Adaptive Execution Research Articles

Advancements in project management methodologies: Integrating agile and waterfall approaches for optimal outcomes

Advancements in project management methodologies have revolutionized the way organizations execute complex projects, aiming for optimal outcomes amidst evolving challenges and dynamic environments. This paper explores the integration of Agile and Waterfall approaches as a strategic response to enhance project effectiveness and efficiency. Traditionally, Waterfall methodology has been predominant in industries requiring structured planning and predictable outcomes. Its sequential phases—requirements gathering, design, implementation, testing, and deployment—ensure a methodical progression from initiation to completion. However, its rigidity in accommodating changes during the project lifecycle has been a notable limitation in fast-paced industries where flexibility and rapid adaptation are paramount. In contrast, Agile methodology emerged as a transformative alternative, particularly suited for projects with uncertain or evolving requirements. Agile emphasizes iterative development cycles, continuous stakeholder engagement, and adaptive planning. These principles enable teams to respond swiftly to changes, deliver incremental value, and maintain high levels of transparency and collaboration throughout the project. Recognizing the strengths of both methodologies, practitioners have increasingly advocated for their integration—a hybrid approach that harnesses the structured planning of Waterfall with the adaptive flexibility of Agile. This integration allows organizations to capitalize on Waterfall's clarity in project scope and deliverables while leveraging Agile's responsiveness to customer feedback and evolving market demands. Key challenges in integrating these methodologies include reconciling differences in project planning, managing stakeholder expectations across iterative cycles, and aligning team dynamics to embrace a blended approach. Effective integration strategies often involve clear delineation of phases suitable for Waterfall and Agile, fostering a culture of collaboration and continuous improvement, and utilizing project management tools that support hybrid methodologies. Successful case studies illustrate the benefits of this integrated approach, including accelerated time-to-market, improved product quality, enhanced stakeholder satisfaction, and optimized resource allocation. Moreover, organizations that adopt hybrid methodologies demonstrate resilience in navigating uncertainties and complexities, positioning themselves competitively in dynamic markets. In conclusion, the integration of Agile and Waterfall methodologies represents a paradigm shift in project management, offering a holistic framework that balances structured planning with adaptive execution. As industries continue to evolve, embracing hybrid approaches promises to drive innovation, improve project outcomes, and sustain long-term organizational success. Keywords: Advancement, Project Management, Methodologies, Integrating Agile, Waterfall Approaches.

Replica Exchange of Expanded Ensembles: A Generalized Ensemble Approach with Enhanced Flexibility and Parallelizability.

Generalized ensemble methods such as Hamiltonian replica exchange (HREX) and expanded ensemble (EE) have been shown effective in free energy calculations for various contexts, given their ability to circumvent free energy barriers via nonphysical pathways defined by states with different modified Hamiltonians. However, both HREX and EE methods come with drawbacks, such as limited flexibility in parameter specification or the lack of parallelizability for more complicated applications. To address this challenge, we present the method of replica exchange of expanded ensembles (REXEE), which integrates the principles of HREX and EE methods by periodically exchanging coordinates of EE replicas sampling different yet overlapping sets of alchemical states. With the solvation free energy calculation of anthracene and binding free energy calculation of the CB7-10 binding complex, we show that the REXEE method achieves the same level of accuracy in free energy calculations as the HREX and EE methods, while offering enhanced flexibility and parallelizability. Additionally, we examined REXEE simulations with various setups to understand how different exchange frequencies and replica configurations influence the sampling efficiency in the fixed-weight phase and the weight convergence in the weight-updating phase. The REXEE approach can be further extended to support asynchronous parallelization schemes, allowing looser communications between larger numbers of loosely coupled processors such as cloud computing and therefore promising much more scalable and adaptive executions of alchemical free energy calculations. All algorithms for the REXEE method are available in the Python package ensemble_md, which offers an interface for REXEE simulation management without modifying the source code in GROMACS.

Estimating added roadway GHG emissions from climate change effects and related adaptations

Civil infrastructure is vulnerable to climate-based event impacts that are increasing. A comparative with-and-without simulation-based GHG emissions change estimation methodology is proposed for estimating the changes in roadway transportation-related GHG emissions, a key contributor to climate change, from repairs required to mitigate damage and the execution of adaptations taken to prevent future damage. Riverine flooding from precipitation, coastal flooding from surge and high tide, and pavement deterioration from high temperatures and dry conditions are considered. A GHG emissions-based link criticality index is developed for identifying links for proactive adaptations. The tool provides insights for climate impact reduction policy creation and evaluation, and can aid in assessing the effectiveness of roadway infrastructure investment plans for combatting climate change effects. The methodology is illustrated on a case study in Northern Virginia. The results uncover how climate change events and actions taken can exacerbate conditions that accelerate climate change in the long term.

Implementation considerations for a point-of-care Neisseria gonorrhoeae rapid diagnostic test at primary healthcare level in South Africa: a qualitative study

BackgroundSouth Africa maintains an integrated health system where syndromic management of sexually transmitted infections (STI) is the standard of care. An estimated 2 million cases of Neisseria gonorrhoeae (N. gonorrhoeae) occur in South Africa every year. Point-of-care diagnostic tests (POCT) may address existing STI control limitations such as overtreatment and missed cases. Subsequently, a rapid lateral flow assay with fluorescence-based detection (NG-LFA) with a prototype reader was developed for N. gonorrhoeae detection showing excellent performance and high usability; however, a better understanding is needed for device implementation and integration into clinics.MethodsA qualitative, time-series assessment using 66 in-depth interviews was conducted among 25 trained healthcare workers involved in the implementation of the NG-LFA. Findings were informed by the Normalization Process Theory (NPT) as per relevant contextual (strategic intentions, adaptive execution, and negotiation capacity) and procedural constructs (coherence, cognitive participation, collective action, reflexive monitoring) to examine device implementation within primary healthcare levels. Interviews were audio-recorded, transcribed, and then analyzed using a thematic approach guided by NPT to interpret results.ResultsOverall, healthcare workers agreed that STI POCT could guide better STI clinical decision-making, with consideration for clinic integration such as space constraints, patient flow, and workload. Perceived NG-LFA benefits included enhanced patient receptivity and STI knowledge. Further, healthcare workers reflected on the suitability of the NG-LFA given current limitations with integrated primary care. Recommendations included sufficient STI education, and appropriate departments for first points of entry for STI screening.ConclusionsThe collective action and participation by healthcare workers in the implementation of the NG-LFA revealed adaptive execution within the current facility environment including team compositions, facility-staff receptivity, and STI management experiences. User experiences support future clinic service integration, highlighting the importance of further assessing patient-provider communication for STI care, organizational readiness, and identification of relevant departments for STI screening.

Towards a Trustworthy and Adaptive Execution of Business Process Choreographies

Towards a Trustworthy and Adaptive Execution of Business Process Choreographies

Production costs and total completion time minimization for three-stage mixed-model assembly job shop scheduling with lot streaming and batch transfer

Assembly scheduling problems have broad applications in discrete manufacturing enterprises and hence gained extensive attention in academics. However, some important and actual factors including lot streaming, batch transfer of components and mixed-model assembly are yet not well studied. To adapt to actual production and enhance competitiveness, a three-stage mixed-model assembly job shop scheduling with lot streaming and batch transfer is investigated in this work. To tackle this problem, spatial-temporal links among three stages with differentiated production lot size are established and a novel mathematical model is formulated to minimize total completion time and production costs including setup costs, transport costs, and inventory costs. Besides, an improved multi-objective coevolutionary simulated annealing algorithm is developed. To promote the coevolution of processing, transfer, and assembly sub-problems, the multi-attribute combination rule, similarity rule, and consistency rule are respectively designed for each sub-problem. An adaptive execution strategy of sub-problems is trained by Q-learning to maximize the overall benefits. Experimental results show that both the discovered rule and the adaptive strategy are effective, and the proposed algorithm significantly outperforms seven competitive multi-objective algorithms in fixing the studied problem. More importantly, the developed production mode of “processing with lot streaming-batch transfer-mixed-model assembly considering pull production” has a better comprehensive performance than the other three actual production modes.

UHTP: A User-Aware Hierarchical Task Planning Framework for Communication-Free, Mutually-Adaptive Human-Robot Collaboration

Collaborative human-robot task execution approaches require mutual adaptation, allowing both the human and robot partners to take active roles in action selection and role assignment to achieve a single shared goal. Prior works have utilized a leader-follower paradigm in which either agent must follow the actions specified by the other agent. We introduce the User-aware Hierarchical Task Planning (UHTP) framework, a communication-free human-robot collaborative approach for adaptive execution of multi-step tasks that moves beyond the leader-follower paradigm. Specifically, our approach enables the robot to observe the human, perform actions that support the human’s decisions, and actively select actions that maximize the expected efficiency of the collaborative task. In turn, the human chooses actions based on their observation of the task and the robot, without being dictated by a scheduler or the robot. We evaluate UHTP both in simulation and in a human subjects experiment of a collaborative drill assembly task. Our results show that UHTP achieves more efficient task plans and shorter task completion times than non-adaptive baselines across a wide range of human behaviors, that interacting with a UHTP-controlled robot reduces the human’s cognitive workload, and that humans prefer to work with our adaptive robot over a fixed-policy alternative.

Deimplementation in the provision of opioid agonist treatment to achieve equity of care for people engaged in treatment: a qualitative study

BackgroundDeimplementation, the removal or reduction of potentially hazardous approaches to care, is key to progressing social equity in health. While the benefits of opioid agonist treatment (OAT) are well-evidenced, wide variability in the provision of treatment attenuates positive outcomes. During the COVID-19 pandemic, OAT services deimplemented aspects of provision which had long been central to treatment in Australia; supervised dosing, urine drug screening, and frequent in-person attendance for review. This analysis explored how providers considered social inequity in health of patients in the deimplementation of restrictive OAT provision during the COVID-19 pandemic.MethodsBetween August and December 2020, semi-structured interviews were conducted with 29 OAT providers in Australia. Codes relating to the social determinants of client retention in OAT were clustered according to how providers considered deimplementation in relation to social inequities. Normalisation Process Theory was then used to analyse the clusters in relation to how providers understood their work during the COVID-19 pandemic as responding to systemic issues that condition OAT access.ResultsWe explored four overarching themes based on constructs from Normalisation Process Theory: adaptive execution, cognitive participation, normative restructuring, and sustainment. Accounts of adaptive execution demonstrated tensions between providers’ conceptions of equity and patient autonomy. Cognitive participation and normative restructuring were integral to the workability of rapid and drastic changes within the OAT services. Key transformative actors included communities of practice and “thought leaders” who had long supported deimplementation for more humane care. At this early stage of the pandemic, providers had already begun to consider how this period could inform sustainment of deimplementation. When considering a future, post-pandemic period, several providers expressed discomfort at operating with “evidence-enough” and called for narrowly defined types of data on adverse events (e.g. overdose) and expert consensus on takeaway doses.ConclusionsThe possibilities for achieving social equity in health are limited by the divergent treatment goals of providers and people receiving OAT. Sustained and equitable deimplementation of obtrusive aspects of OAT provision require co-created treatment goals, patient-centred monitoring and evaluation, and access to a supportive community of practice for providers.

Adaptive Task-Based Intermittent Computing System With Parallel State Backup

Energy harvesting promises to power billions of Internet-of-Things devices without being restricted by battery life. Since the energy harvester generally outputs weak and unstable energy, the system may suffer frequent and unpredictable power failures, thus falling into cyclically reboots without forward progress. Task-based intermittent computing system which periodically backs up system states into Non-Volatile Memory (NVM) is proposed to solve the non-progress problem, with the non-trivial cost of frequent backups. How to reduce the backup overhead becomes a major research problem for intermittent computing. Our paper, for the first time, proposes to parallelize state backup and program execution with asynchronous Direct Memory Access (DMA) to hide the backup latency into the program’s execution. But, straightforwardly executing the state backup and the program in parallel may cause inconsistent system state. In specific, the system state may be modified by the program during backup, and therefore may be backed up incorrectly and further cause the system to deliver an incorrect computation result. We make deep analysis on the system behavior and observe that, although the system state may be backed up incorrectly, the incorrect backup will be covered by the subsequent correct backups soon as the backup operations are performed frequently. In addition, only a small part of variables among all the program states may cause incorrect computation result. So, in this paper, we aggressively allow incorrect backups to occur, and propose a backup error detection method and a fault-tolerant backup management to guarantee the correctness of the system’s execution. To augment the parallel backup method, an adaptive execution method is further proposed to reduce the number of backups and balance the ratio between task execution time and backup latency. We design a run-time system to implement the proposed approach, and experimental results conducted on an STM32F7-based platform show that the proposed method can achieve a 2.6× average speedup.

Translational framework for implementation evaluation and research: a normalisation process theory coding manual for qualitative research and instrument development

BackgroundNormalisation Process Theory (NPT) is frequently used to inform qualitative research that aims to explain and evaluate processes that shape late-stage translation of innovations in the organisation and delivery of healthcare. A coding manual for qualitative researchers using NPT will facilitate transparent data analysis processes and will also reduce the cognitive and practical burden on researchers.Objectives(a) To simplify the theory for the user. (b) To describe the purposes, methods of development, and potential application of a coding manual that translates normalisation process theory (NPT) into an easily usable framework for qualitative analysis. (c) To present an NPT coding manual that is ready for use.MethodQualitative content analysis of papers and chapters that developed normalisation process theory, selection and structuring of theory constructs, and testing constructs against interview data and published empirical studies using NPT.ResultsA coding manual for NPT was developed. It consists of 12 primary NPT constructs and conforms to the Context-Mechanism-Outcome configuration of realist evaluation studies. Contexts are defined as settings in which implementation work is done, in which strategic intentions, adaptive execution, negotiating capability, and reframing organisational logics are enacted. Mechanisms are defined as the work that people do when they participate in implementation processes and include coherence-building, cognitive participation, collective action, and reflexive monitoring. Outcomes are defined as effects that make visible how things change as implementation processes proceed and include intervention mobilisation, normative restructuring, relational restructuring, and sustainment.ConclusionThe coding manual is ready to use and performs three important tasks. It consolidates several iterations of theory development, makes the application of NPT simpler for the user, and links NPT constructs to realist evaluation methods. The coding manual forms the core of a translational framework for implementation research and evaluation.

Automating Adaptive Execution Behaviors for Robot Manipulation

Robotic manipulation in semi-structured and changing environments requires systems with: a) perception and reasoning capabilities able to capture and understand the state of the environment; b) planning and replanning capabilities at both symbolic and geometric levels; c) automatic and robust execution capabilities. To cope with these issues, this paper presents a framework with the following features. First, it uses perception and ontology-based reasoning procedures to obtain the Planning Description Domain Language files that describe the manipulation problem at task level. This is used in the planning stage as well as during task execution in order to adapt to new situations, if required. Second, the proposed framework is able to plan at both task and motion levels, intertwining them by incorporating geometric reasoning modules to determine some of the symbolic predicates needed to describe the states. Finally, the framework automatically generates the behavior trees required to execute the task. The proposal takes advantage of the ability of behavior trees to be edited during run time, allowing adaptation of the action plan or of the trajectories according to changes in the state of the environment. The approach allows for robot manipulation tasks to be automatically planned and robustly executed, contributing to achieve fully functional service robots.

Fangorn

Pervasive needs for data explorations at all scales have populated modern distributed platforms with workloads of different characteristics. The growing complexities and diversities have thereafter imposed distinct challenges to execute them on shared clusters in corporate or public clouds. This paper presents Fangorn, an adaptive execution framework built on an enriched graph model. As the underlying infrastructure for core computation platforms at Alibaba, Fangorn supports various execution modes and caters to heterogeneous workloads. With the capability to orchestrate graph executions with both long-running and requested-on-demand resources at the same time, Fangorn allows exploration of tradeoffs between latency and resource efficiency, for jobs of all scales. By modeling distributed job executions as mutable graphs with pluggable components, Fangorn offers a systematic framework to adjust job executions adaptively, according to data statistics collected during run-time. Fangorn supports an array of different computation engines ranging from relational to deep learning, and is fully deployed on production clusters across Alibaba. It manages tens of millions of distributed jobs daily, with job size scaling from one to half-million.

Image Identification and Classification Using CNN

High precision video mark gauge (request) models are ascribed to colossal extension data. Because of the on-going progression on significant learning examines, Convolutional Neural Network (CNN) based procedure have bet customary article affirmation techniques with a gigantic edge. Notwithstanding, it requires extensively more memory and computational expenses appeared differently in relation to the customary techniques. Along these lines, it is hard to complete a CNN set up article affirmation system regarding a mobile phone where memory and computational power are restricted. In this we take a gander at CNN plans which are sensible for adaptable execution, and propose multi-scale sort out in-frameworks (NIN) in which customers can change the trade-off between affirmation time and precision. We realized multi-hung flexible applications on the two iOS and Android using either NEON SIMD bearings or the BLAS library for speedy computation of convolutional layers, and looked at them with respect to affirmation time on phones. At Final, it has been revealed that BLAS suits iOS, while NEON suits Android, and that reducing the picture size by resizing it is suitable for speeding up of CNN-based affirmation. In this paper, we examine an approach to arrange the pictures of cell phone and to identify/recognizes the highlights of the versatile that are prepared and put away in preparing information priory. Picture game plan is one of the zones of PC vision that is growing rapidly. By virtue of significant learning! Reliably, new calculations/models keep beating the previous ones. Honestly, one of the latest top tier programming structures for object revelation was simply released seven days prior by Facebook AI gathering. The item is considered Detectron that combines different exploration adventures for object location and is constrained by the Caffe2 significant learning framework.

Towards Autonomous Robotic Assembly: Using Combined Visual and Tactile Sensing for Adaptive Task Execution

Robotic assembly tasks are typically implemented in static settings in which parts are kept at fixed locations by making use of part holders. Very few works deal with the problem of moving parts in industrial assembly applications. However, having autonomous robots that are able to execute assembly tasks in dynamic environments could lead to more flexible facilities with reduced implementation efforts for individual products. In this paper, we present a general approach towards autonomous robotic assembly that combines visual and intrinsic tactile sensing to continuously track parts within a single Bayesian framework. Based on this, it is possible to implement object-centric assembly skills that are guided by the estimated poses of the parts, including cases where occlusions block the vision system. In particular, we investigate the application of this approach for peg-in-hole assembly. A tilt-and-align strategy is implemented using a Cartesian impedance controller, and combined with an adaptive path executor. Experimental results with multiple part combinations are provided and analyzed in detail.

Configuring adaptive PID-controllers of the automatic speed control system of the GTE

For non-stationary objects with parameters, which could be changed significantly during operation, using conventional controllers in the form of proportional-integraldifferential regulators may not provide the required quality of the system. Therefore, it is desirable to create an adaptive automatic control system with the structure and parameters of the control regulator that are purposefully changed to ensure the system adaptation, that is based on information about the properties of the object of regulation and external influences, to the changing operating conditions. The problem of designing adaptive systems is one of the most important in control theory and related fields. This is conditioned by two factors: the complexity of solving the problem as a whole and the presence of a large number of technically diverse situations that need to be adapted and optimized. In the paper, an adaptive system for the automatic control of the speed of a gas turbine engine, which includes a magnetic amplifier, a DC motor with a gearbox, a fuel supply valve and a tachogenerator, is developed. For adaptive control execution, three proportional-integral-differential controllers were proposed: "classic", fuzzy and neurofuzzy. The parameters of the "classic" controller were optimized using linear programming methods. The membership functions and the rule base were proposed for the fuzzy controller. An adaptation algorithm was selected for the neuro-fuzzy controller. Three controllers were used for three engine-operating modes: low-gas, cruiser and maximum during the computer simulation of the system. A comparative analysis of the quality of the three regulators was performed and it is based on the obtained transient characteristics. The derived results can be used in the development of automatic control systems for gas turbine engines.

Device-centric adaptive data stream management and offloading for analytics applications in future internet architectures

Information-Centric Networking (ICN) enables in-network data management and communication between multiple parties by replicating data and activating interactions between decoupled senders and receivers. Existing data management and offloading schemes in ICNs primarily use the transport layer hence it becomes inefficient to actively develop and update the ICN standards because of continuously evolving heterogeneous future internet architectures such as mobile edge cloud computing (MECC) architectures. In this paper, we present an adaptive execution model for mobile data stream mining (MDSM) applications in MECC environments to enable device-centric adaptive data management and offloading. We designed the proposed execution model considering multiple factors of complexity such as volume and velocity of continuously streaming data, the selection of data fusion and data preprocessing methods, the choice of learning models, learning rates, learning modes, mobility, limited computational and memory resources in mobile devices, the high coupling between application components, and dependency over Internet connections. We integrated the proposed execution model with multiple MDSM applications mapping to a real-word use-case for activity detection using MECC as a future network architecture. We thoroughly evaluated the proposed execution model in terms of battery power consumption, memory utilization, makespan, accuracy, and the amount of data reduced during in-network communication. The comparison showed that our proposed adaptive execution model outperformed the static and dynamic execution models which were deployed in the same ICN architecture.

Optimal Portfolio Trading: Adaptive Multi-period Trade Execution

We propose a new approach to adaptive multi-period trade execution which can be viewed as an extension of Grinold and Kahn (1995) and Almgren and Chriss (1999). Our methodology does not rely on any exogenous switching criteria but instead explicitly includes trading acceleration and deceleration depending on market conditions into equations describing the trading strategy. In other words, we consider adaptive trading strategies that (among other factors) condition on a current price and therefore are stochastic before the price is revealed. The new approach enables us to specify multi-period mean variance style utility function where expectations are taken over all possible realizations of the price process. Consequently while actual trading will still have stochastic nature, we can compute expected portfolio trading costs as well as optimal response to all market conditions on a pre-trade basis.

Vision Paper: Grand Challenges in Resilience: Autonomous System Resilience through Design and Runtime Measures

In this article, we put forward the substantial challenges in cyber resilience in the domain of autonomous systems and outline foundational solutions to address these challenges. These solutions fall into two broad themes: resilience-by-design and resilience-by-reaction . We use several application drivers from autonomous systems to motivate the challenges in cyber resilience and to demonstrate the benefit of the solutions. We focus on some autonomous systems in the near horizon (autonomous ground and aerial vehicles) and also a little more distant (autonomous rescue and relief). For resilience-by-design , we focus on design methods in software that are needed for our cyber systems to be resilient. In contrast, for resilience-by-reaction , we discuss how to make systems resilient by responding, reconfiguring, or recovering at runtime when failures happen. We also discuss the notion of adaptive execution to improve resilience, execution transparently and adaptively among available execution platforms (mobile/embedded, edge, and cloud). For each of the two themes, we survey the current state, and the desired state and ways to get there. We conclude the paper by looking at the research challenges we will have to solve in the short and the mid-term to make the vision of resilient autonomous systems a reality. This article came out of discussions that started at the NSF-sponsored Grand Challenges in Resilience Workshop held at Purdue in 2019 with the co-authors contributing to going into the depth of the issues and then this article.

Communication-Assisted Protection and Self-Healing Control Scheme for Distribution Networks Based on IEC 61850

Communication-assisted protection and self-healing control (CAPSC) is perceived as a new alternative to ensure reliable operation of distribution networks (DNs) because of its prominent advantages of information sharing. Although the application of CAPSC based on IEC 61850 has been verified in substations, it has not yet been effectively promoted in DNs. To fully exert its advantages in DNs, a novel scheme of IEC 61850-based CAPSC is proposed in this paper. The proposed scheme enable samplings to be synchronized by propagation delay compensation without external clocks. Adaptive execution algorithms of protection and self-healing control are introduced considering changeable topology and configuration conditions. The coordination between protection and self-healing control in this scheme is simplified by component state descriptions instead of event signals. The scheme proposed in this paper has been incorporated in an engineering field application. A closed-loop test has been conducted on a real-time digital simulator (RTDS) according to the field connections and parameters to verify the rationality and effectiveness of the proposed scheme.

Occurrence and Functions of m6A and Other Covalent Modifications in Plant mRNA.

Posttranscriptional control of gene expression is indispensable for the execution of developmental programs and environmental adaptation. Among the many cellular mechanisms that regulate mRNA fate, covalent nucleotide modification has emerged as a major way of controlling the processing, localization, stability, and translatability of mRNAs. This powerful mechanism is conserved across eukaryotes and controls the cellular events that lead to development and growth. As in other eukaryotes, N 6-methylation of adenosine is the most abundant and best studied mRNA modification in flowering plants. It is essential for embryonic and postembryonic plant development and it affects growth rate and stress responses, including susceptibility to plant RNA viruses. Although the mRNA modification field is young, the intense interest triggered by its involvement in stem cell differentiation and cancer has led to rapid advances in understanding how mRNA modifications control gene expression in mammalian systems. An equivalent effort from plant molecular biologists has been lagging behind, but recent work in Arabidopsis (Arabidopsis thaliana) and other plant species is starting to give insights into how this essential layer of posttranscriptional regulation works in plants, and both similarities and differences with other eukaryotes are emerging. In this Update, we summarize, connect, and evaluate the experimental work that supports our current knowledge of the biochemistry, molecular mechanisms, and biological functions of mRNA modifications in plants. We devote particular attention to N 6-methylation of adenosine and attempt to place the knowledge gained from plant studies within the context of a more general framework derived from studies in other eukaryotes.