Adaptation Control Research Articles

Operational strategy, capabilities, and successfully accomplishing business strategy

This study investigates the vital role of corporations’ operational capabilities, which involve resources, competencies, and processes, in attaining the business’s strategic goals for profitability and sustainability. The methodology employs a selective literature review to analyze case studies, books, and articles that give an overview of operational management and its importance in achieving corporate goals. The results exhibit the challenges posed by rapid market changes, revealing the deficiencies of traditional operational systems focused solely on efficiency and cost. To attain sustained success, manufacturers must focus on a client-valued approach, align objectives with capabilities, and support inter-functional links. Companies with distinctive managerial processes outperform in profitability, emphasizing the significance of investing in management capabilities. Operational excellence necessitates a focus on efficiency, innovation, and customer experience, prompting a shift towards a learning style that encourages innovation. Aligning innovation efforts with business strategy is essential for sustainable competitive advantage. Technological innovation, particularly AI adoption, plays a transformative role in operational innovation, highlighting the importance of dynamic capabilities in managing technological change. Yet, effective inventory and supply chain management are important for meeting customer demand and maximizing efficiency through collaboration and adaptable control.

The Simulation of Pose Control for Stewart Platform Based on Gain-Enhanced PID with Generalized Boolean Algebra

Due to the nonlinear and time-varying uncertainties commonly present in practical industrial systems, traditional PID controllers often fail to achieve optimal control performance under significant input variations. This paper proposes a gain-enhanced PID control method optimized by generalized Boolean algebra, applying Boolean algebraic logic control strategies to traditional PID control to improve parameter adaptability and real-time control performance. Using MATLAB/Simulink simulation tools, a dual-loop control simulation model is established for the Stewart platform, incorporating both conventional PID control and the gain-enhanced PID control optimized by generalized Boolean algebra. Both models are configured with identical PID parameters for simulation, and comparative experimental results are presented. The experimental results demonstrate that, in the Simulink simulation model of the Stewart platform's pose control system, the proposed gain-enhanced PID control system optimized by generalized Boolean algebra improves the dynamic performance and response speed compared to the traditional PID control system, enhancing the real-time performance and stability of the Stewart platform control.

Neuronal dynamics of cerebellum and medial prefrontal cortex in adaptive motor timing

Precise temporal control of sensorimotor coordination and adaptation is a fundamental basis of animal behavior. How different brain regions are involved in regulating the flexible temporal adaptation remains elusive. Here, we investigated the neuronal dynamics of the cerebellar interposed nucleus (IpN) and the medial prefrontal cortex (mPFC) neurons during temporal adaptation between delay eyeblink conditioning (DEC) and trace eyeblink conditioning (TEC). When mice were trained for either DEC or TEC and subsequently subjected to a new paradigm, their conditioned responses (CRs) adapted virtually instantaneously. Changes in the activity of the IpN neurons related to CR timing were prominent during DEC-to-TEC adaptation, but less so during TEC-to-DEC adaptation. In contrast, mPFC neurons could rapidly alter their modulation patterns during both adaptation paradigms. Accordingly, silencing the mPFC completely blocked the adaptation of CR timing. These results illustrate how cerebral and cerebellar mechanisms may play different roles during adaptive control of associative motor timing.

SspA is a transcriptional regulator of CRISPR adaptation in E.coli.

The CRISPR integrases Cas1-Cas2 create immunological memories of viral infection by storing phage-derived DNA in CRISPR arrays, a process known as CRISPR adaptation. A number of host factors have been shown to influence adaptation, but the full pathway from infection to a fully integrated, phage-derived sequences in the array remains incomplete. Here, we deploy a new CRISPRi-based screen to identify putative host factors that participate in CRISPR adaptation in the Escherichia coli Type I-E system. Our screen and subsequent mechanistic characterization reveal that SspA, through its role as a global transcriptional regulator of cellular stress, is required for functional CRISPR adaptation. One target of SspA is H-NS, a known repressor of CRISPR interference proteins, but we find that the role of SspA on adaptation is not H-NS-dependent. We propose a new model of CRISPR-Cas defense that includes independent cellular control of adaptation and interference by SspA.

An Analysis Comparing the Performance of Wind Energy Conversion Systems Utilising FLC Controllers

This research gives a comparative analysis of Proportional-Integral (PI) and Fuzzy Logic Control (FLC) controllers for control systems of wind energy conversion systems (WECS). The PI controller is a conventional control technique that is extensively employed due to its simplicity and efficacy in regulating system behaviour through the adjustment of proportional and integral gains. FLC, on the other hand, utilises language rules and fuzzy logic reasoning to imitate human decision-making processes, providing a flexible and adaptable control strategy. The selection between PI and fuzzy controllers is dependent on the particular demands and limitations of the control application. The comparison study evaluates the performance of PI and Fuzzy controllers in various control scenarios, specifically in the scenario of speed control of cascaded doubly fed induction generator (CDFIG). Wind energy conversion systems consist of CDFIG connected to the grid via a matrix converter or rectifier and inverter. This study will present numerical simulation results conducted using the MATLAB/Simulink program to demonstrate the feasibility and efficacy of the proposed control technique

Research and Simulation Application of Automatic Generation of Judging Curves for IoT Mobile Devices in Train Driving Operations Incorporating Hybrid Genetic Algorithms

A control system for automatically generating a train operation evaluation curve based on a hybrid genetic algorithm has been proposed in order to improve the safety of automatic train operation. The system is based on Internet of Things (IoT) mobile devices and utilizes various sensors such as accelerometers, gyroscopes, barometers, and GPS to collect real-time data on the driver’s acceleration, angular velocity, air pressure, and position, among other parameters. 5G wireless communication technology is used to achieve high-speed data transmission and real-time communication with the cloud. Based on cloud data, a spatial grid area planning model is constructed to automatically generate the train operation evaluation curve. Using a spatial dynamic programming method, the entire network of Electric Multiple Unit (EMU) trains is treated as a whole, and a dynamic model of the EMU train is constructed. The spatial area parameters of the EMU train’s automatic driving operation evaluation curve are combined with the dynamic model analysis method. By identifying and analyzing environmental parameters such as train speed and distance, the EMU train’s automatic driving operation evaluation curve is optimized. A hybrid genetic evolution learning optimization algorithm is used to fit the motion spatial parameters of the EMU train’s automatically generated driving operation evaluation curve, and a spatial behavior analysis simulation is created for the EMU train’s automatically generated control driving operation curve. Through the use of hybrid genetic evolution learning optimization technology, adaptive control and automatic driving operation curve simulation planning for the automatically generated driving operation evaluation curve of the maglev train are achieved, as well as simulation and algorithm optimization design for the automatically generated driving operation evaluation curve of the maglev train. The simulation results show that the method has good adaptability and strong automatic control capability. The experimental results demonstrate the control effect of the proposed method on energy consumption and train stopping error, indicating that the proposed method can effectively improve the parameter adjustment and offset correction ability of the evaluation curve generated by the high-speed train driving operation.

Nanoparticle biosensors for cardiovascular disease detection.

Early detection and management of cardiovascular diseases (CVDs) are crucial for patient survival and long-term health. CVD biomarkers such as cardiac Troponin-I (cTnI), N-terminal pro-brain natriuretic peptide (NT-proBNP), creatine kinase MB (CK-MB), Galectin-3 (Gal-3), etc are released into the circulation following heart muscle injury, ie, acute myocardial infarction (AMI). Biosensor technology including the use of nanoparticles can be designed to target specific biomarkers associated with CVD, enabling early detection and more rapid intervention to decrease morbidity and mortality. To date, with the combination of developed nanoparticles, several optical and electrochemical-based biosensors have successfully been used detection of CVD biomarkers. Nanomaterials, when introduced as the modifiers of sensor surfaces like electrodes and gold chips, can result in the more comprehensive and more effective immobilization of capture molecules, ie, antibodies, aptamers and other ligands, due to their large surface area. In recent years, inorganic nanoparticles have regularly been used in the production of biosensors mostly due to their excellent response intensification, adaptable functionalization chemistry, shape control, good biocompatibility, and great stability. In this review, we discuss the application of different kinds of nanoparticles for the sensitive and specific detection of CVD biomarkers.

Advanced Leader-Follower Control Strategies: Integrating Adaptation Laws with Model Predictive Control

This article investigates the intricate dynamics of the leader-follower problem within the framework of model predictive control (MPC). The study focuses on a scenario where a leader, characterized by a differential dynamic model, is diligently followed by a follower vehicle with a distinct differential dynamic model. The follower has full access to the leader's state information, facilitating real-time informed decision-making. A novel adaptation law is introduced to adjust the weighting matrix of the MPC controller, ensuring the follower approaches the leader in the tangent plane manifold by prioritizing the heading angle error. The control strategy is designed to synchronize the follower's trajectory with that of the leader, which performs various maneuvers such as lane changes, abrupt heading angle alterations, and sudden shifts in linear velocity. The leader-follower formation control problem is thoroughly investigated across diverse scenarios, including straight-line movements, circular trajectories, and intricate S-shaped paths. Comprehensive analysis demonstrates the effectiveness of MPC and the proposed adaptation law in achieving precise and adaptable formation control, significantly enhancing the understanding of leader-follower dynamics under varying conditions. This research contributes to the field by offering a robust solution for precise and reliable formation control in dynamic environments, showcasing the potential of MPC in autonomous systems.

Disentangling the socio-natural dynamics of drought and water scarcity in Colombia's Tropical Andes

Study regionThe Guachal River Basin (GRB), a headwater of the Cauca River in Colombia's Tropical Andes. Study focusDroughts develop gradually, without clear time-space boundaries, affecting extensive geographical areas. When droughts occur, they exacerbate existing water scarcity, aggravating the negative impacts on vulnerable populations and ecosystems. The drivers of these phenomena are complex, ranging from natural to anthropogenic, and their impacts are cumulative and not structural. This research investigates the socio-natural dynamics of drought and water scarcity in the GRB. We explore these dynamics through secondary data review and stakeholder interviews, capturing perceptions and manifestations of droughts beyond official reported data. The Drivers-Response-Impacts framework is employed to unravel these complexities and offer insights to improve drought and water management. New hydrological insights for the regionOur research reveals that water scarcity in the GRB primarily results from land use changes and water overconsumption by the local elite, who have transitioned to sugarcane farming. ENSO-driven droughts further exacerbate water shortages in the GRB. Policy responses to drought and water scarcity are often ineffective and reactive, addressing only immediate symptoms rather than long-term drivers, such as the role of the elite in perpetuating scarcity. We explore several strategies to enhance water management: exploring new drought indicators, creating comprehensive drought damage inventories, implementing adaptable demand control mechanisms targeting high-volume users, and enhancing stakeholder participation in decision-making processes.

A novel Zero Back Power Flow (ZBPF) controlled DAB for DC bus stability and energy storage integrations in hybrid DC/AC off-grid systems

The paper presents an innovative approach for integrating energy storage devices into hybrid AC/DC grids to ensure a consistent power supply for modern loads. It introduces a Zero Back Power Flow (ZBPF) enabled bi-directional Dual Active Bridge (or, Direct AC-AC Boost) (DAB) controller strategy aimed at enhancing the power handling capacity DABs, while offering adaptable control for the microgrid system. Utilizing the proposed ZBPF control technique, the system endeavors to improve the power quality of the high-frequency AC link and regulate power transfer across DAB converter. Furthermore, a power management control strategy is described to facilitate efficient power flow among the diverse sources and loads within the hybrid off-grid system via battery system. By incorporating multiple utility features, the system aims to maintain stable bus voltages and mitigate potential power supply reliability issues. The paper concludes with simulation results validating the efficacy of the proposed ZBPF DAB control algorithms in grid forming operational modes, demonstrating the feasibility and effectiveness of the proposed solution. Additionally, the proposed ZBPF controller is tested on a hardware platform using batteries. An experimental setup has been conducted to verify the operation of the proposed ZBPF modulation scheme for a DAB converter in a practical setting. It involves implementing the ZBPF modulation scheme using a Hardware-in-the-Loop (HIL) setup and testing the DAB converter under both forward and reverse power flow modes.

Optimizing adaptability and rational control strategies for cyclogyro systems

Optimizing adaptability and rational control strategies for cyclogyro systems

Robust Deep Reinforcement Learning in Autonomous Car Path Planning

The rapid advancements in autonomous vehicle technology have emphasized the need for adaptable and dependable control systems. Deep Reinforcement Learning (DRL) has emerged as a key method to address the complexity involved in autonomous driving. This research explores how DRL can enhance path planning and control mechanisms in self-driving vehicles, with a particular focus on its ability to handle dynamic traffic environments. The challenges in training DRL models, including generalization, safety, and real-time decision-making, are analyzed. The paper also suggests potential research directions for improving DRL's application in autonomous driving, offering insights into its strengths and limitations in the current technological landscape

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.

BehaviorMate: An Intranet of Things Approach for Adaptable Control of Behavioral and Navigation-Based Experiments.

Investigators conducting behavioral experiments often need precise control over the timing of the delivery of stimuli to subjects and to collect the precise times of the subsequent behavioral responses. Furthermore, investigators want fine-tuned control over how various multi-modal cues are presented. behaviorMate takes an "Intranet of Things" approach, using a networked system of hardware and software components for achieving these goals. The system outputs a file with integrated timestamp-event pairs that investigators can then format and process using their own analysis pipelines. We present an overview of the electronic components and GUI application that make up behaviorMate as well as mechanical designs for compatible experimental rigs to provide the reader with the ability to set up their own system. A wide variety of paradigms are supported, including goal-oriented learning, random foraging, and context switching. We demonstrate behaviorMate's utility and reliability with a range of use cases from several published studies and benchmark tests. Finally, we present experimental validation demonstrating different modalities of hippocampal place field studies. Both treadmill with burlap belt and virtual reality with running wheel paradigms were performed to confirm the efficacy and flexibility of the approach. Previous solutions rely on proprietary systems that may have large upfront costs or present frameworks that require customized software to be developed. behaviorMate uses open-source software and a flexible configuration system to mitigate both concerns. behaviorMate has a proven record for head-fixed imaging experiments and could be easily adopted for task control in a variety of experimental situations.

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.

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.

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.