP-D). In this article, we devise a new algorithm for the TSP-D that solves every benchmark instance with up to 59 customers, and it scales up to 99 customers when the drone is much faster than the truck. The core of our method is a dynamic programming algorithm that is applied for column generation and variable fixing within tailored decremental state-space relaxation strategies. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by Fondo para la Investigación Científica y Tecnológica [Grant PICT-2018-2961] (Ministry of Science, Argentina). Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0390 .

Recovery management in sport: Overview and outcomes of a nine-year multicenter research program

The nine-year multicenter research project, titled REGman—Optimization of Training and Competition: Management of Regeneration in Elite Sports—aimed to advance the understanding of recovery management in sports and generate practical recommendations. In this article, we outline the overarching research program that links the specific research strands and outputs during the two federal funding cycles (2012–2021) and summarize the main project findings. The two-stage conceptual framework involved investigating monitoring measures for short-term fatigue and recovery, as well as assessing the efficacy of various recovery interventions. These interventions encompassed psychological relaxation and recovery strategies, sleep, cooling techniques like precooling, percooling, cold water immersion, or whole-body cryotherapy, compression garments, active recovery, stretching, heating interventions such as sauna or contrast water therapy, massage, and foam rolling. The findings revealed inconclusive or marginal effects of recovery interventions at the group level, while indicating possible interindividual differences in responses. Additionally, the findings highlighted the effectiveness of diverse monitoring measures, showing satisfactory sensitivity in tracking performance changes related to fatigue and recovery. The use of individualized reference ranges significantly improved classification accuracy compared to group-based reference ranges. Athletes and coaches are encouraged to prioritize fundamental aspects of training and recovery: meticulous training planning and execution, effective sleep management, and proper nutrition. Furthermore, monitoring and analyzing individual responses, even though it demands suitable methodologies and presents challenges in high-performance sports environments, can yield valuable insights for personalized recovery management. If these aspects are comprehensively addressed, and resources allow, additional recovery strategies might be explored.

Linear energy-stable method with correction technique for the Ohta–Kawasaki–Navier–Stokes model of incompressible diblock copolymer melt

An efficiently linear time-marching method is proposed for the incompressible fluid flows coupled Ohta–Kawasaki model of diblock copolymer melt. Although this model satisfies the energy dissipation law under appropriate boundary conditions, the existences of nonlinear terms and coupling terms lead to difficulties of designing energy-stable numerical algorithms. To overcome the difficulties resulting from nonlinear and coupling terms, we herein construct a numerical method based on a highly efficient variant of scalar auxiliary variable approach. The stabilization technique is introduced to maintain the stability with large time steps. To improve the consistency, a simple and practical relaxation strategy is used to correct the difference between time-discretized modified energy and original energy. In each time step, all variables are totally decoupled and we only need to solve linear elliptic equations in a step-by-step manner. The unique solvability and energy stability are analytically proved. Extensive numerical experiments indicate that the proposed method not only achieves desired accuracy, energy stability, and consistency, but also has good capabilities on simulating various hydrodynamically coupled pattern formations.

A Successive Gap Constraint Linearization Method for Optimal Control Problems with Equilibrium Constraints

In this study, we propose a novel gap-constraint-based reformulation for optimal control problems with equilibrium constraints (OCPECs). We show that the proposed reformulation generates a new constraint system equivalent to the original one but more concise and with favorable differentiability. Moreover, constraint regularity can be recovered by a relaxation strategy. We show that the gap constraint and its gradient can be evaluated efficiently. We then propose a successive gap constraint linearization method to solve the discretized OCPEC. We also provide an intuitive geometric interpretation of the gap constraint. Numerical experiments validate the effectiveness of the proposed reformulation and solution method.

A nonlinear relaxation-strategy-based algorithm for solving sum-of-linear-ratios problems

<p>This paper mainly studies the sum-of-linear-ratios problems, which have important applications in finance, economy and computational vision. In this process, we first propose a new method to re-represent the original problem as an equivalent problem (EP). Secondly, by relaxing these constraints, a nonlinear relaxation subproblem is constructed for EP. In view of the special structure of the relaxation, it is reconstructed as a second-order cone programming (SOCP) problem, which is essentially a SOCP relaxation of EP. Thirdly, through the structural characteristics of the objective function of EP, a region reduction technique is designed to accelerate the termination of the algorithm as much as possible. By integrating the SOCP relaxation and acceleration strategy into the branch and bound framework, a new global optimization algorithm is developed. Further, the theoretical convergence and computational complexity of the algorithm are analyzed. Numerical experiment results reveal that the algorithm is effective and feasible.</p>

Flexible Label-Induced Manifold Broad Learning System for Multiclass Recognition.

Broad learning system (BLS), which emerges as a lightweight network paradigm, has recently attracted great attention for recognition problems due to its good balance between efficiency and accuracy. However, the supervision mechanism in BLS and its variants generally relies on the strict binary label matrix, which imposes limitations on approximation and fails to adequately align with the data distribution. To address this issue, in this article, two novel flexible label-induced BLS models with the manifold manner are proposed, whose notable characteristics are as follows. First, two proposed label relaxation strategies can both enlarge the margins between different categories and simultaneously enhance the diversity within labels. Second, the integration of manifold geometrical criterion enables the models to capture local feature structures, ensuring the obtained flexible labels align better with the similarity between samples. Third, the proposed models can be optimized efficiently with the alternating direction method of multipliers. Each iteration benefits from a closed-form solution, facilitating the optimization process. Extensive experiments and thorough theoretical analysis are intended to show the advantages of our proposed models compared to other state-of-the-art recognition algorithms.

A Preconditioned Landweber iteration-based Bundle adjustment for large-scale 3D reconstruction

Bundle Adjustment (BA) plays a crucial role in 3D reconstruction methods and Structure from Motion (SFM) algorithms, as it involves nonlinear minimization of reprojection error. The efficiency and accuracy of BA solutions are of paramount importance for achieving high-quality 3D reconstructions. The solution of the normal equations derived from nonlinear minimization is typically the most time-consuming step in BA and directly impacts the reconstruction accuracy. In this paper, we propose a Preconditioned Landweber Iteration solver for the normal equations. This solver utilizes a preconditioner and a corresponding relaxation strategy to improve the conditioning of the normal equations, leading to improved convergence speed and accuracy of the iteration. Compared to traditional direct and iterative algorithms, our approach significantly enhances the accuracy and solution time of BA. Experimental results demonstrate that our proposed solver outperforms the Schur complement solver in terms of both speed and accuracy when solving the normal equations. This improvement contributes to further acceleration and accuracy in large-scale 3D reconstruction, particularly for challenging large-scale intensive scenarios.

Optimizing energy storage performance of 0.9(Na0.5Bi0.5)(Fe0.02Ti0.98)O3-0.1SrTiO3 flexible capacitor via relaxation strategy

Flexible thin film capacitors have gained attention due to the trend of miniaturization, portability, and integration in electronic devices, especially in wearable technology. The Pt(111)/Ti/F-mica substrates with varying flexibility (S400, S500, S600, and S700) are fabricated under different sputtering temperatures (400 °C, 500 °C, 600 °C, and 700 °C) using magnetron sputtering. The flexible thin film capacitor, composed of 0.9(Na0.5Bi0.5)(Fe0.02Ti0.98)O3-0.1SrTiO3 (NBSFT), grown on the S600 substrate, achieved remarkable performance with a Wrec of 34.05 J/cm³ and η of 72.74% at 2434.64 kV/cm. This superior performance is due to enhanced wettability, enabling efficient ion migration and increased ion disorder during annealing. Disruption of long-range ordered domains decreased the activation energy of PNRs and enhanced relaxation. Additionally, this flexible capacitor demonstrated exceptional bending resistance, thermal stability (>200 °C), stable frequency response (1.0∼20.0 kHz), and reliable charge-discharge cycles (1 × 108 cycles) with ultrafast rates (τ0.9 = 12.6 μs). These findings highlight the potential of NBSFT flexible thin film capacitors on Pt(111)/Ti/F-mica substrates for energy storage applications.

Variance optimality in constrained and unconstrained stochastic differential games

The purpose of this paper is to extend the variance optimality criterion to the settings of constrained and unconstrained two-person stochastic differential games. We give conditions that ensure the existence of Nash equilibria with minimal variance. This criterion is a complement to that of the average long-run expected reward of an ergodic Markov process. Our main contribution is that we give sufficient conditions to define and ensure the existence of relaxed strategies that optimize the limiting variance of a constrained performance index of each player in the continuous-time framework. To the best of our knowledge, this theoretical task has been approached only for unconstrained Markov decision problems in a discrete-time context. The applications of our research range from predator–prey systems to actuarial paradigms and cell growth modeling in patients diagnosed with cancer.

A class of unconditionally energy stable relaxation schemes for gradient flows

In this paper, a novel class of unconditionally energy stable schemes are constructed for solving gradient flow models by combining the relaxed scalar auxiliary variable (SAV) approach with the linear multistep technique. The main idea is first to rewrite the original model as an equivalent system, and then the linear multistep method in time, together with the relaxation strategy is employed to compute the solution of the reformulated system. The main benefit of the proposed schemes is that it can achieve second-order temporal accuracy and strictly unconditional energy stability, as well as a quite close to, and in many cases equal to, the original energy than the baseline SAV approaches. Finally, ample numerical experiments for the Allen–Cahn equation and Cahn–Hilliard equation are presented to confirm the efficiency, accuracy and strictly unconditional energy stability of the proposed schemes.

Robust tube-based NMPC for dynamic systems with discrete degrees of freedom

We present a robust nonlinear model predictive control (NMPC) algorithm for dynamic systems with mixed degrees of freedom, i.e., continuous and discrete manipulated variables. The discrete variables are often excluded from the NMPC layer and determined at a supervisory layer, which typically leads to suboptimal solutions. In contrast, the proposed controller optimizes both classes of degrees of freedom within the NMPC layer, thereby enhancing the closed-loop performance. Our approach relies on a computationally efficient relaxation and integrality restoration strategy. Initially, a nominal controller computes relaxed central reference trajectories for the states and the input variables. These trajectories are then tracked by an ancillary NMPC controller before the integrality of the discrete variables is restored. The role of the ancillary controller is to ensure robustness against the integer restoration error as well as other disturbances based on a nonlinear Tube MPC approach. Furthermore, we provide sufficient conditions to establish recursive feasibility and guarantee robust closed-loop stability. Finally, the effectiveness of the approach is demonstrated through two nonlinear simulation examples, affirming its viability and robustness.

The Block Landweber Iterative Method for Light Field Reconstruction from a Focal Stack

Light field imaging involves reconstructing a 4D light field from a 3D focal stack, which makes it challenging to reconstruct the light field from incomplete projection data. To address this problem, a linear projection system is established to model the focal stack imaging process using discrete refocusing equations. Based on this system, we propose the block Landweber iterative method to find the least-squares solution. This method computes the sparse matrix while iterating, which overcomes the problem of data storage. The 2-norm of the block matrix is utilized as the weighted matrix to normalize every block matrix on an identical scale, delivering an effective relaxation strategy under the convergence condition in the inconsistent case, which yields better reconstruction results and accelerates the convergence speed. The experimental results based on the image quality assessments of reference and non-reference images show that our method achieved better reconstruction results compared to other relevant common methods, even with fewer focal stacks and higher angle resolution.

Insights into varicella-zoster virus assembly from the B- and C-capsid at near-atomic resolution structures

Varicella-zoster is a highly communicable virus that can be transmitted through the airborne route. About one quarter of people are infected with this virus. Previous studies have described the structure of A-capsid and a blurred reconstruction of the C-capsid with icosahedral symmetry. In this study, we have determined the more precise detailed structures of the varicella-zoster virus (VZV) B- and C-capsid in icosahedral symmetry using a combination of block-based reconstruction and symmetry relaxation strategies. In addition, we are reporting structural details of the portal vertex reconstructions in five-fold symmetry and portal reconstructions in twelve-fold symmetry. The structures unveil the basis for the high thermal stability of the VZV capsid. The conformational flexibility of structural elements of the capsid plays a role in the assembly of the capsid and drives processes critical for the viral life cycle. The results of the study open up new avenues for the development of drugs against a highly prevalent and contagious pathogen.

Relaxation strategy for the block Landweber method

The block Landweber iteration is a general method for the solution of linear systems which is widely applied for image reconstructions. The convergence behavior of the block Landweber iteration is of both theoretical and practical importance. When the linear system is consistent, we derive a neat iterative representation and find an optimal relaxation coefficient by minimizing the 2-norm of the newly derived iteration matrix in the range of the conjugate transpose of the corresponding block matrix. We also establish the accelerating convergence relaxation strategies based on the maximum eigenvalue of each block matrix. When the linear system is inconsistent, we normalize each block matrix by the 2-norms of the each weighted block matrix at a uniform scale to give the relaxation strategies. The numerical simulations show that the relaxation strategies proposed for the block Landweber iteration in both consistent and inconsistent cases in this paper have the better reconstruction performances. Compared with the Landweber iteration, the block Landweber iteration has superiority in reconstruction results.

PSYCHOLOGICAL REASONS FOR GADGETS DEPENDENCE AND OVERCOMING STRATEGIES

This study was conducted at the request of educational psychologists from educational organizations and was devoted to study the psychological reasons for schoolchildren’s dependence on gadgets. The study identified factors that precede students’ gadget addictions, and then analyzed overcoming psychological mechanisms. Active methods of the psychological service system of secondary schools were classified into three groups: strategies for relaxation, regulation and strengthening of the nervous system of students (rebirthing and meditation); strategies for mobilizing children to activity through play therapy, art therapy and neurographic algorithms; strategies that promote the development of awareness and responsibility (coaching technologies, psychoanalytic techniques, genesis therapy). The results of the study and recommendations for overcoming gadget addiction among students of educational organizations were tested at a scientific and practical conference among educational psychologists of the Zhambyl region and the city of Taraz and also discussed on social networks Instagram, Facebook.

Uso de la realidad virtual inmersiva como método de relajación en el entorno de una unidad de cuidados intensivos

IntroductionThe usage of immersive virtual reality (iVR) in the context of an intensive care unit (ICU) is scarce. Our objective was to assess the feasibility of the usage of iVR in critical patients with or without mechanical ventilation (MV) and to determine the anxiety degree before and after each session. MethodsAnalytical, descriptive, prospective, and cross-sectional research. Pilot test with 20 patients from a polyvalent ICU of a tertiary hospital. Adult patients were included, either connected or not to MV, watchful and calmed (RASS −1/+1) and without delirium (negative CAM-ICU).Oculus Go (Facebook Technologies, LLC) iVR glasses were the model used. The relaxation strategy consisted in the visualization of an experience of 15minutes with scenes related to nature and fantasy, relaxing music with a plot. The sessions were individual, with the patient monitored in a fowler position or seated. The anxiety degree before and after each session was evaluated following a reduced version of the Spanish “Cuestionario de Ansiedad Estado-Rasgo (STAI-e)” and they were analysed using T samples coupled (statistical significance when p-value was <.05). ResultsIncorporation of 20 patients with an average age of 63.9 years old (60% men). A total of 34 sessions of iVR were conducted. The 32% patients mechanically ventilated, 32% high-flow oxygen therapy, 36% other breathing supports. 80% of the sessions were completed without serious side effects. A significant decrease in the anxiety degree was observed after each iVR session: first session mean change −2.68 (SD=2.75), P=.000; second session mean change −1.86 (SD=1.57), P=.021; third session mean change −1.67 (SD=1.63), P=.054. ConclusionThe usage of iVR in the context of an ICU is feasible, even with patients mechanically ventilated. iVR reduces the anxiety degree in the critic patient, which suggests that “digital therapies” can be effective to improve the emotional state during their stay in the ICU.

Gradient-Based Neural Architecture Search: A Comprehensive Evaluation

One of the challenges in deep learning involves discovering the optimal architecture for a specific task. This is effectively tackled through Neural Architecture Search (NAS). Neural Architecture Search encompasses three prominent approaches—reinforcement learning, evolutionary algorithms, and gradient descent—that have demonstrated noteworthy potential in identifying good candidate architectures. However, approaches based on reinforcement learning and evolutionary algorithms often necessitate extensive computational resources, requiring hundreds of GPU days or more. Therefore, we confine this work to a gradient-based approach due to its lower computational resource demands. Our objective encompasses identifying the optimal gradient-based NAS method and pinpointing opportunities for future enhancements. To achieve this, a comprehensive evaluation of the use of four major Gradient descent-based architecture search methods for discovering the best neural architecture for image classification tasks is provided. An overview of these gradient-based methods, i.e., DARTS, PDARTS, Fair DARTS and Att-DARTS, is presented. A theoretical comparison, based on search spaces, continuous relaxation strategy and bi-level optimization, for deriving the best neural architecture is then provided. The strong and weak features of these methods are also listed. Experimental results for comparing the error rate and computational cost of these gradient-based methods are analyzed. These experiments involved using bench marking datasets CIFAR-10, CIFAR-100 and ImageNet. The results show that PDARTS is better and faster among the examined methods, making it a potent candidate for automating Neural Architecture Search. By effectively conducting a comparative analysis, our research provides valuable insights and future research directions to address the criticism and gaps in the literature.