Relaxation Strategies Research Articles

ABodyBuilder3: improved and scalable antibody structure predictions.

In this article, we introduce ABodyBuilder3, an improved and scalable antibody structure prediction model based on ABodyBuilder2. We achieve a new state-of-the-art accuracy in the modelling of CDR loops by leveraging language model embeddings, and show how predicted structures can be further improved through careful relaxation strategies. Finally, we incorporate a predicted Local Distance Difference Test into the model output to allow for a more accurate estimation of uncertainties. The software package is available at https://github.com/Exscientia/ABodyBuilder3 with model weights and data at https://zenodo.org/records/11354577.

Tomographic projection optimization for volumetric additive manufacturing with general band constraint Lp-norm minimization

Tomographic volumetric additive manufacturing is a rapidly growing fabrication technology that enables rapid production of 3D objects through a single build step. In this process, the design of projections directly impacts geometric resolution, material properties, and manufacturing yield of the final printed part. Herein, we identify the hidden equivalent operations of three major existing projection optimization schemes and reformulate them into a general loss function where the optimization behavior can be systematically studied, and unique capabilities of the individual schemes can coalesce. The loss function formulation proposed in this study unified the optimization for binary and greyscale targets and generalized problem relaxation strategies with local tolerancing and weighting. Additionally, this formulation offers control on error sparsity and consistent dose response mapping throughout initialization, optimization, and evaluation. A parameter-sweep analysis in this study guides users in tuning optimization parameters for application-specific goals.

Strategical Ways for Dental Anxiety Management Prior to Third Molar Extraction.

A patient experiencing moderate to severe anxiety often skips regular check-ups, choosing to visit only when necessary. As a result, their oral health worsens. Various variables can affect anxiety such as age, gender, culture, and previous experiences. One of the most frequent oral surgical procedures is extracting the third molar, which is accompanied by severe pain, tension, and anxiety, the use of invasive equipment, and local anesthetic. A combined effect was found of four factors, including the patient, dental employees, setting, and dental procedure, which can trigger and affect dental anxiety. Both pharmacological and non-pharmacological approaches were explored; as a non-drug approach, anxiety was managed by music therapy. Acupuncture, music, audio-visual aids, and lavender oil showed an effect that helped in reducing anxiety. Patients' dental anxiety can be reduced through behavioral management such as educating the patients before the surgical procedure, relaxation strategies, biofeedback, and exposure therapy. Medicationsinclude benzodiazepines, nitrous oxide (N2O), and midazolam. Effective treatment relies on collaboration between the patients anddentists; calm patients not only provide a manageable environment for the dental team but can also result in better treatment outcomes.

A dual-population Constrained Many-Objective Evolutionary Algorithm based on reference point and angle easing strategy.

Constrained many-objective optimization problems (CMaOPs) have gradually emerged in various areas and are significant for this field. These problems often involve intricate Pareto frontiers (PFs) that are both refined and uneven, thereby making their resolution difficult and challenging. Traditional algorithms tend to over prioritize convergence, leading to premature convergence of the decision variables, which greatly reduces the possibility of finding the constrained Pareto frontiers (CPFs). This results in poor overall performance. To tackle this challenge, our solution involves a novel dual-population constrained many-objective evolutionary algorithm based on reference point and angle easing strategy (dCMaOEA-RAE). It relies on a relaxed selection strategy utilizing reference points and angles to facilitate cooperation between dual populations by retaining solutions that may currently perform poorly but contribute positively to the overall optimization process. We are able to guide the population to move to the optimal feasible solution region in a timely manner in order to obtain a series of superior solutions can be obtained. Our proposed algorithm's competitiveness across all three evaluation indicators was demonstrated through experimental results conducted on 77 test problems. Comparisons with ten other cutting-edge algorithms further validated its efficacy.

On symmetry adapted bases in trigonometric optimization

The problem of computing the global minimum of a trigonometric polynomial is computationally hard. We address this problem for the case, where the polynomial is invariant under the exponential action of a finite group. The strategy is to follow an established relaxation strategy in order to obtain a converging hierarchy of lower bounds. Those bounds are obtained by numerically solving semi-definite programs (SDPs) on the cone of positive semi-definite Hermitian Toeplitz matrices, which is outlined in the book of Dumitrescu Dumitrescu (2007). To exploit the invariance, we show that the group has an induced action on the Toeplitz matrices and prove that the feasible region of the SDP can be restricted to the invariant matrices, whilst retaining the same solution. Then we construct a symmetry adapted basis tailored to this group action, which allows us to block-diagonalize invariant matrices and thus reduce the computational complexity to solve the SDP.The approach is in its generality novel for trigonometric optimization and complements the one that was proposed as a poster at the ISSAC 2022 conference Hubert et al. (2022) and later extended to Hubert et al. (2024). In the previous work, we first used the invariance of the trigonometric polynomial to obtain a classical polynomial optimization problem on the orbit space and subsequently relaxed the problem to an SDP. Now, we first make the relaxation and then exploit invariance.Partial results of this article have been presented as a poster at the ISSAC 2023 conference Metzlaff (2023).

The contribution of integrated relaxation for stress management among delinquent adolescent -Suggestion diagnosis and therapeutic protocol

This study aims to explore the contribution of an integrated therapeutic protocol based on integrated relaxation strategies in teaching and equipping delinquent adolescents with knowledge, skills and experience related to managing psychological stress responses. The protocol includes a series of exercises inspired by levels of muscular, respiratory and mental relaxation, using key mechanisms such as body awareness through muscle group activities, followed by mental focus on differentiating between muscle tension and relaxation to enhance imaginative activity. The study adopts a combined clinical and quasi-experimental approach, with each case being studied individually, using pre- and post-measurements and follow-up measurements spread over three different periods. The sample consists of fifteen (15) juvenile delinquents. The research instruments include semi-structured interviews, two scales developed by the researchers to assess the level of psychological stress among delinquent adolescents, and a questionnaire on the delinquent adolescent's response to stress (the diagnostic protocol). The therapeutic protocol, based on integrated relaxation techniques, is then implemented. The main finding of the study is the contribution of integrated relaxation to the management of physiological, emotional, cognitive and behavioural stress responses in delinquent adolescents.

Managing the Pandemic in Digitized Spaces: Assessing the Social Media Approaches of Scandinavian Public Health Authorities

In response to the COVID-19 pandemic, health- and civil-contingency agencies—referred to here as public health authorities (PHAs)—in Sweden, Norway, and Denmark turned to social media to disseminate pandemic recommendations and information. This study explores the social media crisis management strategies employed by Scandinavian PHAs. Specifically, we apply a multiplatform research approach to assess communication objectives (Instruct, Support, Manage Reputation, and Solicit Interaction) across three social media platforms—Facebook, Instagram, and Twitter (currently known as X). Introducing a series of hypotheses based on previous scholarship, we detail the prevalence of different objectives across platforms and countries. The results indicate prominent use of reputational management, particularly on Twitter, while instructive information emerged as a highly used communication objective in Sweden and Denmark. Overall, the communicative trends remained parallel across nations, despite Sweden implementing a more relaxed crisis management strategy. The main distinction in Sweden’s approach manifested in a relatively lower emphasis on the pandemic by its PHAs compared to Denmark and Norway. National differences in crisis communication objectives indicate that Norwegian PHAs stand out in terms of using reputational management, while Sweden stands out in employing more supportive information on Instagram.

Joint-learning-based coded aperture compressive temporal imaging

Coded aperture compressive temporal imaging (CACTI) is a recently developed imaging technique based on the theory of compressed sensing. It uses an optical imaging system to sample a high-speed dynamic scene (a set of consecutive video frames), integrates the sampled data in time according to masks (sensing matrix), and thus obtains compressive measurements. Considerable effort has been devoted to the sampling strategy and the ill-posed inverse process of reconstructing a three-dimensional (3D) high-speed dynamic scene from two-dimensional (2D) compressive measurements. The importance of the reconstruction algorithm and the optimization mask is evident. In this paper, a flexible, efficient, and superior quality Landweber iterative method is proposed for video reconstruction through jointly learning the optimal binary mask strategy, relaxation strategy, and regularization strategy. To solve the sparse representation problem in iteration, multiple denoisers are introduced to obtain more regularization prior information. By combining the mathematical structure of the Landweber iterative reconstruction method with deep learning, the challenging parameter selection procedure is successfully tackled. Extensive experimental results demonstrate the superiority of the proposed method.

Providing a Review of Hot Carrier (HC) Relaxation Dynamics and Strategies of HC Extraction in Perovskites Nanocrystals

AbstractMetal halide perovskite (MHPs) nanocrystals (NCs) have recently emerged as promising materials for optoelectronic applications due to their exceptional photophysical properties. A comprehensive understanding of the underlying principles and key challenges associated with controlling the hot carrier (HC) cooling dynamics in MHPs is pivotal for the design of efficient perovskite‐based optoelectronic applications. This review highlights the general carrier cooling mechanism and identifies influencing crucial controlling parameters for carrier dynamics. The discussion includes strategies for manipulating HC cooling dynamics through carrier‐phonon and carrier‐carrier interactions. Notably, recent advancements in HC extraction with suitable acceptors are explored, as efficient HC extraction is imperative for the advancement of optoelectronic applications. The objective of this review is to outline potential pathways for strategically modifying carrier cooling processes to align with specific optoelectronic applications. A profound comprehension of charge carrier dynamics in MHPs NCs will pave the way for the development of highly efficient optoelectronic applications.

New Hyperacusis Therapy Combines Psychoeducation, Sound Exposure, and Counseling.

The aim of this study was to investigate the short- and long-term effects of a new cognitive sound exposure therapy (CSET) in patients with hyperacusis. A new therapy was developed to reduce hyperacusis using sound exposure combined with breathing and relaxation strategies from both acceptance and commitment therapy and cognitive behavioral therapy. Patients who were referred to the Speech and Hearing Centers located in Hengelo and Zwolle in the Netherlands and aged ≥ 18 years with hyperacusis as main complaint and no or mild hearing loss were included in this study. Patients were seen for CSET between June 2020 and August 2022. The sessions took place biweekly. Sessions ended when exposure reached a level with a maximum of 70-80 dB SPL. Short-term effects between the start and the end of therapy were based on tolerable level of sound exposure (dB SPL), subjective-level hinderance of hyperacusis, and sensitivity to sound using the Hyperacusis Questionnaire (HQ). The long-term effect was based on HQ 6 months after the end of therapy. Linear mixed-effects and regression models were applied to study outcomes over time. In total, 30 patients, 15 men and 15 women, aged between 24 and 76 years were included in this study. The mean number of sessions during therapy was 6 and ranged between 4 and 8. Results showed an increase of exposure level (mean change was +23.7 dB with an SD of 7.9, p < .001), a decrease in sensitivity to daily sounds (mean [SD] change was -1.6 [2.1], p < .001), and a decrease in HQ (mean [SD] change was -9.8 [4.9], p < .001), between the start and the end of therapy. There was no significant change in HQ after the end of therapy and 6 months later; mean (SD) change was 0.2 (4.3), p = .81. The evaluation of CSET indicated a decrease in short- and long-term sensitivity to sound in patients with hyperacusis. Additionally, CSET has shown a positive impact, not only for the sounds used in the therapy sessions but also in transferring benefits to everyday sounds. The results of combining psychoeducation, sound exposure, and counseling are promising and warrant further evaluation.

An adaptive differential evolution algorithm based on relaxation strategy to realize the Nash equilibrium of multi-oligopoly game

. Inspired by the idea of game learning, this paper studies the Cournot game model of the multi-oligopoly market and studies the realization of the Nash equilibrium by using adaptive differential evolution algorithm based on relaxation strategies. The realization path of Nash equilibrium is attained through an adaptive differential evolution based on a relaxation strategy procedure applied to an objective function with best response dynamics. Among them, the objective function is derived from the sum of the payoff changes of each oligopoly under best response decisions. And theoretically proved that the Nash equilibrium is the optimal solution of the objective function.

Supervised Contrastive Discrete Hashing for cross-modal retrieval

Cross-modal hashing aims at mapping data points from varied modalities to a shared Hamming space for providing swift and adaptive search services. The recent progress in contrastive learning has significantly enhanced the retrieval performance of cross-modal hashing, owing to its strong ability in powerful representations learning. However, most of them face two primary challenges: (1) they select negative samples for anchors randomly in each mini-batch, which may bring false negative samples that destroy the intrinsic semantic similarity in the learned hash codes; (2) the continuous relaxation strategy is commonly used to generate discrete hash codes, which results in quantization errors and consequently impairing the quality of hash codes. To alleviate these issues, we propose a deep supervised cross-modal hashing scheme, termed Supervised Contrastive Discrete Hashing (SCDH). Specifically, we extend the unsupervised contrastive method to the supervised setting to construct the positive and negative data points with class labels, avoiding the performance decreasing caused by false negative data. Besides, multiple positive and negative samples for each anchor can be chosen from the generated global dictionary bank by class labels, which can better capture the global data structure. Moreover, discrete hash codes can be generated directly, enhancing the feature representational capacity of hash codes. Comprehensive experiments conducted on two public datasets affirm the superior performance of the proposed SCDH over several other cutting-edge methods.

Loss Relaxation Strategy for Noisy Facial Video-based Automatic Depression Recognition

Automatic depression analysis has been widely investigated on face videos that have been carefully collected and annotated in lab conditions. However, videos collected under real-world conditions may suffer from various types of noise due to challenging data acquisition conditions and lack of annotators. Although deep learning (DL) models frequently show excellent depression analysis performances on datasets collected in controlled lab conditions, such noise may degrade their generalization abilities for real-world depression analysis tasks. In this article, we uncovered that noisy facial data and annotations consistently change the distribution of training losses for facial depression DL models; i.e., noisy data–label pairs cause larger loss values compared to clean data–label pairs. Since different loss functions could be applied depending on the employed model and task, we propose a generic loss function relaxation strategy that can jointly reduce the negative impact of various noisy data and annotation problems occurring in both classification and regression loss functions for face video-based depression analysis, where the parameters of the proposed strategy can be automatically adapted during depression model training. The experimental results on 25 different artificially created noisy depression conditions (i.e., five noise types with five different noise levels) show that our loss relaxation strategy can clearly enhance both classification and regression loss functions, enabling the generation of superior face video-based depression analysis models under almost all noisy conditions. Our approach is robust to its main variable settings and can adaptively and automatically obtain its parameters during training.

Generalization of graph-based active learning relaxation strategies across materials

Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learning (ML) assisted techniques to improve the computational efficiency of DFT. In this study, we present a comprehensive investigation of the broader application of Finetuna, an active learning framework to accelerate structural relaxation in DFT with prior information from Open Catalyst Project pretrained graph neural networks. We explore the challenges associated with out-of-domain systems: alcohol ( C>2 ) on metal surfaces as larger adsorbates, metal oxides with spin polarization, and three-dimensional (3D) structures like zeolites and metal organic frameworks. By pre-training ML models on large datasets and fine-tuning the model along the simulation, we demonstrate the framework’s ability to conduct relaxations with fewer DFT calculations. Depending on the similarity of the test systems to the training systems, a more conservative querying strategy is applied. Our best-performing Finetuna strategy reduces the number of DFT single-point calculations by 80% for alcohols and 3D structures, and 42% for oxide systems.

OkayPlan: Obstacle Kinematics Augmented Dynamic real-time path Planning via particle swarm optimization

Existing Global Path Planning (GPP) algorithms predominantly presume planning in static environments. This assumption immensely limits their applications to Unmanned Surface Vehicles (USVs) that typically navigate in dynamic environments. To address this limitation, we present OkayPlan, a GPP algorithm capable of generating safe and short paths in dynamic scenarios at a real-time executing speed (125 Hz on a desktop-class computer). Specifically, we approach the challenge of dynamic obstacle avoidance by formulating the path planning problem as an Obstacle Kinematics Augmented Optimization Problem (OKAOP), which can be efficiently resolved through a PSO-based optimizer at a real-time speed. Meanwhile, a Dynamic Prioritized Initialization (DPI) mechanism that adaptively initializes potential solutions for the optimization problem is established to further ameliorate the solution quality. Additionally, a relaxation strategy that facilitates the autonomous tuning of OkayPlan’s hyperparameters in dynamic environments is devised. Comprehensive experiments, including comparative evaluations, ablation studies, and applications to 3D physical simulation platforms, have been conducted to substantiate the efficacy of our approach. Results indicate that OkayPlan outstrips existing methods in terms of path safety, length optimality, and computational efficiency, establishing it as a potent GPP technique for dynamic environments. The video and code associated with this paper are accessible at https://github.com/XinJingHao/OkayPlan.

A one-dimensional branching rule based branch-and-bound algorithm for minimax linear fractional programming

This paper investigates a type of minimax linear fractional program (MLFP) that often occurs in practical problems such as design of electronic circuits, finance and investment. We first transform the MLFP problem into an equivalent problem (EP) by using the Charnes–Cooper transformation and introducing an auxiliary variable. A linear relaxation strategy should simplify the nonconvex parts of the constraints in (EP). For globally solving MLFP, a branch-and-bound algorithm is then developed. It integrates the presented relaxation with the one-dimensional branching. The convergence of the algorithm is demonstrated and the number of worst-case iterations is estimated. Finally, preliminary numerical experiments verify that the proposed algorithm in this article is robust and efficient for solving the tested instances.

IJCM_154A: Feasibility and Adherence of the Multimodal Oncology Rehabilitation Exercise (MORE) program amongst head and neck cancer survivors in a tertiary care hospital in India – A Cross Sectional Study

Background: Owing to the improvements in the diagnostic and treatment modalities, survivorship outcomes have improved drastically among cancer survivors across the globe. A mounting body of evidence states that exercise can significantly benefit physical and mental health during the various stages of anti-cancer treatment. A frequently used outcome measure used to determine exercise adherence rates and the causes for adherence and non-adherence is the Exercise Adherence Rating Scale (EARS). Objective: To assess the adherence of a tailored home Multimodal Oncology Rehabilitation Exercise (MORE) program amongst head and neck cancer survivors in a tertiary care hospital in India using the Exercise Adherence Rating Scale (EARS). Methodology: The EARS tool was provided to a sample of 22 head and neck cancer (HNC) survivors with stages III to IVb during the 3rd month follow-up. The home exercise program consisted of a template provided to the survivors on the day of discharge. The program consisted of tailored exercises i.e., cognitive motor strategies, a physical exercise program and relaxation strategies. Additionally, the participants were provided with an activity log to record the time, duration and any adverse events occurring during the exercise program. Results: The 6-item EARS tool was administered to the 22 HNC survivors. 45.5% of the 22 survivors regarded ‘lack of time’ as a barrier to exercising regularly. 18% of the survivors claimed to often forget exercising on a regular basis as recommended by the therapist. However, 50% of the survivors performed most or all of the exercises on a regular basis. Conclusion: Lack of time, forgetting to exercise and not fitting the exercises into regular routine can be regarded as essential barriers to adherence to the exercise regimen. The participants did not report any adverse events during the course of the exercise routine.

A self-adaptive Gaussian mutation-based arithmetic optimiser algorithm for integrated production scheduling and vehicle routing problem in the distributed manufacturing environment

ABSTRACT In response to the challenges posed by economic globalisation and increasing customer demands, enterprises are compelled to adapt and refine their production models and operational objectives, which motivates this paper to address the integrated production scheduling and vehicle routing problem in the distributed manufacturing environment (IPSVRP-DME). The objective is to simultaneously minimise both total energy consumption and total earliness/tardiness. Initially, a mixed integer programming model is proposed to address small-scale problems using Gurobi. Considering the NP-hardness of the problem, a novel Self-adapted Gaussian Mutation-based Arithmetic Optimiser Algorithm (SGMAOA) is developed to handle medium-scale and large-scale instances. To address the complexity of decision-making, an innovative two-level encoding method that encompasses four decision dimensions is introduced. Additionally, an incremental repair strategy is devised to rectify infeasible solutions caused by unreasonable delivery batching, while a time relaxation strategy is proposed to further enhance service levels without compromising energy consumption. Comparative experiments are conducted to demonstrate the effectiveness of SGMAOA, which is benchmarked against five prominent metaheuristics. In addition, a specific example is applied for the discussion of managerial applications and to illustrate the practicality of the proposed model and solution method.

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.