Adaptation Control Research Articles

The Role of Psychological Resilience, Coping Mechanisms and Locus of Control in Managing Students' Perceived Stress during Exams

Abstract: With this study we provide conclusive evidence on the importance of psychological resilience, coping mechanisms, self-esteem and locus of control for optimal adaptation in situations of intense psychological stress, such as the stress perceived by students in exam situations. The data reveal that out of 120 students included in the study group, 29 students, i.e., 33.2 ⁒ were in the high stress zone and 4 students, i.e., 3.3 ⁒ were in the dangerously high stress zone, which can be explained by the poor functioning of some coping mechanisms and psychological resilience. For example, self-blame as a variable of coping mechanisms has a negative influence on the level of perceived stress and explains 49.72% of the stress level. Also, by comparison, some variables play a positive role in stress management and explain in different proportions the level of perceived stress, such as perspective-taking (35.64%), positive reappraisal (27.64%), positive refocusing (20.25%) or self-esteem (12.33%). The study also revealed that females better manage specific manifestations of perceived stress in exam situations, proving that they more easily establish interpersonal relationships, which in turn can support some healthy response behaviors, how to solve specific tasks and adapt more easily to some specific demands of academic work.

Lineage tracing of nuclei in skeletal myofibers uncovers distinct transcripts and interplay between myonuclear populations

Multinucleated skeletal muscle cells need to acquire additional nuclei through fusion with activated skeletal muscle stem cells when responding to both developmental and adaptive growth stimuli. A fundamental question in skeletal muscle biology has been the reason underlying this need for new nuclei in cells that already harbor hundreds of nuclei. Here we utilize nuclear RNA-sequencing approaches and develop a lineage tracing strategy capable of defining the transcriptional state of recently fused nuclei and distinguishing this state from that of pre-existing nuclei. Our findings reveal the presence of conserved markers of newly fused nuclei both during development and after a hypertrophic stimulus in the adult. However, newly fused nuclei also exhibit divergent gene expression that is determined by the myogenic environment to which they fuse. Moreover, accrual of new nuclei through fusion is required for nuclei already resident in adult myofibers to mount a normal transcriptional response to a load-inducing stimulus. We propose a model of mutual regulation in the control of skeletal muscle development and adaptations, where newly fused and pre-existing myonuclear populations influence each other to maintain optimal functional growth.

Innate immune control of influenza virus interspecies adaptation via IFITM3

Influenza virus pandemics are caused by viruses from animal reservoirs that adapt to efficiently infect and replicate in human hosts. Here, we investigate whether Interferon-Induced Transmembrane Protein 3 (IFITM3), a host antiviral factor with known human deficiencies, plays a role in interspecies virus infection and adaptation. We find that IFITM3-deficient mice and human cells can be infected with low doses of avian influenza viruses that fail to infect WT counterparts, identifying a new role for IFITM3 in controlling the minimum infectious virus dose threshold. Remarkably, influenza viruses passaged through Ifitm3−/− mice exhibit enhanced host adaptation, a result that is distinct from viruses passaged in mice deficient for interferon signaling, which exhibit attenuation. Our data demonstrate that IFITM3 deficiency uniquely facilitates potentially zoonotic influenza virus infections and subsequent adaptation, implicating IFITM3 deficiencies in the human population as a vulnerability for emergence of new pandemic viruses.

Performance Analysis of Multi-Core Systems in Multistage Interconnection Networks: Investigating Challenges in Inter-Processor Communication

For high-performance computers and data-heavy apps, how well multi-core systems work in multistage interconnection networks (MINs) is very important. As the need for parallel processing grows, inter-processor communication (IPC) has become a major issue that affects both the speed of work and the ability of the system to grow. This essay looks into the problems with IPC in multi-core systems set up with MINs, mainly delay, bandwidth limits, and network congestion. imulation-based performance review is used to test multi-core systems with different amounts of traffic, message sizes, and route techniques. A mix of mathematical models and simulation tools are used to test different MIN designs on measures like delay, speed, and fault tolerance. It is also looked at how network structures and methods for controlling crowding affect IPC efficiency. According to the results, MINs can be used to connect multiple devices, but when there is a lot of traffic, the delay goes up a lot because of bottlenecks in some parts of the network. Also, in higher-dimensional networks, processors fighting over bandwidth causes speed to drop. Some of these problems can be solved with optimized transport methods and adaptable congestion control systems, which also make IPC work better overall. This study shows how important it is to keep improving how processors talk to each other in multi-core systems, especially when it comes to making network switching and managing congestion more efficient.

Cybersickness Abatement from Repeated Exposure to VR with Reduced Discomfort.

Cybersickness, or sickness induced by virtual reality (VR), negatively impacts the enjoyment and adoption of the technology. One method that has been used to reduce sickness is repeated exposure to VR, herein Cybersickness Abatement from Repeated Exposure (CARE). However, high sickness levels during repeated exposure may discourage some users from returning. Field of view (FOV) restriction reduces cybersickness by minimizing visual motion in the periphery, but also negatively affects the user's visual experience. This study explored whether CARE that occurs with FOV restriction generalizes to a full FOV experience. Participants played a VR game for up to 20 minutes. Those in the Repeated Exposure Condition played the same VR game on four separate days, experiencing FOV restriction during the first three days and no FOV restriction on the fourth day. Results indicated significant CARE with FOV restriction (Days 1-3). Further, cybersickness on Day 4, without FOV restriction, was significantly lower than that of participants in the Single Exposure Condition, who experienced the game without FOV restriction only on one day. The current findings show that significant CARE can occur while experiencing minimal cybersickness. Results are considered in the context of multiple theoretical explanations for CARE, including sensory rearrangement, adaptation, habituation, and postural control.

Physio-avatar EB: aftereffects in error learning with EMG manipulation of first-person avatar experience.

Many studies have investigated the manipulation of a virtual upper arm using electromyogram (EMG); however, these studies primarily used a machine learning model or trigger control for this purpose. Furthermore, most of them could only display the constant motion of the virtual arm because the motion to be displayed was selected by pattern recognition or trigger control. In addition, these studies did not examine changes in the electromyographic signals after experiencing the virtual arm. By contrast, we propose a real-time, continuous, learning-free avatar that manipulates the virtual arm with electromyogram signals or physio-avatar EMG biofeedback (EB). The goal of the physio-avatar EB system is to induce physiological changes through experiential interactions. We explored the possibility of changing motor control strategies by applying the system to healthy individuals as a case study. An intervention method that provided an experience of a body different from one's own was conducted on seven participants using a time-invariant calculation algorithm to determine the joint angles of the avatar. Control strategies for an indicator of the equilibrium point in the baseline and adaptation phases were determined to evaluate the physio-avatar EB intervention effect. The similarity of these BL and adaptation control strategies compared to those used during the washout period was assessed using the coefficient of determination. The accuracy and reliability of the virtual reality (VR) system were evaluated by comparison with existing studies and the required specs. Changes in motor control strategies due to the physio-avatar EB system were observed in four experiments, where the participants gradually returned to their pre-intervention control strategies. This result can be attributed to the aftereffects caused by error learning. This implies that the developed system influenced their motor control strategies. The number of EMG acquisition bits was 16 bits, and the sampling rate was 1,000 Hz. The refresh rate of the head-mounted display was 90 Hz, and its resolution was for a single eye. Additionally, the simulation frame rate was 30 FPS. These values were adequate compared to existing studies and required specs. The essential contribution of this study is the development of an avatar that is controlled by a different method than has been used in previous studies and the demonstration of changes in a subject's muscle activity after they experience an avatar. In the future, the clinical efficacy of the proposed system will be evaluated with actual patients.

A scalable Deep Q-Learning approach for hot stamping process under dynamic control environment

Despite the significant advancements in Artificial Intelligence and its applications, traditional control techniques still dominate industrial and manufacturing processes. These techniques often rely on static setups and default values, which make them resistant to environmental changes. However, in dynamic and uncertain contexts, real-time process adaptation offers significant advantages in decision-making and control. In this research, we present a novel approach utilising Deep Reinforcement Learning for real-time dynamic parameter adaptation and autonomous control of processes in unpredictable environments. The effectiveness of our proposal is illustrated through the development and training of a Deep Q-Learning control system tailored for optimising a hot stamping process. The control model aims to minimise defect rates and enhance efficiency by reducing forming times during batch productions. Comparative analysis against conventional industry practices, characterised by static and non-adaptive time settings, validates the efficacy of our solution, with improvements of over 20% in the times, depending on the scenario considered. Experimental evaluation in a semi-industrial pilot setting not only demonstrates the adaptability of our approach but also highlights its potential scalability for diverse industrial applications.

Complex sporulation-specific expression of transcription termination factor Rho highlights its involvement in Bacillus subtilis cell differentiation

Termination factor Rho, responsible for the main factor-dependent pathway of transcription termination and the major inhibitor of antisense transcription, is an emerging regulator of various physiological processes in microorganisms. In Gram-positive bacterium Bacillus subtilis, Rho is involved in the control of cell adaptation to starvation and, in particular, in the control of sporulation, a complex differentiation program leading to formation of a highly resistant dormant spore. While the initiation of sporulation requires a decrease in Rho protein levels during the transition to stationary phase, the mechanisms regulating the expression of rho gene throughout the cell cycle remain largely unknown. Here we show that a drop in the activity of the vegetative SigA-dependent rho promoter causes the inhibition of rho expression in stationary phase. However, after the initiation of sporulation, rho gene is specifically reactivated in two compartments of the sporulating cell using distinct mechanisms. In the mother cell, rho expression occurs by read-through transcription initiated at the SigH-dependent promoter of the distal spo0F gene. In the forespore, rho gene is transcribed from the intrinsic promoter recognized by the alternative sigma factor SigF. These regulatory elements ensure the activity of Rho during sporulation, which appears important for the proper formation of spores. We provide experimental evidence that disruption of the spatiotemporal expression of rho during sporulation affects the resistance properties of spores, their morphology, and the ability to return to vegetative growth under favorable growth conditions.

Research and Practice of Sand Control Completion Technology Optimization in Western H Oilfield

Abstract In view of the problems of poor reservoir physical properties, high clay content, loose cementation, shallow burial, low compaction degree, low temperature in the middle and deep oil layers during the development process of unconsolidated sandstone reservoirs in the western H oilfield, the original sand control technology has poor adaptability and short sand control validity period., this paper carried out research on the optimization of sand control completion technology. In the study, a series of optimization studies were conducted, including analysis of acoustic time difference in sand producing layers, simulation of sand control formation compaction, optimization of gravel filling particle size, and sand control tools. A high-strength artificial wellbore and cyclic gravel filling sand control completion process suitable for different sand producing well conditions were developed and applied in practice. The research results and on-site practice indicate that the high-strength artificial wellbore sand control completion technology has good adaptability in low-temperature and long wellbore sand production wells; The cyclic gravel filling sand control completion technology has the advantages of simple construction, low cost, and low probability of major repairs in conventional shallow sand producing wells; The complementary advantages of the two processes effectively solve the problem of sand damage in H oilfield. Since 2021, the optimized sand control completion technology has been applied 46 times in H oilfield, with an average recovery oil production of 3.4t per well and an average effective period of over 500 days for sand control. The overall input-output ratio is 1:2.8, achieving good benefits and having high promotion and application value.

Theoretical and simulation analysis of DC bus current fluctuation control strategies of modular multilevel converter under AC side fault conditions

Modular Multilevel Converters (MMCs) play a crucial role in high-voltage DC (HVDC) systems due to their adaptable control and swift response capabilities. However, AC side faults could introduce asymmetric double-line frequency components into the MMC's circulating current, jeopardizing DC bus stability. Although strategies to mitigate DC side fluctuations are well-established, a detailed comparison of their operational impacts remains unexplored. The operational effects of MMCs under AC side faults are firstly examined in this study, including issues related to grid-connected current asymmetry and DC bus current fluctuations. Subsequently, two targeted control schemes are devised based on the goals of suppressing the double-line frequency components and only the zero-sequence components of circulating current. Through simulation, these approaches are contrasted, delineating their advantages and disadvantages in terms of circulating current suppression, submodules(SMs) capacitance voltage fluctuation, and other performance metrics, which could provide a foundational reference for designing and selecting MMC control strategies under AC side faults.

Adaptation in Edge Computing: A Review on Design Principles and Research Challenges

Edge computing places the computational services and resources closer to the user proximity, to reduce latency, and ensure the quality of service and experience. Low latency, context awareness and mobility support are the major contributors to edge-enabled smart systems. Such systems require handling new situations and change on the fly and ensuring the quality of service while only having access to constrained computation and communication resources and operating in mobile, dynamic and ever-changing environments. Hence, adaptation and self-organisation are crucial for such systems to maintain their performance, and operability while accommodating new changes in their environment. This article reviews the current literature in the field of adaptive edge computing systems. We use a widely accepted taxonomy, which describes the important aspects of adaptive behaviour implementation in computing systems. This taxonomy discusses aspects such as adaptation reasons, the various levels an adaptation strategy can be implemented, the time of reaction to a change, categories of adaptation technique and control of the adaptive behaviour. In this article, we discuss how these aspects are addressed in the literature and identify the open research challenges and future direction in adaptive edge computing systems. The results of our analysis show that most of the identified approaches target adaptation at the application level, and only a few focus on middleware, communication infrastructure and context. Adaptations that are required to address the changes in the context, changes caused by users or in the system itself are also less explored. Furthermore, most of the literature has opted for reactive adaptation, although proactive adaptation is essential to maintain the edge computing systems’ performance and interoperability by anticipating the required adaptations on the fly. Additionally, most approaches apply a centralised adaptation control, which does not perfectly fit the mostly decentralised/distributed edge computing settings.

Muscle spindles provide flexible sensory feedback for movement sequences.

Sensory feedback is essential for motor performance and must adapt to task demands. Muscle spindle afferents (MSAs) are a major primary source of feedback about movement, and their responses are readily modulated online by gain-controller fusimotor neurons and other mechanisms. They are therefore a powerful site for implementing flexible sensorimotor control. We recorded from MSAs innervating the jaw musculature during performance of a directed lick sequence task. Jaw MSAs encoded complex jaw-tongue kinematics. However, kinematic encoding alone accounted for less than half of MSA spiking variability. MSA representations of kinematics changed based on sequence progression (beginning, middle, or end of the sequence, or reward consumption), suggesting that MSAs are flexibly tuned across the task. Dynamic control of incoming feedback signals from MSAs may be a strategy for adaptable sensorimotor control during performance of complex behaviors.

Examining Facade Adaptability: A Study of Adaptive Facade Classification Criteria

In this research, the facade classification of three different buildings located in Europe, Asia and Australia is carried out by using a classification model developed by utilising the criteria of some adaptive facade classification models in the literature. The control parameters affecting the facade design, physical environment criteria, facade adaptation method, control system and technique, visibility of adaptation and time scale criteria in the model are discussed in detail. Besides, the historical development, contemporary trends and basic parameters on architecture are analysed comprehensively. The emergence of adaptive architecture is discussed, as well as efforts to demonstrate changes in the design of elements sensitive to dynamic environmental and user needs. Furthermore, how adaptability is integrated with the building envelope and facade, the historical evolution of adaptive facades, and the development of key components are also examined. Thus, this study aims to make an important contribution to understanding adaptive architecture and adaptive facades while categorising examples of adaptive facades across three continents, including cultural and geographical differences.

Locally different models in a checkerboard pattern with mesh adaptation and error control for multiple quantities of interest

Locally different models in a checkerboard pattern with mesh adaptation and error control for multiple quantities of interest

A Framework for Quality Control in Quantitative Proteomics.

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow, from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at the protein and peptide levels allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and ProteomeXchange under the identifier PXD051318.

Adaptive Modular Neural Control for Online Gait Synchronization and Adaptation of an Assistive Lower-Limb Exoskeleton.

Gait synchronization has attracted significant attention in research on assistive lower-limb exoskeletons because it can circumvent conflicting movements and improve the assistance performance. This study proposes an adaptive modular neural control (AMNC) for online gait synchronization and the adaptation of a lower-limb exoskeleton. The AMNC comprises several distributed and interpretable neural modules that interact with each other to effectively exploit neural dynamics and adopt feedback signals to quickly reduce the tracking error, thereby smoothly synchronizing the exoskeleton movement with the user's movement on the fly. Taking state-of-the-art control as the benchmark, the proposed AMNC provides further improvements in the locomotion phase, frequency, and shape adaptation. Accordingly, under the physical interaction between the user and the exoskeleton, the control can reduce the optimized tracking error and unseen interaction torque by up to 80% and 30%, respectively. Accordingly, this study contributes to the advancement of exoskeleton and wearable robotics research in gait assistance for the next generation of personalized healthcare.

Prioritized adjustments in posture stabilization and adaptive reaching during neuromuscular fatigue of lower-limb muscles.

Neuromuscular fatigue (NMF) induces temporary reductions in muscle force production capacity, affecting various aspects of motor function. Although studies have extensively explored NMF's impact on muscle activation patterns and postural stability, its influence on motor adaptation processes remains less understood. This article investigates the effects of localized NMF on motor adaptation during upright stance, focusing on reaching tasks. Using a force-field perturbation paradigm, participants performed reaching movements while standing upright before and after inducing NMF in the ankle dorsiflexor muscles. Results revealed that despite maintained postural stability, participants in the NMF group exhibited larger movement errors during reaching tasks, suggesting impaired motor adaptation. This was evident in both initial and terminal phases of adaptation, indicating a disruption in learning processes rather than a decreased adaptation rate. Analysis of electromyography activation patterns highlighted distinct strategies between groups, with the NMF group showing altered activation of both fatigued and nonfatigued muscles. In addition, differences in coactivation patterns suggested compensatory mechanisms to prioritize postural stability despite NMF-induced disruptions. These findings underscore the complex interplay between NMF, motor adaptation, and postural control, suggesting a potential role for central nervous system mechanisms in mediating adaptation processes. Understanding these mechanisms has implications for sports performance, rehabilitation, and motor skill acquisition, where NMF may impact the learning and retention of motor tasks. Further research is warranted to elucidate the transient or long-term effects of NMF on motor adaptation and its implications for motor rehabilitation interventions.NEW & NOTEWORTHY We assessed motor adaptation during force-field reaching following exercise-induced neuromuscular fatigue (NMF) on postural muscles. NMF impaired adaptation in performance. Similarly, diverging activation strategies were observed in the muscles. No effects were seen on measures of postural control. These results suggest the remodulation of motor commands to the muscles in the presence of NMF, which may be relevant in settings where participants could be exposed to NMF while learning, such as sports and rehabilitation.

Modulating bacterial function utilizing A knowledge base of transcriptional regulatory modules.

Synthetic biology enables the reprogramming of cellular functions for various applications. However, challenges in scalability and predictability persist due to context-dependent performance and complex circuit-host interactions. This study introduces an iModulon-based engineering approach, utilizing machine learning-defined co-regulated gene groups (iModulons) as design parts containing essential genes for specific functions. This approach identifies the necessary components for genetic circuits across different contexts, enhancing genome engineering by improving target selection and predicting module behavior. We demonstrate several distinct uses of iModulons: (i) discovery of unknown iModulons to increase protein productivity, heat tolerance and fructose utilization; (ii) an iModulon boosting approach, which amplifies the activity of specific iModulons, improved cell growth under osmotic stress with minimal host regulation disruption; (iii) an iModulon rebalancing strategy, which adjusts the activity levels of iModulons to balance cellular functions, significantly increased oxidative stress tolerance while minimizing trade-offs and (iv) iModulon-based gene annotation enabled natural competence activation by predictably rewiring iModulons. Comparative experiments with traditional methods showed our approach offers advantages in efficiency and predictability of strain engineering. This study demonstrates the potential of iModulon-based strategies to systematically and predictably reprogram cellular functions, offering refined and adaptable control over complex regulatory networks.

High environmentally adaptable phase control with reinforcement learning for coherent beam combination

In contemporary times, the utilization of high-power lasers is progressively expanding. However, the potential of a single laser to generate power confronts inherent physical constraints, leading to limitations in its incremental power augmentation. To address this challenge, coherent beam combining technology has been introduced as a means to achieve amplified power output. In this paper, an intelligent phase control method for coherent beam combining is proposed, which introduces reinforcement learning algorithm to provide assistance for the control problem, designs algorithmic variables based on the physical model of coherent beam combining, deploys it in the environment for multiple interactions, and updates the neural network in real time using the feedback from the environment. The efficacy of the proposed algorithm is validated in a 7-channel coherent beam combining simulation system, and the experiment results show that the synthesized beam is strong and stable under the control of the algorithm, and the convergence of the algorithm as well as the control effect is discussed and validated in different environments, and its performance also exceeds that of the existing mainstream phase control algorithm SPGD.

Synthesis of astigmatic non-uniformly correlated beams.

We present a general strategy for synthesizing astigmatic random structured light beams by jointly manipulating the astigmatic phase and optical coherence. With it, we facilitate the creation of a distinct category of beams termed astigmatic non-uniformly correlated (ANUC) beams. Our study emphasizes the significant influence of the astigmatic phase on the optical spatial coherence distribution, resulting in novel propagation features. Furthermore, we elucidate their underlying physical nature. Experimentally, we successfully generate such beams, validating theoretical projections. Our findings hold promise for diverse applications requiring adaptable spectral density control.