Adaptation Control Research Articles

Multi-omic Analyses Shed Light on The Genetic Control of High-altitude Adaptation in Sheep.

Sheep were domesticated in the Fertile Crescent and then spread globally, where they have been encountering various environmental conditions. The Tibetan sheep has adapted to high altitudes on the Qinghai-Tibet Plateau over the past 3000 years. To explore genomic variants associated with high-altitude adaptation in Tibetan sheep, we analyzed Illumina short-reads of 994 whole genomes representing ∼ 60 sheep breeds/populations at varied altitudes, PacBio High fidelity (HiFi) reads of 13 breeds, and 96 transcriptomes from 12 sheep organs. Association testing between the inhabited altitudes and 34,298,967 variants was conducted to investigate the genetic architecture of altitude adaptation. Highly accurate HiFi reads were used to complement the current ovine reference assembly at the most significantly associated β-globin locus and to validate the presence of two haplotypes A and B among 13 sheep breeds. The haplotype A carried two homologous gene clusters: (1) HBE1, HBE2, HBB-like, and HBBC, and (2) HBE1-like, HBE2-like, HBB-like, and HBB; while the haplotype B lacked the first cluster. The high-altitude sheep showed highly frequent or nearly fixed haplotype A, while the low-altitude sheep dominated by haplotype B. We further demonstrated that sheep with haplotype A had an increased hemoglobin-O2 affinity compared with those carrying haplotype B. Another highly associated genomic region contained the EGLN1 gene which showed varied expression between high-altitude and low-altitude sheep. Our results provide evidence that the rapid adaptive evolution of advantageous alleles play an important role in facilitating the environmental adaptation of Tibetan sheep.

Human-in-the-Loop Cooperative Control of a Walking Exoskeleton for Following Time-Variable Human Intention.

This article presents a human-in-the-loop cooperative control of a walking exoskeleton to provide assistance to the user and enhance human mobility. First, a dynamic mathematical model of the human-exoskeleton system is derived, and then, a human-in-the-loop cooperative control framework is proposed in two ways: separable cooperative control (SCC) and interactive cooperative control (ICC), respectively. The SCC introduces a space division of the human and the exoskeleton, while the ICC allows the robot to perceive the human intention and follows human motor, thereby improving the collaborative performance. The ICC formulates the impedance connection between the human and the exoskeleton in the divided orthogonal subspaces of walking, such that the robot is able to modify its position of center of mass (COM) when its motor trajectory deviates the one of the human. In addition, a novel adaptation control is proposed to deal with the unmodeled dynamics and trajectory tracking. Finally, to validate the effectiveness of our proposed controller, a series of experiments are conducted in three adults in gait at different speeds. It shows that the proposed controller can preserve the periodic walking gait and inherit the robustness of dealing with perturbations during walking.

The drivers of intraspecific trait variation and their implications for future tree productivity and survival.

Forests are facing unprecedented levels of stress from pest and disease outbreaks, disturbance, fragmentation, development, and a changing climate. These selective agents act to alter forest composition from regional to cellular levels. Thus, a central challenge for understanding how forests will be impacted by future change is how to integrate across scales of biology. Phenotype, or an observable trait, is the product of an individual's genes (G) and the environment in which an organism lives (E). To date, researchers have detailed how environment drives variation in tree phenotypes over long time periods (e.g., long-term ecological research sites [LTERs]) and across large spatial scales (e.g., flux network). In parallel, researchers have discovered the genes and pathways that govern phenotypes, finding high degrees of genetic control and signatures of local adaptation in many plant traits. However, the research in these two areas remain largely independent of each other, hindering our ability to generate accurate predictions of plant response to environment, an increasingly urgent need given threats to forest systems. I present the importance of both genes and environment in determining tree responses to climate stress. I highlight why the difference between G versus E in driving variation is critical for our understanding of climate responses, then propose means of accelerating research that examines G and E simultaneously by leveraging existing long-term, large-scale phenotypic data sets from ecological networks and adding newly affordable sequence (-omics) data to both drill down to find the genes and alleles influencing phenotypes and scale up to find how patterns of demography and local adaptation may influence future response to change.

Transmitter Adaptation and Wireless Power Control for Capsule Endoscopy

Transmitter Adaptation and Wireless Power Control for Capsule Endoscopy

ADAPTIVE PROCESS MANAGEMENT USING DEEP LEARNING ON A PROGRAMMABLE LOGIC CONTROLLER (PLC)

In the era of Industry 4.0, the integration of advanced digital technologies into manufacturing processes has become paramount for enhancing operational efficiency and adaptability. This study introduces a groundbreaking approach to adaptive process management through the integration of deep learning algorithms within Programmable Logic Controllers (PLCs), thus addressing the limitations of traditional PLCs in dynamically adjusting to new operational conditions without manual intervention. By leveraging the inherent capabilities of deep learning for realtime data analysis and decision-making, this research develops a novel framework that enables PLCs to autonomously learn from process data, adapt control strategies in real-time, and optimize manufacturing operations. The methodology encompasses the design and implementation of deep learning models tailored for PLC environments, the development of a data-driven learning mechanism directly on the PLC, and a comprehensive evaluation of the system’s adaptability, efficiency, and performance in real-world industrial settings. The findings reveal significant improvements in process efficiency, reduction in downtime, and enhanced adaptability to changing operational conditions, demonstrating the potential of combining deep learning with PLC-based systems for fostering intelligent and flexible manufacturing processes. This study not only provides a viable solution to the challenges of static PLC programming but also opens new ways for research and development in smart manufacturing technologies, offering insights into the practical implications of deploying intelligent automation systems in Industry 4.0.

Wind turbine emulator using three-phase IM controlled through an adaptive reactive power estimator fed by soft-VSI topology

PurposeThe purpose of this paper aims to design a robust wind turbine emulator (WTE) based on a three-phase induction motor (3PIM).Design/methodology/approachThe 3PIM is driven by a soft voltage source inverter (VSI) controlled by a specific space vector modulation. By adjusting the appropriate vector sequence selection, the desired VSI output voltage allows a real wind turbine speed emulation in the laboratory, taking into account the wind profile, static and dynamic behaviors and parametric variations for theoretical and then experimental analysis. A Mexican hat profile and a sinusoidal profile are therefore used as the wind speed system input to highlight the electrical, mechanical and electromagnetic system response.FindingsThe simulation results, based on relative error data, show that the proposed reactive power control method effectively estimates the flux and the rotor time constant, thus ensuring an accurate trajectory tracking of the wind speed for the wind emulation application.Originality/valueThe proposed architecture achieves its results through the use of mathematical theory and WTE topology combine with an online adaptive estimator and Lyapunov stability adaptation control methods. These approaches are particularly relevant for low-cost or low-power alternative current (AC) motor drives in the field of renewable energy emulation. It has the advantage of eliminating the need for expensive and unreliable position transducers, thereby increasing the emulator drive life. A comparative analysis was also carried out to highlight the online adaptive estimator fast response time and accuracy.

Analysis of inertia, damping, and synchronization characteristics in grid-connected photovoltaic systems with fuzzy logic control

The integration of renewable energy sources (RES) into DC-distributed power systems (DC-DPSS) is gaining traction as a sustainable energy solution. However, the inherent variability of RES output can introduce instability into the grid, posing challenges for maintaining system reliability and stability. Fuzzy logic controllers (FLCs) have emerged as a promising approach to mitigate these instability issues, offering a robust and adaptable control strategy that can effectively handle the complexities of DC-DPSS. This paper examines the application of FLCs in DC-DPSS, exploring their effectiveness in addressing instability caused by RES fluctuations. FLCs are a control system that leverages fuzzy logic, a form of logic that utilizes linguistic variables to represent uncertainty, make decisions, and improve the stability of DC-distributed power systems. The research analyzes various system parameters, including inertia, damping, and synchronization characteristics, using a static synchronous generator (SSG) model. The study builds upon prior findings by adding a fuzzy logic controller to the existing system. The results showed better performance which resulted in improved inertia, damping, and synchronization characteristics. The efficiency of the proposed controller is demonstrated using MATLAB/Simulink.

Clinical laboratory strategies for managing pandemic outbreaks

This review article comprehensively examines clinical laboratory strategies for managing pandemic outbreaks, with a particular focus on lessons learned from the COVID-19 pandemic. Drawing upon an extensive analysis of existing literature from databases such as PubMed, Web of Science, and Cochrane, as well as manual searches on Google Scholar, the study aims to provide insights into key aspects of pandemic preparedness and response. The literature review encompasses diverse combinations of medical terminology, emphasizing the role and response of clinical laboratories during pandemics. The primary focus areas include rapid test development and deployment, increased testing capacity, sample collection, and transportation protocols, data management, personnel safety, resource allocation, adaptability, and quality control measures. The inclusion of articles in this study adheres to rigorous criteria, ensuring a thorough and robust review process. The findings highlight the critical importance of rapid and accurate diagnostic testing, especially in the early stages of a pandemic, to facilitate timely interventions and containment. Efficient sample collection and transportation systems are crucial for maintaining the integrity of specimens, while advanced data management and reporting systems contribute to informed decision-making. Adaptability and flexibility are identified as indispensable traits for navigating the dynamic challenges of a pandemic, and stringent quality control measures are foundational for upholding the accuracy of diagnostic testing. The review underscores the collaborative efforts needed among clinical laboratories, public health agencies, and research institutions to strengthen global pandemic preparedness and response strategies.

A coordinated restoration method of three-phase AC unbalanced distribution network with DC connections and mobile energy storage systems

In the rapidly changing domain of hybrid AC/DC urban distribution networks, this research unveils a groundbreaking method for the restoration of three-phase unbalanced systems by astutely harnessing the unique potential of DC line interconnections. At the heart of this innovation lies the synthesis of symmetrical Second Order Cone Programming (SOCP) with a sophisticated topology search technique, a union that offers a precise depiction of complex three-phase power flow with network restoration while simultaneously accelerating computational processes. Building upon this foundation, our approach places significant emphasis on the utilization of adaptable DC power control, coupled with the optimal deployment of mobile energy storage systems (MESSs), to ensure a harmonized power balance during critical interruptions. These strategies converge to prioritize the restoration of vital loads, especially those with high weighting factors, thereby significantly augmenting the network’s resilience, particularly in contexts vulnerable to disasters. The corroborative numerical results, as delineated in our study, highlight the distinct advantage and effectiveness of our methodology over prevailing practices in fortifying grid resilience against serious adversities.

Evaluating the Effectiveness and Transferability of a Data-Driven Two-Region Perimeter Control Method Using Microsimulation

Regional perimeter control based on the existence of macroscopic fundamental diagrams has been widely studied as an effective tool to regulate traffic and prevent oversaturation in dense urban areas. Significant research efforts have been performed concerning the modeling aspects of perimeter control. More recently, data-driven techniques for perimeter control have shown remarkable promise; however, few studies have examined the transferability of these techniques. While it is surely of the highest priority to devise effective perimeter control methods, the ability of such methods to transfer the learned knowledge and quickly adapt control policies to a new setting is critical, particularly in real-life situations where training a method from scratch is intractable. This work seeks to bridge this research gap by comprehensively examining the effectiveness and transferability of a reinforcement-learning-based perimeter control method for a two-region urban network in a microsimulation setting. The results suggest: 1) the presented data-driven method demonstrates promising control effectiveness in comparison with no perimeter control and an extended greedy controller and 2) the method can readily transfer its learned knowledge and adapt its control policy with newly collected data to simulation settings with different traffic demands, driving behaviors, or both.

The impact of Caputo-Fabrizio fractional derivative and the dynamics of noise on worm propagation in wireless IoT networks

The main objective of this article on Wireless sensor network of the Internet of Things (IoT). The wireless network, B bluetooth network, infrared network, and other networks are the key components of the Internet of Things (IoT). The major emphasis of this work was on the impact of Caputo-Fabrizio fractional derivative on worm propagation in heterogeneous susceptible-exposed-infected-recovered wireless IoT devices. We first determined the equilibrium points and fundamental reproduction number for the Caputo-Fabrizio HSEIR system, and then we discussed the stability of the system at the worm propagation equilibrium point. Using the Picard-Lindeof method, we determine the existence and unique solution for the fractional CF system of the heterogeneous SEIR model. Next, we use fixed point theory to judge the stability of the iterative method. We investigate the impact of the derivative order on the behaviour of the resultant functions and acquired numerical values by computing the model's findings for various fractional orders. In addition, we compute the integer-order model's results and contrast them with the results of the fractional-order model. We develop a periodically intermittent controller driven by white noise with the amazing benefits of reduced cost and more adaptable control technique to restrict the spread of worms in wireless IoT networks. To clearly define the conditions for stability in probability one, we employ the stochastic analysis technique. Our results show that the nonlinear worm propagation system may be stabilised by intermittent stochastic perturbation under the parameters of intermittent time linked to stochastic perturbation strength. Our theoretical conclusions may be used to analyse the observable processes of the worm, design countermeasures to prevent its spread, and evaluate the consequences of various system parameters.

Anticipatory postural control in adaptation of goal-directed lower extremity movements.

Skilled football players can adapt their kicking movements depending on external environments. Predictive postural control movements, known as anticipatory postural adjustments (APAs), are needed preceding kicking movements to precisely control them while maintaining a standing posture only with the support leg. We aimed to clarify APAs of the support leg in the process of adaptation of goal-directed movements with the lower limb. Participants replicated ball-kicking movements such that they reached a cursor, representing a kicking-foot position towards a forward target while standing with the support leg. APAs were observed as the centre of pressure of the support leg shifted approximately 300ms in advance of the onset of movement of the kicking foot. When the cursor trajectory of the kicking foot was visually rotated during the task, the kicking-foot movement was gradually modified to reach the target, indicating adaptation to the novel visuomotor environment. Interestingly, APAs in the mediolateral direction were also altered following the change in kicking-foot movements. Additionally, the APAs modified more slowly than the kicking-foot movements. These results suggest that flexible changes in predictive postural control might support the adaptation of goal-directed movements of the lower limb.

Older adults and individuals with Parkinson's disease control posture along suborthogonal directions that deviate from the traditional anteroposterior and mediolateral directions.

A rich and complex temporal structure of variability in postural sway characterizes healthy and adaptable postural control. However, neurodegenerative disorders such as Parkinson's disease, which often manifest as tremors, rigidity, and bradykinesia, disrupt this healthy variability. This study examined postural sway in young and older adults, including individuals with Parkinson's disease, under different upright standing conditions to investigate the potential connection between the temporal structure of variability in postural sway and Parkinsonism. A novel and innovative method called oriented fractal scaling component analysis was employed. This method involves decomposing the two-dimensional center of pressure (CoP) planar trajectories to pinpoint the directions associated with minimal and maximal temporal correlations in postural sway. As a result, it facilitates a comprehensive assessment of the directional characteristics within the temporal structure of sway variability. The results demonstrated that healthy young adults control posture along two orthogonal directions closely aligned with the traditional anatomical anteroposterior (AP) and mediolateral (ML) axes. In contrast, older adults and individuals with Parkinson's disease controlled posture along suborthogonal directions that significantly deviate from the AP and ML axes. These findings suggest that the altered temporal structure of sway variability is evident in individuals with Parkinson's disease and underlies postural deficits, surpassing what can be explained solely by the natural aging process.

Pelatihan Peningkatan Kualitas Pengawas Internal Koperasi di Provinsi Kalimantan Barat

In an era of economic dynamism and increasingly stringent regulatory demands increasingly stringent regulatory demands, internal supervisors in the context of cooperatives have a very important role in ensuring smooth operations, compliance with role in ensuring smooth operations, compliance with regulations, and effective risk management. rules, and effective risk management. Supervision is a key function of goal-oriented management in other words, supervision is very important to achieve the goals of an organization. important to achieve the goals of an organization. Cooperatives as unique economic entities unique economic entity has its own characteristics, which require reliable and adaptable internal control that is reliable and able to adapt to the dynamics of the business environment. business environment. This condition emphasizes the need to continuously improve quality of internal supervisors to be able to respond to external and internal changes optimally. internal changes optimally. There are several aspects that need to be considered by cooperative supervisors to improve the quality of cooperative internal supervisors, namely regarding compliance and ethics, good governance for cooperatives and risk management. risk management. With the training to improve the quality of cooperative internal supervisors It is expected that cooperative supervisors have managerial, technical, and entrepreneurial skills, so that the entrepreneurial spirit, so that cooperative management reflects a characteristic based on cooperative principles. based on cooperative principles.

Hierarchal attribute based cryptographic model to handle security services in cloud environment: a new model

The sharing of information in the cloud is a unique element of the environment, but there is a risk that the information may land with the wrong people. To counterattack this problem, security-associated methodologies were used to secure the information that was readily available to clients. Despite the lack of benefits, this provides productive/adaptability and dependability in access control strategies between clients in the sharing of information. The novel hierarchal attribute-based cryptographic security model (NHACSM) is being proposed to provide adaptability, versatility, and access control in sharing information in the appropriate climate. This model allows clients to share information in a hierarchal way, allowing for a productive assessment of access control strategy and improved security. The NHACSM method is used to reduce the total time values for different user instances compared to conventional approaches, for example, attribute-set-based encryption (ASBE), key-policy attribute-based encryption (KP-ABE), and ciphertext-policy attribute-based encryption (CP-ABE). With respect to 10 instances existing methods achieve 2.7, 2.5, and 2.3 respectively, and also compared to 20, 30, 40, and 50 instances, our proposed method is low. The encryption and decryption time evaluation values and performance evaluation of different approaches, ASBE, CP-ABE, were taken into account when increasing the user instance.

Towards better IT governance: A comprehensive IT internal control model for information technology firms

Purpose- The primary objective of this study is to enhance IT governance within the information technology sector by developing a comprehensive IT Internal Control Model. This model is designed to address the unique and evolving challenges in IT internal control processes, providing a structured approach to risk optimization, resource optimization, and benefit management. By introducing this model, the study aims to offer IT firms a robust internal control model that can be adapted to their specific operational needs, promoting a more effective, efficient, and resilient IT governance structure. Methodology- A thorough literature review, incorporating a wide array of international sources, forms the foundation of this study. The research involved designing an IT Internal Control Model, informed by the processes observed at THY Technology. This model was then applied across 12 distinct operational processes, and feedback from stakeholders was systematically gathered through surveys to evaluate its practicality and impact. Findings- The feedback from stakeholders was predominantly positive, underscoring the model’s efficacy in bolstering IT governance. The model was praised for its thoroughness and adaptability, successfully addressing a broad spectrum of IT internal control issues while remaining flexible enough to be tailored to various IT environments. Conclusion- This study highlights the importance of dynamic and adaptable IT internal control model in the fast-paced technology sector. The developed IT Internal Control Model opens new avenues for research into effective governance practices and provides a valuable blueprint for IT firms seeking to enhance their governangfce and control mechanisms in a digitally-driven world. Keywords: IT internal control model, internal control, IT governance, IT compliance, continuous improvement JEL Codes: M15, M19, M42

Measuring stomatal and guard cell metrics for plant physiology and growth using StoManager1.

Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing geometrical, mathematical algorithms, and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics, including guard cell and stomatal area, length, width, stomatal aperture area/guard cell area, orientation, stomatal evenness, divergence, and aggregation index. Combined with leaf functional traits, some of these StoManager1-measured guard cell and stomatal metrics explained 90% and 82% of tree biomass and intrinsic water use efficiency (iWUE) variances in hardwoods, making them substantial factors in leaf physiology and tree growth. StoManager1 demonstrated exceptional precision and recall (mAP@0.5 over 0.96), effectively capturing diverse stomatal properties across over 100 species. StoManager1 facilitates the automation of measuring leaf stomatal and guard cells, enabling broader exploration of stomatal control in plant growth and adaptation to environmental stress and climate change. This has implications for global gross primary productivity (GPP) modeling and estimation, as integrating stomatal metrics can enhance predictions of plant growth and resource usage worldwide. Easily accessible open-source code and standalone Windows executable applications are available on a GitHub repository (https://github.com/JiaxinWang123/StoManager1) and Zenodo (https://doi.org/10.5281/zenodo.7686022).

An Architecture and Review of Intelligence Based Traffic Control System for Smart Cities

City traffic congestion can be reduced with the help of adaptable traffic signal control system. The technique improves the efficiency of traffic operations on urban road networks by quickly adjusting the timing of signal values to account for seasonal variations and brief turns in traffic demand. This study looks into how adaptive signal control systems have evolved over time, their technical features, the state of adaptive control research today, and Control solutions for diverse traffic flows composed of linked and autonomous vehicles. This paper finally came to the conclusion that the ability of smart cities to generate vast volumes of information, Artificial Intelligence (AI) approaches that have recently been developed are of interest because they have the power to transform unstructured data into meaningful information to support decision-making (For instance, using current traffic information to adjust traffic lights based on actual traffic circumstances). It will demand a lot of processing power and is not easy to construct these AI applications. Unique computer hardware/technologies are required since some smart city applications require quick responses. In order to achieve the greatest energy savings and QoS, it focuses on the deployment of virtual machines in software-defined data centers. Review of the accuracy vs. latency trade-off for deep learning-based service decisions regarding offloading while providing the best QoS at the edge using compression techniques. During the past, computationally demanding tasks have been handled by cloud computing infrastructures. A promising computer infrastructure is already available and thanks to the new edge computing advancement, which is capable of meeting the needs of tomorrow's smart cities.

Profiling of the gene expression and alternative splicing landscapes of Eucalyptus grandis.

Eucalyptus is a widely planted hardwood tree speciesdue to its fast growth, superior wood properties and adaptability. However, the post-transcriptional regulatory mechanisms controlling tissue development and stress responses in Eucalyptus remain poorly understood. In this study, we performed a comprehensive analysis of the gene expression profile and the alternative splicing (AS) landscape of E. grandis using strand-specific RNA-Seq, which encompassed 201 libraries including different organs, developmental stages, and environmental stresses. We identified 10 416 genes (33.49%) that underwent AS, and numerous differentially expressed and/or differential AS genes involved in critical biological processes, such as primary-to-secondary growth transition of stems, adventitious root formation, aging and responses to phosphorus- or boron-deficiency. Co-expression analysis of AS events and gene expression patterns highlighted the potential upstream regulatory role of AS events in multiple processes. Additionally, we highlighted the lignin biosynthetic pathway to showcase the potential regulatory functions of AS events in the KNAT3 and IRL3 genes within this pathway. Our high-quality expression atlas and AS landscape serve as valuable resources for unravelling the genetic control of woody plant development, long-term adaptation, and understanding transcriptional diversity in Eucalyptus. Researchers can conveniently access these resources through the interactive ePlant browser (https://bar.utoronto.ca/eplant_eucalyptus).

Reinforcement learning of adaptive control strategies

Humans can up- or downregulate the degree to which they rely on task information for goal-directed behaviour, a process often referred to as cognitive control. Adjustments in cognitive control are traditionally studied in response to experienced or expected task-rule conflict. However, recent theories suggest that people can also learn to adapt control settings through reinforcement. Across three preregistered task switching experiments (n = 415), we selectively rewarded correct performance on trials with either more (incongruent) or less (congruent) task-rule conflict. Results confirmed the hypothesis that people rewarded more on incongruent trials showed smaller task-rule congruency effects, thus optimally adapting their control settings to the reward scheme. Using drift diffusion modelling, we further show that this reinforcement of cognitive control may occur through conflict-dependent within-trial adjustments of response thresholds after conflict detection. Together, our findings suggest that, while people remain more efficient at learning stimulus-response associations through reinforcement, they can similarly learn cognitive control strategies through reinforcement.