Relaxation Strategies Research Articles

Usage of immersive virtual reality as a relaxation method in an intensive care unit

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 15 min 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 <0.05). ResultsIncorporation of 20 patients with an average age of 63.9 years old (60% men). A total of 34 sessions of iVR were conducted. 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 = 0.000; second session mean change −1.86 (SD = 1.57), p = 0.021; third session mean change −1.67 (SD = 1.63), p = 0.054. 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.

Output-space branch-and-bound reduction algorithm for generalized linear fractional-multiplicative programming problem

In this paper, we investigate a class of linear fractional-multiplicative programs with exponents, which have important applications in finance and economy. By introducing p variables, the problem is re-represented as an equivalent problem. Immediately, two new linear relaxation strategies are proposed and embedded in the branch-and-bound framework, and the corresponding new global optimization algorithms are developed in combination with an acceleration technique. Furthermore, the theoretical convergence and computational complexity of the algorithms are elucidated. Numerical experiment results illustrate that both algorithms are effective and feasible.

EPC-DARTS: Efficient partial channel connection for differentiable architecture search

With weight-sharing and continuous relaxation strategies, the differentiable architecture search (DARTS) proposes a fast and effective solution to perform neural network architecture search in various deep learning tasks. However, unresolved issues, such as the inefficient memory utilization, and the poor stability of the search architecture due to channels randomly selected, which has even caused performance collapses, are still perplexing researchers and practitioners. In this paper, a novel efficient channel attention mechanism based on partial channel connection for differentiable neural architecture search, termed EPC-DARTS, is proposed to address these two issues. Specifically, we design an efficient channel attention module, which is applied to capture cross-channel interactions and assign weight based on channel importance, to dramatically improve search efficiency and reduce memory occupation. Moreover, only partial channels with higher weights in the mixed calculation of operation are used through the efficient channel attention mechanism, and thus unstable network architectures obtained by the random selection operation can also be avoided in the proposed EPC-DARTS. Experimental results show that the proposed EPC-DARTS achieves remarkably competitive performance (CIFAR-10/CIFAR-100: a test accuracy rate of 97.60%/84.02%), compared to other state-of-the-art NAS methods using only 0.2 GPU-Days.

EST-NAS: An evolutionary strategy with gradient descent for neural architecture search

Using weight-sharing and continuous relaxation strategies, the gradient descent-based differential architecture search has achieved great success in automatically designing neural network architectures. However, unresolved issues, i.e., the local optimum dilemma of the gradient descent method, and the network performance collapse of the searched architecture with too many unreasonable operations, are still frustrating for researchers and practitioners. To address these two issues, a novel and efficient neural architecture search approach based on a hybrid evolutionary strategy, termed EST-NAS, is proposed in this paper. In particular, we propose using a new evolutionary strategy to explore various search directions based on the gradient descent-based neural network architecture search, aiming at obtaining a more excellent architecture. In the proposed EST-NAS, the gradient descent architecture search is performed first, and then the best architecture obtained is utilized to design an efficient initialization for the following evolutionary strategy-based architecture search. By hybridizing evolutionary strategy with gradient descent-based search, EST-NAS can improve the performance of the searched architecture with better search efficiency. Meanwhile, the validation accuracy is applied to directly measure the importance of operations, which reduces the error in the relationship between operation and task performance. Extensive experimental results in the various datasets on different search spaces show that the proposed EST-NAS achieves remarkably competitive performance with less search cost, compared to other state-of-the-art NAS approaches.

RiSSNet: Contrastive Learning Network with a Relaxed Identity Sampling Strategy for Remote Sensing Image Semantic Segmentation

Contrastive learning techniques make it possible to pretrain a general model in a self-supervised paradigm using a large number of unlabeled remote sensing images. The core idea is to pull positive samples defined by data augmentation techniques closer together while pushing apart randomly sampled negative samples to serve as supervised learning signals. This strategy is based on the strict identity hypothesis, i.e., positive samples are strictly defined by each (anchor) sample’s own augmentation transformation. However, this leads to the over-instancing of the features learned by the model and the loss of the ability to fully identify ground objects. Therefore, we proposed a relaxed identity hypothesis governing the feature distribution of different instances within the same class of features. The implementation of the relaxed identity hypothesis requires the sampling and discrimination of the relaxed identical samples. In this study, to realize the sampling of relaxed identical samples under the unsupervised learning paradigm, the remote sensing image was used to show that nearby objects often present a large correlation; neighborhood sampling was carried out around the anchor sample; and the similarity between the sampled samples and the anchor samples was defined as the semantic similarity. To achieve sample discrimination under the relaxed identity hypothesis, the feature loss was calculated and reordered for the samples in the relaxed identical sample queue and the anchor samples, and the feature loss between the anchor samples and the sample queue was defined as the feature similarity. Through the sampling and discrimination of the relaxed identical samples, the leap from instance-level features to class-level features was achieved to a certain extent while enhancing the network’s invariant learning of features. We validated the effectiveness of the proposed method on three datasets, and our method achieved the best experimental results on all three datasets compared to six self-supervised methods.

Low-rank discrete multi-view spectral clustering

Spectral clustering has attracted intensive attention in multimedia applications due to its good performance on arbitrary shaped clusters and well-defined mathematical framework. However, most existing multi-view spectral clustering methods still have the following demerits: (1) They ignore useful complementary information embedded in indicator matrices of different views. (2) The conventional post-processing methods based on the relax and discrete strategy inevitably result in the sub-optimal discrete solution. To tackle the aforementioned drawbacks, we propose a low-rank discrete multi-view spectral clustering model. Drawing inspiration from the fact that the difference between indicator matrices of different views provides useful complementary information for clustering, our model exploits the complementary information embedded in indicator matrices with tensor Schatten p-norm constraint. Further, we integrate low-rank tensor learning and discrete label recovering into a uniform framework, which avoids the uncertainty of the relaxed and discrete strategy. Extensive experiments on benchmark datasets have demonstrated the effectiveness and superiority of the proposed method.

Nonconvex Rician noise removal via convergent plug-and-play framework

Restoring images corrupted by Rician noise is a challenging issue in medical image processing. In the existing methods, the model-driven method can not recover the images well, and the learning-based methods lack good interpretability. In this paper, we propose a plug-and-play (PnP) method to remove Rician noise. Due to the statistical properties of Rician distribution and the implicit deep image priors, the problem is non-convex. We present a convergent PnP method to address these issues by an adaptively relaxed alternating direction method of multipliers. Theoretically, we give some useful mathematical properties and the global linear convergence of the proposed method by an adaptive relaxation strategy. Experimental results show that the proposed method outperforms the existing state-of-art traditional and learning-based methods.

Coded aperture compression temporal imaging based on a dual-mask and deep denoiser.

Coded aperture compressive temporal imaging (CACTI) is the mapping of multiple frames using different encoding patterns into a single measurement and then using an algorithm to reconstruct the required high-dimensional signals, thus enabling high-speed photography on low-speed cameras. An encoding pattern and a reconstruction algorithm both play a critical role for CACTI. To improve the quality of the reconstruction, in terms of encoding, we took advantage of the reflective properties of the digital micromirror device and used a complementary dual-mask pattern to obtain more projection information. In terms of decoding, we developed what we believe, to the best of our knowledge, is a new model combining the weighted Landweber regularization with the relaxation strategy and a deep denoiser. The experimental results show the superiority of our proposed encoding-decoding combination, which achieves better performance in terms of the peak SNR, structural similarity index measure, and visual effects.

Psychological Approaches for Migraine Management.

Migraine headaches are among the most prevalent and disabling pain conditions worldwide. Best-practice migraine management is multidisciplinary and includes the psychological approaches to address cognitive, behavioral, and affective factors that worsen pain, distress, and disability. The psychological interventions with the strongest research support are relaxation strategies, cognitive-behavioral therapy, and biofeedback, though the quality of clinical trials for all psychological interventions needs continued improvement. The efficacy of psychological interventions may be improved by validating technology-based delivery systems, developing interventions for trauma and life stress, and precision medicine approaches matching treatments to patients based on specific clinical characteristics.

Revealing the enhancement mechanism of carbon-encapsulated surface-strained MoNi4 bimetallic nanoalloys toward high-stability polysulfide conversion with a wide temperature range

Bimetallic alloy catalysts, due to their more adsorption sites, more abundant electronic structures, and higher catalytic properties than single-metal ones, have attracted much interest in the field of lithium–sulfur batteries (LSBs). However, many LSBs with bimetallic alloys often suffered from their low cycling stability, which was caused by the over-strong adsorption and the bad chemical stability of the catalysts. Herein, in-situ carbon-encapsulation-induced strain relaxation strategy has been adopted to balance the adsorption and catalytic properties of the MoNi4 bimetallic nanoalloy catalyst for LSBs. As a result, the cathode with strained-MoNi4 embedded carbon nanofibers (CNF@s-MoNi4) delivers a high capacity (1632.5 mAh g−1 at 0.1 C), a superior rate capability (retaining 832.4 mAh g–1 at 5.0 C) and excellent cycling stability (decaying rate of 0.0204% per cycle over 520 cycles at 1.0 C). Even at high rates, the CNF@s-MoNi4 can keep a stable cycling capacity (827.5 mAh g–1 and 447.7 mAh g–1 after 250 cycles at 5.0 C and 10.0 C, respectively). Besides, the CNF@s-MoNi4 LSB also exhibits an excellent wide-temperature-range adaptability (-30 ∼ 50°C) and a superior dynamic bending stability. This study would provide a feasible method for developing high-capacity and long-life LSBs with a wide temperature range demand.

A centralized relaxation strategy for cooperative lane change in a connected environment

This paper proposes a centralized relaxation strategy for cooperative lane changes on the freeway with a connected and automated vehicle (CAV) dedicated lane. Three numerical models are designed in this scenario. Firstly, a lane-changing decision (LCD) model is established considering the speed difference between merging CAV and the target platoon, and the number of following vehicles (FVs) in the platoon. The merging gap is decided by the gap choice model, which is based on the safe distance according to the Gipps model. The relaxation model controls the longitudinal accelerations of all FVs in the platoon to provide proper gaps for merging CAVs. The parameters of these three models are obtained by sensitivity analysis. The results show that safety risks are significantly reduced in the lane-changing (LC) process with centralized relaxation. Benefitting from extending relaxation time, the decrease in amplitude of acceleration alleviates the speed oscillation among the FVs. It also helps to improve traffic efficiency and driving comfort. However, these advantages are weakened when the number of FVs exceeds 5 or the speed difference is over 7 m/s. It is suggested that the single CAV (SV) should merge to the end of the platoon in these conditions from a systematic view.

80 Examining the Relationship between Burn Injury Acuity and Risk for Developing PTSD and Depression Symptoms

Abstract Introduction Individuals who experience traumatic events are at heightened risk for developing adverse mental health outcomes, specifically posttraumatic stress and depressive symptoms. Those hospitalized following a burn injury experience significant psychological and emotional burden during acute burn treatment. Symptoms of depression and posttraumatic stress are common following a traumatic burn injury; however, little is known about the impact of burn-related intensive care unit (ICU) admission on future risk of developing posttraumatic stress disorder (PTSD) and depression. Thus, the current study examined the associations between burn injury acuity (defined by admission to ICU vs. non-ICU) and risk for developing mental health problems (i.e., depression and PTSD) among burn center patients assessed within the initial 30 days following their injury. Methods Participants included 286 patients admitted to the burn center at a southeastern academic medical center (Mage = 46.79, SD = 17.47; 53% White; 66% Male; 94% Non-Hispanic; 25% admitted to ICU). Patients were assessed by the hospital’s burn behavioral health program. Risk of PTSD and depression was assessed via the Injured Trauma Survivor Screen (ITSS), a validated measure used to determine risk of PTSD and depression following an injury. Results Independent samples t-tests revealed that ICU admission was associated with an increased risk for developing posttraumatic stress (p = .022) and depressive symptoms (p = .016) as compared to non-ICU admitted patients when assessed within the initial 30 days following a burn injury. Conclusions Though preliminary, results highlight the importance of considering burn acuity when evaluating risk of developing psychopathology following a burn injury. Admission to ICU appears to play a key role in individuals’ risk for developing PTSD and depressive symptoms. Both physical and emotional healing are critical components of a patient’s recovery process following a burn injury. Applicability of Research to Practice Given these results, it is important to prioritize promoting psychological recovery following a potentially traumatic burn injury in those admitted to ICU through early screening and intervention. Early intervention should emphasize reducing avoidance of feared stimuli, gradual exposure, relaxation strategies, social support, and behavioral activation techniques particularly for patients admitted to the ICU to help mitigate negative effects of trauma exposure that may lead to increased psychopathology.

From Stressful to Mindful: Reactions to a Proposed Emergency Department Design for Enhancing Mindfulness and Stress Reduction Among Healthcare Clinical Staff.

This study explored design solutions that can help clinicians manage work-related stress, pursue mindful work, and practice relaxation and coping strategies. Clinicians are experiencing burnout at increasingly higher rates, leading to compromised patient care. While self-care and stress management strategies are shown to be effective in healthcare settings, little is known about how the design of healthcare settings can facilitate these strategies. Using findings of a preceding study by the authors, a virtual walk-through video of a design proposal for a free-standing emergency department was built. Feedback data were collected from clinicians in 10 in-depth interviews, and emerging themes were identified using qualitative content analysis. Our findings suggest that work environments that reflect intentional efforts to meet clinicians' needs for stress management can enhance their feeling of being valued and sense of professional worth. Clinicians are more likely to step away from work if there are multiple spaces for bio breaks and rest breaks. Primary break rooms are preferred to be close to patient care areas, but respite/escape spaces are preferred to be distant and private from patient care areas. Participants endorsed the provision of at least one staff bathroom large enough for extra seating and one outdoor respite space. Multiple workspaces should allow the completion of various tasks with adjustable levels of privacy. We suggest special attention to non-institutional aesthetics, providing choice and control, increasing flexibility, promoting privacy for focused work, maximizing access to daylight and outdoors, and providing opportunities for grief support.

Experience of Pain and Unpleasantness during Mammography Screening: A Cross-Sectional Study on the Roles of Emotional, Cognitive, and Personality Factors.

Breast cancer is the most frequent cause of malignant tumors among women worldwide. Its successful prevention depends on the degree of participation in screening programs, which can be influenced by psychological factors, including fear. A cross-sectional study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement. Twenty-six healthy women aged 50-69 years took part in this study, all of whom were summoned for routine mammography screening and were randomly selected. Prior mammography screening, breast pain intensity, unpleasantness (visual analog scale), and psychological (catastrophizing, state anxiety, and fear of pain) and personality (neuroticism, psychoticism, and extraversion) variables were evaluated. Pain, unpleasantness, and state anxiety were further evaluated pre- and post-mammography screening. During the mammography screening, pain and unpleasantness levels were higher than those observed pre- and post-screening. Residual unpleasantness remained post-screening. State anxiety was positively associated with pain, and psychoticism with unpleasantness, as reported by participants during the mammography screening. Anxiety levels influence the pain experienced in association with the mammography procedure. Women subjected to mammography screenings might benefit from relaxation strategies aimed at reducing anxiety to pre-mammography levels and, by extension, pain and unpleasantness during mammography. The inclusion of these strategies in breast cancer prevention campaigns could improve the rates of mammography reattendance, and therefore, benefit cancer prevention efforts.

TO STUDY STRESS AND PSYCHOLOGICAL WELL-BEING IN MOTHER OF INTELLECTUALLY DISABLED CHILDREN IN RURALAREA: A CROSS SECTIONAL OBSERVATION STUDY

Introduction: Intellectual and developmental disability (IDD) is a developmental impairment that has no known treatment. Children with intellectual disabilities are typically abandoned and excluded from society as a whole. The wellbeing of a family is inuenced by the mental and physical health of the parents, especially the women who are the centre of the family. They are the ones who must deal with issues related to their child's disability, such as delayed development, a higher incidence of child psychopathology, behavioural issues, and decisions about whether to seek out-of-home care for the child and therapeutic services while simultaneously maintaining the household. current study was conducted to determine stress and psychological well-being in mother of intellectually disabled children Materials and Methods: Present cross-sectional prospective study was conducted on 50 Caregiver mothers of intellectual disability (ID) child from rural area to determine their stress and psychological well-being. Children were classied into mild (IQ-55 to 79), moderate (IQ 35 to 54) and severe (IQ 20 to 35) intellectual disability (ID) based on IQ. Stress was assessed by Perceived Stress Scale (PSS) and wellbeing by psychological wellbeing scale. Observations and Results: PSS score low (0 to 13), Moderate (14 to 26) and High (27 to 40) was found in 18 (36 %), 18 (36 %) and 14 (28 %) cases respectively. Result was statistically signicant (P=0.032). PWB score Very Low (50 to 58), Low (58 to 83), Moderate (83 to 217), High (217 to 242) and Very High (242 to 250) was found in 7 (14 %), 9 (18 %), 11 (22 %), 11 (22 %) and 12(24 %) cases respectively. Result was statistically signicant (P=0.034) Conclusion: Parents of intellectually disabled children should engage in therapies including psychotherapy, relaxation, and stressreduction strategies to improve their psychological health. Proper counselling services also assist the parent in leading a healthy life and in maintaining their psychological health

An Evolutionary Algorithm With Constraint Relaxation Strategy for Highly Constrained Multiobjective Optimization.

Highly constrained multiobjective optimization problems (HCMOPs) refer to constrained multiobjective optimization problems (CMOPs) with complex constraints and small feasible regions, which are commonly encountered in many real-world applications. Current constraint-handling techniques will face two difficulties when dealing with HCMOPs: 1) feasible solution is hard to be found and too much search effort is spent in locating the feasible region and 2) since the total feasible region of an HCMOP can consist of several disconnected subregions, the search process might be stuck in the comparatively larger feasible subregion, which does not contain the whole Pareto front (PF). To address these two issues, an evolutionary algorithm with constraint relaxation strategy based on differential evolution algorithm, that is, CRS-DE, is proposed in this article. In each generation, the CRS-DE relaxes the constraints by dividing the infeasible solutions into two subpopulations based on total constraint violation, that is, the ``semifeasible'' subpopulation (SF) and ``infeasible'' subpopulation (IF), respectively. The SF provides information on the promising regions of finding the feasible solution and is the driving force for convergence toward the PF, while the IF focuses on global exploration for new promising regions. Corresponding reproduction and selection strategies are devised for the SF, IF, and feasible subpopulations, which create a clear division of labor with cooperation to facilitate the search for feasible solutions. To leverage the influence of CRS and prevent the population from premature convergence, a mobility restriction mechanism is developed to restrict the individuals in the SF and IF from entering the feasible subpopulation and enhance the diversity of the whole population. Comprehensive experiments on a series of benchmark test problems and a real-world CMOP demonstrate the competitiveness of our method compared with other representative algorithms in terms of effectiveness and reliability in finding a set of well-distributed optimal solutions for HCMOPs.

Immersive virtual reality: An effective strategy for reducing stress in young adults

Background: Psychological stress is ubiquitous in young adults, but traditional relaxation strategies such as mindfulness or meditation are not consistently utilised. Immersive virtual reality (VR) is being increasingly used in mental health contexts with improved clinical outcomes. However, there is limited research exploring the perceived usefulness of VR for individuals without a mental health diagnosis. Aim: To identify if an immersive VR experience was effective in reducing stress for young adults and to explore the experience of the participants. Methods: Twenty-one young adults aged 18–25 years participated in an immersive VR experience. Heart rate was measured pre- and post-test to objectively establish the efficacy of VR to facilitate relaxation. Participants were invited to participate in a semi-structured interview ( n = 18) to gain a rich understanding of their experience. Results: Four themes emerged from analysis of the interviews: I felt relaxed and calm, It was time-efficient and easy to use, It took me to another place and It was different than I thought it would be. Heart rate reduced during the immersive VR experience. Conclusion: These findings suggest that immersive VR can reduce psychological stress, and it is perceived by young adults as a useful and time-effective strategy to reduce psychological stress.

Assessment of Lagrangean decomposition for short-term planning of integrated refinery-petrochemical operations

We present an integrated methodology for optimal short-term planning of integrated refinery-petrochemical complexes (IRPCs) and demonstrate it on a full-scale industrial case study under four realistic planning scenarios. The large-scale mixed-integer quadratically constrained optimization models are amenable to a spatial Lagrangean decomposition through dividing the IRPC into multiple subsections, which comprise crude management, refinery, fuel blending, and petrochemical production. The decomposition algorithm creates virtual markets for trading crude blends and intermediate petrochemical streams within the IRPC and seeks an optimal tradeoff in such markets, with the Lagrange multipliers acting as transfer prices. The best results are obtained for decompositions with two or three subsections, achieving optimality gaps below 4% in all four planning scenarios. The Lagrangean decomposition provides tighter primal and dual bounds than the global solvers BARON and ANTIGONE, and it also improves the dual bounds computed using piecewise linear relaxation strategies.

Orientation estimation of cryo-EM images using projected gradient descent method

Orientation estimation is an important task in three-dimensional cryo-EM image reconstruction. By applying the common line method, the orientation estimation task can be formulated as a least squares (LS) problem or a least un-squared deviation (LUD) problem with orthogonality constraint. However, the non-convexity of the orthogonality constraint introduces numerical difficulties to the orientation estimation. The conventional approach is to reformulate the orthogonality constrained minimization problem into a semi-definite programming problem using convex relaxation strategies. In this paper, we consider a direct way to solve the constrained minimization problem without relaxation. We focus on the weighted LS problem because the LUD problem can be reformulated into a sequence of weighted LS problems using the iteratively re-weighted LS approach. As a classical approach, the projected gradient descent (PGD) method has been successfully applied to solve the convex constrained minimization problem. We apply the PGD method to the minimization problem with orthogonality constraint and show that the constraint set is a generalized prox-regular set, and it satisfies the norm compatibility condition. We also demonstrate that the objective function of the minimization problem satisfies the restricted strong convexity and the restricted strong smoothness over a constraint set. Therefore, the sequence generated by the PGD method converges when the initial conditions are satisfied. Experimental results show that the PGD method significantly outperforms the semi-definite relaxation methods from a computation standpoint, and the mean square error is almost the same or smaller.

Nonmodified Laser-Induced Graphene Sensors for Lead-Ion Detection

Electrochemical detection, especially anodic stripping voltammetry (ASV), has been widely used in the detection of heavy metal ions because of its high sensitivity, low detection limit, as well as on-site and real-time detection capability. However, ASV detection typically relies on modified sensors, which complicates the preparation process and introduces contaminants to the tested solutions. Herein, we developed a preconcentration relaxation strategy to improve detection performances of bare sensors without modification. Specifically, in the preconcentration step of ASV, the solution stirring did not run throughout the whole preconcentration process but stopped before the end of preconcentration, remaining as a relaxation period for metal ions fully depositing. To verify this strategy, we fabricated porous laser-induced graphene sensors without modification to detect lead ions. With the preconcentration strategy, the detection linear range was widened from 30–100 to 1–100 μg·L–1, and the sensitivity was increased from 0.1362 μA·(μg·L–1)−1 to 0.1772 μA·(μg·L–1)−1 in 1–5 μg·L–1 and 0.7081 μA·(μg·L–1)−1 in 10–100 μg·L–1. Moreover, the limit of detection reached 0.5 μg·L–1 (S/N = 3). This work sheds light on the accurate and effective detection of heavy metal ions.