System Architecture Research Articles

A power-aware vision-based virtual sensor for real-time edge computing

Graphics processing units and tensor processing units coupled with tiny machine learning models deployed on edge devices are revolutionizing computer vision and real-time tracking systems. However, edge devices pose tight resource and power constraints. This paper proposes a real-time vision-based virtual sensors paradigm to provide power-aware multi-object tracking at the edge while preserving tracking accuracy and enhancing privacy. We thoroughly describe our proposed system architecture, focusing on the Dynamic Inference Power Manager (DIPM). Our proposed DIPM is based on an adaptive frame rate to provide energy savings. We implement and deploy the virtual sensor and the DIPM on the NVIDIA Jetson Nano edge platform to prove the effectiveness and efficiency of the proposed solution. The results of extensive experiments demonstrate that the proposed virtual sensor can achieve a reduction in energy consumption of about 36% in videos with relatively low dynamicity and about 21% in more dynamic video content while simultaneously maintaining tracking accuracy within a range of less than 1.2%.

Optimal deployment of private 5G multi-access edge computing systems at smart factories: Using hybrid crow search algorithm

In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities.

Lithofacies, architecture and evolution of carbonate tidal delta system: Implications for reservoir heterogeneity (mid-cretaceous Mishrif Formation, Southeast Iraq)

Carbonate tidal delta system used to be put much less attention on unlike the counterparts in siliciclastic systems, and its unique architecture influences the reservoir heterogeneity and corresponding development strategy significantly. This study aims to recognize and characterize the carbonate tidal delta system of Mishrif Formation in Southeast Iraq.Based on detailed observation of cores from 5 wells and integrated analysis on well-loggings and 3D seismic, five lithofacies associations were recognized: (1) subtidal mudstone to wackestone (LF-1); (2) back-margin massive wackestone to packstone (LF-2); (3) high-energy bioclastic grainstone (LF-3); (4) swamp carbonaceous mudstone interbedded with bioclastic wackestone (LF-4); and (5) incision brown mudstone to wackestone with chert nodular (LF-5).A detailed description of the recognized lithofacies and facies association of the system is given in this work, together with an interpretation of the corresponding depositional setting. The architecture of the reservoir bodies has been reconstructed in detail and placed within a high-frequency sequence-stratigraphic framework.The studied interval belongs to the highstand system tract (HST) of a single composite sequence and comprises two shallowing-upward high-frequency sequences. The internal architecture of the tidal delta system is the result of complex tidal-dominated hydrodynamics.Facies and diagenetic partitioning with each high-frequency sequences are primary controls on pore types and reservoir properties. Original lithofacies texture and subsequent meteoric dissolution resulted in regional barriers and high permeable streaks that created a complex internal architecture of reservoir bodies. The shape of reservoir bodies generally follows those of the lithofacies, but with the addition of intercalated thin flow barriers.

CNS Delivery Systems Based on Nanotechnology with Particular Attention to Parkinson's and Alzheimer's Disease –A Review

Abstract:: Nanotechnology has emerged as a promising frontier in the field of drug delivery, offering unprecedented opportunities for precise and targeted interventions in neurodegenerative disorders, such as Parkinson's and Alzheimer's diseases. This review explores recent advancements in nanotechnology-based delivery systems designed to enhance drug transport across the blood-brain barrier and facilitate targeted drug delivery to the central nervous system. The unique challenges posed by the intricate central nervous system architecture, particularly in the context of Parkinson's and Alzheimer's diseases, necessitate innovative solutions to improve therapeutic outcomes. The review comprehensively discusses various nanocarrier platforms, including liposomes, polymeric nanoparticles, dendrimers, and exosomes, and their potential applications in drug delivery to the central nervous system (CNS). Special emphasis is placed on addressing the multifaceted challenges associated with neurodegenerative diseases, such as the need for sustained drug release, protection of therapeutic agents from enzymatic degradation, and the efficient crossing of the blood-brain barrier. Furthermore, the review highlights recent breakthroughs in nanotechnology-driven diagnostic and therapeutic approaches, ranging from early detection of disease biomarkers to the targeted delivery of neuroprotective agents. The potential of personalized medicine and the integration of advanced imaging techniques with nanotechnology for precise disease monitoring are also discussed. In conclusion, this review provides a comprehensive overview of recent advancements in nanotechnologybased delivery systems tailored for CNS targeting, with a specific focus on Parkinson's and Alzheimer's diseases. The synthesis of current research findings underscores the transformative potential of nanotechnology in reshaping the landscape of therapeutic strategies for neurodegenerative disorders, offering hope for improved patient outcomes and the development of novel treatment modalities.

CADCare: Smart System for CHD Identification & Sensor Alerts

INTRODUCTION: Cardiovascular diseases, particularly coronary artery disease (CAD), present a global health challenge, necessitating effective detection and diagnosis methods for early intervention. Various machine learning and deep learning approaches have emerged, utilizing diverse data sources such as electrocardiogram (ECG) signals and clinical features to enhance CAD detection. Additionally, circadian heart rate variability (HRV) has been explored as a potential diagnostic marker for CAD severity. This research aims to contribute to the burgeoning field of medical AI and its application in cardiology.OBJECTIVES: This study seeks to develop a Comprehensive Coronary Artery Disease Detection System integrating real-time heart rate monitoring and CAD prediction via an Android application. The objectives include seamless data transmission, efficient cloud-based data management, and the utilization of AI models, including ANNs, CNNs for ECG images, and hybrid models combining clinical and ECG data, to improve early CAD detection and management.METHODS: The system architecture involves integrating key sensors, an Arduino microcontroller, a Bluetooth module, and AI models to facilitate early CAD detection. An Android application complements the system, offering heart rate monitoring and CAD prediction using various data sources. Cloud computing is employed for efficient data management and analysis.RESULTS: The developed system successfully integrates cutting-edge technology to enhance CAD detection, achieving accurate and efficient results in real-time heart rate monitoring and CAD prediction.CONCLUSION: The Comprehensive Coronary Artery Disease Detection System, leveraging AI and cloud computing, contributes to proactive health monitoring and informed decision-making in CAD management and prevention, thereby addressing a critical need in cardiovascular health care.

Genome-wide association studies of root system architecture traits in a broad collection of Brassica genotypes.

The root systems of Brassica species are complex. Eight root system architecture (RSA) traits, including total root length, total root surface area, root average diameter, number of tips, total primary root length, total lateral root length, total tertiary root length, and basal link length, were phenotyped across 379 accessions representing six Brassica species (B. napus, B. juncea, B. carinata, B. oleracea, B. nigra, and B. rapa) using a semi-hydroponic system and image analysis software. The results suggest that, among the assessed species, B. napus and B. oleracea had the most intricate and largest root systems, while B. nigra exhibited the smallest roots. The two species B. juncea and B. carinata shared comparable root system complexity and had root systems with larger root diameters. In addition, 313 of the Brassica accessions were genotyped using a 19K Brassica single nucleotide polymorphism (SNP) array. After filtering by TASSEL 5.0, 6,213 SNP markers, comprising 5,103 markers on the A-genome (covering 302,504 kb) and 1,110 markers on the C-genome (covering 452,764 kb), were selected for genome-wide association studies (GWAS). Two general linear models were tested to identify the genomic regions and SNPs associated with the RSA traits. GWAS identified 79 significant SNP markers associated with the eight RSA traits investigated. These markers were distributed across the 18 chromosomes of B. napus, except for chromosome C06. Sixty-five markers were located on the A-genome, and 14 on the C-genome. Furthermore, the major marker-trait associations (MTAs)/quantitative trait loci (QTLs) associated with root traits were located on chromosomes A02, A03, and A06. Brassica accessions with distinct RSA traits were identified, which could hold functional, adaptive, evolutionary, environmental, pathological, and breeding significance.

Modeling Technical Risk Propagation Using Field-Effects in Automotive Technology Infusion Design Studies

Abstract The infusion of new technology into existing product architectures introduces significant challenges, particularly in managing the risks associated with higher-order effects on system functionality and reliability. Within current industrial practices, the development of such integration support is challenging as it requires integrating data related to component and system testing activities, and product architecture design. There is a need to make decisions based on incomplete data due to the difficulty of evaluating all potential configurations, as well as to proactively identify areas of the product architecture that could be problematic in the future. This study aims to support the development of new products at a prominent international automotive company, by examining the field effects generated and influenced by their components and the impact of infusing new technology on the system architecture. We propose integrating and linking these concerns about field effects and architectural impact within a DSM-based method. The method has been implemented in a case study based on the introduction of a steer-by-wire system. The method is proven effective for finding relevant configurations for integrating new technology into products under development. Thus, a robust framework is proposed for harmonizing the evolution of products with the evolution of technology, fostering innovation within product platform design.

Statistical reliability analysis of marine systems with varied levels of redundancy

In designing autonomous vessels for long-duration independent operation, maintaining the performance of machinery systems without human intervention is a key challenge. Designers are faced with a range of potential system architecture choices but have little guidance on which will be optimal. Working only with high-reliability components can increase the probability of completing a voyage successfully, though the availability of such components may be limited. Alternatively, designers can select a redundant architecture to provide options for reconfiguration if a component fails during a voyage, such architectures typically have weight, space, and cost implications. This work presents a parametric exploration of the probability of system failures over time under different architectures. The reliability of individual components is expressed through exponential probability distributions and the weight of each component is approximated. Two systems are presented and the effectiveness of various architectures for both systems is compared. A simple design penalty function is also tracked to capture the different architectures’ weight and implication number of components. From this study optimal architectures for long-term autonomous missions are proposed.

The Development and Utilization of an Electronic Assessment Software in Dental Education: A Comprehensive Evaluation

Purpose/ObjectivesThis article introduces the Key Electronic Assessment Platform (KEAP), an electronic-based assessment platform created in-house at the College of Dentistry (COD), King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia. It highlights the platform’s integral value and demand within the educational system. MethodsThe article introduces the KEAP system design and architecture, describes its features, tests, and implementation in addition to its maintenance and security. It also presents the outcomes of KEAP utilization and the level of users’ satisfaction. ResultsThe KEAP item bank has been expanded by more than 26,500 questions over the last four academic years and 1,255 exams have been administered. KEAP provides a highly secure structured framework for assessment planning, conduction, and evaluation. More than 90% of students and faculty are satisfied or very satisfied with their experience using the KEAP system indicating that it is well accepted by the end users. ConclusionsThe development of KEAP is based on contextual factors in COD, KSAU-HS with the aim of creating a process for assessment that is valid, efficient, standardized, and highly secure. KEAP’s design and successful implementation can provide a successful example of implementing electronic-based assessment in higher education.

Electrode module for EIT with a robust howland current source

Electrical Impedance Tomography (EIT) is a non-invasive imaging technique that reconstructs internal conductivity distributions of a body from electrical measurements taken on its boundary. This study contributes to the field by focusing on the technological intricacies of absolute EIT imaging, which is challenged by limitations such as the resolution capacity of the hardware and the complexities introduced by imaging capacitive bodies. The novel EIT system architecture proposed enhances the accuracy of measurement by integrating current sources and Analog-to-Digital Converters (ADCs) closer to the electrodes, employing alternating current excitations to accurately capture phase information. This system uses a dynamic arrangement of surface electrodes that continuously alter their roles between current injection and voltage measurement, in a synchronized sequence, to ensure the accuracy of the measurements. The paper describes the design and implementation of both the excitation and measurement subsystems, highlighting the use of digital signal demodulation near the electrode to reduce data transfer issues. Experimental results confirm the system’s capability for real-time image reconstruction at 50 frames per second with precision in phase delay measurements, suggesting significant potential for clinical and industrial applications. Future work will aim to further refine signal generation with higher-speed DACs and expand to image reconstruction with more channels.

LoRa Network-based IoT Node for indoor environment monitoring

The concept of the Internet of Things (IoT), just by its name, presents itself as a network of physical objects, which are incorporated into a vast array of sensors, software, and any other type of technology, with the purpose of connecting and exchanging information with other devices and systems via the Internet. As we know it, the IoT is present in a wide range of devices ranging from simple household objects to complex tools spread across all sectors of industry. The means of communication used in IoT devices is based on Long Range (LoRa) technology. This technology stands out for its ability to operate with low energy consumption, which is vital for the use of IoT devices since they run on batteries. This paper will look at the development of an indoor space monitoring system based on an IoT Node made up of sensors supported by the LoRa network. To achieve the objectives, an in-depth study of the technology used in LoRa networks is carried out, followed by a presentation of the system's architecture and then the various tests carried out in indoor spaces to assess the functioning of both the sensors and the network itself. During the work, numerous tests were carried out over extended periods, which will be duly detailed throughout the document. These are used to ensure the stability of the monitoring system and gauge the environmental status of the space where it is installed.

Synaptic device-based neuromorphic computing in artificial intelligence

The application of synaptic device-based neuromorphic computing in artificial intelligence is an emerging research field aimed at simulating the structure and function of the human brain and realizing high-efficiency, low-power, and adaptive intelligent computing. This paper reviews the principles, growth and challenges of neuromorphic devices based on synapses computing and its applications and perspectives in artificial intelligence fields like an image processing as well as natural language processing. The paper first introduces the basic concepts, properties and classification of synaptic devices, as well as the basic framework and algorithms of neuromorphic computing. Then, the paper analyzes the advantages and difficulties of neuromorphic computing based on synaptic devices, including the preparation, testing, modelling and integration of the devices, as well as the systems architecture, programming and optimization. Then, this paper gives examples of the applications and effects of synaptic device-based neuromorphic computing in artificial intelligence fields such as image processing and natural language processing, including image denoising, image segmentation, image recognition, text classification, text summarization, and text generation. Finally, this paper summarizes the current research status and future synaptic device-based neuromorphic computing trends. It puts forward some research directions and suggestions to promote the development and innovation in this field.

Understanding Human Cognition Through Computational Modeling.

One important goal of cognitive science is to understand the mind in terms of its representational and computational capacities, where computational modeling plays an essential role in providing theoretical explanations and predictions of human behavior and mental phenomena. In my research, I have been using computational modeling, together with behavioral experiments and cognitive neuroscience methods, to investigate the information processing mechanisms underlying learning and visual cognition in terms of perceptual representation and attention strategy. In perceptual representation, I have used neural network models to understand how the split architecture in the human visual system influences visual cognition, and to examine perceptual representation development as the results of expertise. In attention strategy, I have developed the Eye Movement analysis with Hidden Markov Models method for quantifying eye movement pattern and consistency using both spatial and temporal information, which has led to novel findings across disciplines not discoverable using traditional methods. By integrating it with deep neural networks (DNN), I have developed DNN+HMM to account for eye movement strategy learning in human visual cognition. The understanding of the human mind through computational modeling also facilitates research on artificial intelligence's (AI) comparability with human cognition, which can in turn help explainable AI systems infer humans' belief on AI's operations and provide human-centered explanations to enhance human-AI interaction and mutual understanding. Together, these demonstrate the essential role of computational modeling methods in providing theoretical accounts of the human mind as well as its interaction with its environment and AI systems.

Proto.IoT: supporting IoT prototype ideation for non-experts in the classroom

ABSTRACT This paper introduces Proto.IoT, an IoT prototyping toolkit designed to complement teaching in Higher Education (HE) by enabling non-expert users to learn IoT concepts through making within time-constrained lecture settings. The significance of Proto.IoT lies in its abstraction of hardware and software implementations, facilitating accessibility for non-experts through a visual rule-based programming environment and plug-and-play approaches. A pilot study, involving 25 IoT developers from the UK and Malaysia, evaluated the IoT back-end system architecture, revealing that Proto.IoT was perceived as user-friendly and effective in reducing development time. The main evaluation focused on the toolkit's front-end rule-based graphical user interface (GUI), engaging 41 undergraduate students. Results indicated that the toolkit supported prototype ideation and was intuitive to use. These findings illustrate the potential of IoT prototyping toolkits to complement traditional lectures, fostering rapid prototyping and ideation, thus facilitating maker education within formal classroom settings.

Solar–Hydrogen Storage System: Architecture and Integration Design of University Energy Management Systems

As a case study on sustainable energy use in educational institutions, this study examines the design and integration of a solar–hydrogen storage system within the energy management framework of Kangwon National University’s Samcheok Campus. This paper provides an extensive analysis of the architecture and integrated design of such a system, which is necessary given the increasing focus on renewable energy sources and the requirement for effective energy management. This study starts with a survey of the literature on hydrogen storage techniques, solar energy storage technologies, and current university energy management systems. In order to pinpoint areas in need of improvement and chances for progress, it also looks at earlier research on solar–hydrogen storage systems. This study’s methodology describes the system architecture, which includes fuel cell integration, electrolysis for hydrogen production, solar energy harvesting, hydrogen storage, and an energy management system customized for the needs of the university. This research explores the energy consumption characteristics of the Samcheok Campus of Kangwon National University and provides recommendations for the scalability and scale of the suggested system by designing three architecture systems of microgrids with EMS Optimization for solar–hydrogen, hybrid solar–hydrogen, and energy storage. To guarantee effective and safe functioning, control strategies and safety considerations are also covered. Prototype creation, testing, and validation are all part of the implementation process, which ends with a thorough case study of the solar–hydrogen storage system’s integration into the university’s energy grid. The effectiveness of the system, its effect on campus energy consumption patterns, its financial sustainability, and comparisons with conventional energy management systems are all assessed in the findings and discussion section. Problems that arise during implementation are addressed along with suggested fixes, and directions for further research—such as scalability issues and technology developments—are indicated. This study sheds important light on the viability and efficiency of solar–hydrogen storage systems in academic environments, particularly with regard to accomplishing sustainable energy objectives.

How the presence of a giant planet affects the outcome of terrestrial planet formation simulations

The architecture and masses of planetary systems in the habitable zone could be strongly influenced by the presence of outer giant planets. Here, we investigate the impact of outer giants on terrestrial planet formation, under the assumption that the final assembly of the planetary system is set by a giant impact phase. Utilizing a state-of-the-art N-body simulation software, GENGA, we interpret how the late stage of terrestrial planet formation contributes to diversity among planetary systems. We designed two global model setups: 1) we placed a gas giant on the outer side of planetesimals and embryo disk and 2) we only included planetesimals and embryos, but no giant. For the model including the outer giant, we studied the effect of different giant initial masses in the range of 1.0-3.0 Jupiter masses, as well as a range of orbital radii from 2.0-5.8 AU. We also studied the influence of different initial positions of planetesimals and embryos on the results. Our N-body simulation time is approximately 50 Myr. The results show that the existence of an outer giant will promote the interaction between planetesimals and embryos, making the orbits of the formed terrestrial planets more compact. However, placing the giant planet too close to the planetesimals and embryo disk suppresses the formation of massive rocky planets. In addition, under the classical theory, where planetary embryos and planetesimals collide to form terrestrial planets, our results show that the presence of a giant planet actually decreases the gap complexity of the inner planetary system.

Seedling root system adaptation to water availability during maize domestication and global expansion.

The maize root system has been reshaped by indirect selection during global adaptation to new agricultural environments. In this study, we characterized the root systems of more than 9,000 global maize accessions and its wild relatives, defining the geographical signature and genomic basis of variation in seminal root number. We demonstrate that seminal root number has increased during maize domestication followed by a decrease in response to limited water availability in locally adapted varieties. By combining environmental and phenotypic association analyses with linkage mapping, we identified genes linking environmental variation and seminal root number. Functional characterization of the transcription factor ZmHb77 and in silico root modeling provides evidence that reshaping root system architecture by reducing the number of seminal roots and promoting lateral root density is beneficial for the resilience of maize seedlings to drought.

Transcriptional changes during crown-root development and emergence in barley (Hordeum vulgare L.)

BackgroundRoots play an important role during plant growth and development, ensuring water and nutrient uptake. Understanding the mechanisms regulating their initiation and development opens doors towards root system architecture engineering.ResultsHere, we investigated by RNA-seq analysis the changes in gene expression in the barley stem base of 1 day-after-germination (DAG) and 10DAG seedlings when crown roots are formed. We identified 2,333 genes whose expression was lower in the stem base of 10DAG seedlings compared to 1DAG seedlings. Those genes were mostly related to basal cellular activity such as cell cycle organization, protein biosynthesis, chromatin organization, cytoskeleton organization or nucleotide metabolism. In opposite, 2,932 genes showed up-regulation in the stem base of 10DAG seedlings compared to 1DAG seedlings, and their function was related to phytohormone action, solute transport, redox homeostasis, protein modification, secondary metabolism. Our results highlighted genes that are likely involved in the different steps of crown root formation from initiation to primordia differentiation and emergence, and revealed the activation of different hormonal pathways during this process.ConclusionsThis whole transcriptomic study is the first study aiming at understanding the molecular mechanisms controlling crown root development in barley. The results shed light on crown root emergence that is likely associated with a strong cell wall modification, death of the cells covering the crown root primordium, and the production of defense molecules that might prevent pathogen infection at the site of root emergence.

Aguhyper: a hyperledger-based electronic health record management framework

The increasing importance of healthcare records, particularly given the emergence of new diseases, emphasizes the need for secure electronic storage and dissemination. With these records dispersed across diverse healthcare entities, their physical maintenance proves to be excessively time-consuming. The prevalent management of electronic healthcare records (EHRs) presents inherent security vulnerabilities, including susceptibility to attacks and potential breaches orchestrated by malicious actors. To tackle these challenges, this article introduces AguHyper, a secure storage and sharing solution for EHRs built on a permissioned blockchain framework. AguHyper utilizes Hyperledger Fabric and the InterPlanetary Distributed File System (IPFS). Hyperledger Fabric establishes the blockchain network, while IPFS manages the off-chain storage of encrypted data, with hash values securely stored within the blockchain. Focusing on security, privacy, scalability, and data integrity, AguHyper’s decentralized architecture eliminates single points of failure and ensures transparency for all network participants. The study develops a prototype to address gaps identified in prior research, providing insights into blockchain technology applications in healthcare. Detailed analyses of system architecture, AguHyper’s implementation configurations, and performance assessments with diverse datasets are provided. The experimental setup incorporates CouchDB and the Raft consensus mechanism, enabling a thorough comparison of system performance against existing studies in terms of throughput and latency. This contributes significantly to a comprehensive evaluation of the proposed solution and offers a unique perspective on existing literature in the field.

Plant strategy of root system architecture and exudates for acquiring soil nutrients

AbstractVascular plants develop diverse root system architectures and exudates to facilitate acquisition of soil nutrients (nitrogen and phosphorus). Plant species have inherently specific properties of root system architectures and exudates, but some plants exhibit high plasticity to respond to spatiotemporal variations in soil nutrient forms and availability. This paper synthesizes evidence on how plant species diversity and root plasticity contribute to soil nutrient mobilization and uptake in plants from the tropics to the Arctic with varying plant species diversity. The development of finer roots in the surface soil is a well‐known strategy for the acquisition of limited nutrients (especially phosphorus), but the allocation of roots foraging “nutrient hotspots” in deeper soil (podzolic soil or permafrost‐affected soils) is an alternative strategy for coniferous trees tested in the Arctic and boreal forests. This contrasts with findings in tropical forests, where diverse plant species with different capacities of organic acid exudation coexist and species composition shifts in response to phosphorus deficiency. In particular, high malate exudation from roots and rhizosphere microbes stimulates phosphorus solubilization, aluminum detoxification, and lignin degradation in acidic soils. The diversity and plasticity of the root system architecture, root exudation, and the flexibility of nutrient sources mitigate nutrient limitation in soil. Root plasticity facilitating soil nutrient acquisition has a large impact on biogeochemistry and soil formation, such as podzolization, in the long term.