Adaptive optimization of renewable natural resources: Solution algorithms and a computer program

Purpose of ReviewTheoretical frameworks for adaptive natural resource management are quite common, whereas documented examples showing successful implementation of adaptive management and learning through multiple time intervals have remained uncommon. Measures of quality of adaptive natural resource management processes are needed to examine potential factors driving the successful implementation. To address this gap, we developed a multimetric index composed of 22 metrics to assess quality of case studies using quantitative decision support (QDS) to inform adaptive forest management (AFM). Metrics represented three main tasks, including conceptual setup, modeling, and application. We further distinguished these into subtasks: definition of objectives and management options (setup); specifying uncertainty, prediction, and optimization (modeling); and stakeholder involvement along with practice and learning (application). We used a multimetric index to examine temporal and geographic variation in quality of reviewed case studies using QDS to inform AFM. We then conducted a structured literature review of 179 articles, wherein 34 case studies met a priori criteria.Recent FindingsWhen applying the multimetric index to these case studies, we found that over the past decade the index has been intermediate and annual average scores declined by 33% from 4.5 to 3.0 of 10 (where 10 is the highest possible quality score). Aligning with reviews of adaptive natural resource management, reported on-ground application of QDS to inform AFM was rare (n = 2). We also confirmed the expectation that there has been a substantial lack of stakeholder engagement during QDS development tasks.SummaryOur multimetric index provides a novel tool to examine gaps in the use of QDS for adaptive management in diverse domains including but not limited to forests.

Adaptive resource management (ARM) is an approach to managing that allows decision makers to learn about a system and subsequently change management actions based on new information about system processes (i.e., adapt) to better meet fundamental objectives. This is not to be confused with changing management actions when the state of the system changes. For example, changing a harvest regulation when populations decline is not ARM. This dynamic decision making may be fortuitously optimal, but if the effect of harvest is uncertain then changing regulations may be suboptimal—for example, weather may have caused the decline. Adaptive resource management can be implemented along a spectrum of passive to active to reduce system uncertainty. Active ARM is when explicit hypotheses are posited then implemented to test them, and monitoring occurs to elucidate whether the effect of the management action achieved a given result. Passive ARM uses current management practices, natural variation in the system, and monitoring to reduce uncertainty. Even though northern bobwhite (Colinus virginianus) have been studied for a century, uncertainty regarding optimal management strategies still exists. The Albany Quail Project (AQP) has used both modes of ARM to learn about northern bobwhite populations to better meet the hunting objectives of stakeholders. Between 1992 and 2019 the AQP radio-tagged 5,182 unique individuals and banded an additional 5,008 birds on the primary study area near Albany, Georgia, USA. Additionally, 1,724 nests have been monitored and population surveys conducted in the spring and autumn. Active ARM occurred with tests of supplemental feeding, hardwood cleanup, and predator control whereas passive ARM was used to learn about prescribed fire regimes, brood habitat preferences, small mammal cycles, and raptor migrations. We built an integrated population model (IPM) that combined known-fate survival data, nesting records, dead recoveries from harvest, and population surveys to model the system. The preliminary results of the IPM demonstrated that populations have increased during the project. The 5 most abundant autumn populations have occurred in the most recent 10 years of the project when bobwhite populations throughout their range have continued to decline. Unlike typical ARM applications, the AQP has not had an explicit model to predict consequences of future management actions; nonetheless, the essence of ARM has been followed. Since its inception uncertainty has been reduced, management actions have been changed, and bobwhite populations have responded. Improvements to AQP and similar projects could include explicit hypotheses and predictive models about the system

Real-time and embedded systems have traditionally been designed for closed environments where operating conditions, input workloads, and resource availability are known a priori and are subject to little or no change at runtime. There is an increasing demand, however, for autonomous capabilities in open distributed real-time and embedded (DRE) systems that execute in environments where input workload and resource availability cannot be accurately characterized a priori. These systems can benefit from autonomic computing capabilities, such as self-(re)configuration and self-optimization, that enable autonomous adaptation under varying-even unpredictable-operational conditions. A challenging problem faced by researchers and developers in enabling autonomic computing capabilities to open DRE systems involves devising adaptive planning and resource management strategies that can meet mission objectives and end-to-end quality of service (QoS) requirements of applications. To address this challenge, this paper presents the integrated planning, allocation, and control (IPAC) framework, which provides decision-theoretic planning, dynamic resource allocation, and runtime system control to provide coordinated system adaptation and enable the autonomous operation of open DRE systems. This paper presents two contributions to research on autonomic computing for open DRE systems. First, we describe the design of IPAC and show how IPAC resolves the challenges associated with the autonomous operation of a representative open DRE system case study. Second, we empirically evaluate the planning and adaptive resource management capabilities of IPAC in the context of our case study. Our experimental results demonstrate that IPAC enables the autonomous operation of open DRE systems by performing adaptive planning and management of system resources.

Support for intelligent, autonomous, adaptive and distributed resource management is a key to the success of scalable and dynamic wireless sensor network applications. Distributed independent reinforcement learning (DIRL) is a micro-learning framework that enables distributed, adaptive resource management using only local information at individual sensor nodes. In this paper we propose COllective INtelligence (COIN) a macro-learning paradigm that aims to specifically address the problem of designing utility functions for individual agents in order to achieve higher system wide utility. We extend DIRL, by combining it with COIN based macro-learning paradigm to steer the system towards global optimization, improving performance with minimal communication overheads. We present results of simulation to compare our approach with other existing approaches such as team game and DIRL in an example object tracking application. Simulation results demonstrate that a combination of micro- and macro-learners is two times more energy-efficient than micro-learners (DIRL) alone and four times more energy-efficient than macro-learners (TEAM game) alone.

Developing adaptive management guidance for groundwater planning and development

We are entering an era where species declines are occurring at their fastest ever rate, and the increased spread of non‐native species is among the top causes. High uncertainty in biological processes makes the accurate prediction of the outcomes of management interventions very challenging. Adaptive management (AM) offers solutions to reduce uncertainty and improve predictability so that the outcomes of interventions can continuously improve. We quantitatively assess the extent to which AM is used for managing vertebrates, with a focus on invasive non‐native species (INNS). Using the Web of Science, we evaluated 3992 articles returned by the search terms ‘adaptive management’ or ‘adaptive harvest management’ against seven recommended elements of AM (engagement with stakeholders, defining objectives, forecasting and estimating uncertainty, implementing management, monitoring populations, adjusting management in response to monitoring, and improving forecasting and reducing uncertainty in response to monitoring populations). The use of AM for vertebrates was reported in 56 (1%) of the evaluated studies; including four for managing INNS. Of these, ten studies excluding INNS and no studies of INNS management implemented all seven recommended elements of AM. Those elements infrequently implemented were as follows: the use of analysis or models to forecast and represent uncertainty (44%) and the feedback of monitoring data to improve forecasting and reduce uncertainty (25%). Complete active AM has rarely been implemented and reported for managing INNS, despite the significant advantages it offers. Among studies purporting to have implemented AM, most did not use analyses or models to forecast and represent uncertainty, while most defined objectives, implemented management, and monitored populations. Improvements to ongoing control programmes and much broader adoption of the AM approach are required to increase the efficiency and success of INNS management campaigns and reduce their negative impacts on native species.

The nature and peculiarities of formation of risks of economic activity of enterprise are researched. Expediency of application of instruments of adaptive risk management of economic activity of enterprise on the basis of use of the situational approach and study of the risk management system in dynamics is substantiated. A model of adaptive risk management of economic activity of enterprise on the basis of precedents has been formed, which includes subsystems of active and reactive risk management. Important components of influence on the adaptive management system are the current risk situation caused by the existing risk factors, the dynamics of risk factors development and the strategy of risk management of economic activity adopted by the enterprise, which defines the targets, priorities and prerequisites for making adaptive management decisions at the enterprise in the face of risk. The accumulation of information about the object of management within this model allows developing optimal management decisions, accumulating information about adaptive decisions in the Bank of precedents of management decisions and using them as a template for making management decisions in similar (typical) situations. It is substantiated that an important feature of the development of the system of adaptive management of economic risks at enterprise is the presence of a certain direction of transformation - the vector of development of the adaptive management system. A vector model of adaptive transformation of the risk management system at enterprise has been proposed, which contributes to a comprehensive understanding of the process of development of the system of adaptive risk management of economic activity of enterprise. Building a model of risk management at enterprise on the basis of precedents, taking into account the vector nature of its development, will contribute to a holistic understanding of the patterns of formation and transformation of the system of adaptive risk management of economic activity at enterprise. The study substantiates the feasibility of using an adaptive approach in the system of enterprise economic risk management, which is considered as a flexible open system capable of self-learning and progressive development. The use of precedents for making adaptive management decisions increases the system's ability to self-develop and more effectively adapt to the changing conditions of the external and internal environment of the enterprise.

Adaptive water resources management recognizes the centrality of learning to its effective implementation.However, the literature on learning in Adaptive Management (AM) is disjointed and lacks rigorous treatment.The purpose of this paper is to clarify how learning associated with AM is distinctive and to explain the importance of successfully integrating it into policy design.Because adaptive learning holds a unique position within the adaptive management and assessment process, it should serve as the basis for a systematic redesign of water policy processes.Adaptive environmental assessment and management processes, which highlight the role of adaptive learning, are built upon in this paper.For the purpose of this paper, adaptive learning is defined as "an ongoing process of inquiry that incorporates new knowledge with the aim to continually improve management policies".In order for adaptive learning to live with and profit from nature's dynamism, it is argued that it must be problem-centered, decision relevant, reflective, and inherently transformative.When this kind of learning becomes a fully integrated component of AM, water resource institutions have a greater likelihood of maturing into actual "learning systems".The specific objectives of this paper are to: 1) redefine what adaptive learning is, 2) explain how this particular type of learning is distinctive and 3) outline the role of adaptive learning in policy formation.Regarding policy design, the paramount issue is determining how to respond to the speed, scale, and complexity of the planetary challenges.This paper draws on a number of related areas of research to present propositions for increasing the adaptive capacity of water organizations.These research topics include: demonstrating how adaptive learning should be integrated into policy design; establishing new standards of practice that are based on adaptive learning rather than achieving 'optimal' end-game solutions; using the breakthrough views in order to transform learning into effective action; framing management problems in non-reductionist ways so that nature may serve as an ally and pilot; using shadow networks to inspire innovation, encourage institutional learning, and improve governance rules; and creating a new social reality that is more future-responsive to problems and more hospitable to new ways of thinking about water management.

Elasticity is a key feature of current cloud computing platforms. Dependent on their demand tenants can dynamically scale up and down their applications. To increase their revenue, cloud providers are used to over-provision their clusters, but they still have to reserve capacity to avoid that services get unresponsive and cause SLO violation during bursts. In this paper, we propose CLOUDFARM, a PaaS architecture with an adaptive SLO-based resource management mechanism. It introduces new flexible SLAs backed with a respective development model and management interface for end-user services. According to their SLAs and the price tenants pay, services can be selectively downgraded to overcome short-term peaks, e.g. While preparing for scale-out. Providers can deploy services optimistically and thus maximize their data center utilization and revenue.

In South Australia’s Eyre Peninsula, groundwater provides 85% of the region’s reticulated water supply. Fresh groundwater resides within shallow karstic limestone aquifers recharged by incident rainfall. Water levels are very responsive to short-term climate variability and are at risk of sustained decline due to long-term drying trends and the further rainfall declines indicated by projections of future climate, thereby increasing risk to water security and groundwater-dependent ecosystems. In 2009, a new adaptive resource management approach was enabled through legislative reform that better addresses climate variability, particularly where aquifer robustness is low. This allows the volume of water available for licensed allocations to be varied annually depending on the current condition of the aquifer resources. A three-tiered trigger level policy varies the rate at which water allocations are limited in proportion to monitored changes in groundwater storage. The three trigger thresholds are specified for each discrete groundwater resource, based on levels of risk. We now have more than five years of observations and practice of this approach to learn of its efficacy and consequences for water users, the water resources, and the environment. It has proved to be an effective way to deal with the uncertainties in how and when climate may change and how water management principles can effectively respond. Our case study provides an example of the importance of legislative reform to enable adaptive water resource management to effectively tackle the challenges of water planning in a drying climate.

Globally, river management is a multi-billion-dollar industry. The United Nations (UN) Decade of Ecosystem Restoration calls for accelerated action towards integrated, participatory, and adaptive water resources management. Here we test whether the required shifts are occurring in the Australian stream management industry, an environmental management industry in a developed western nation. We undertook structured review and topic modelling of 958 peer-reviewed papers presented at the national stream management conference from 1996-2021. We investigated trends in collaboration, transdisciplinary knowledge, diversity of input and perspectives, adaptive management, interaction with policy, and responses to natural events. We found that the industry has matured over the past 25 years, with increasing collaboration, diversity and interdisciplinarity. However, there was no measurable increase in on-ground community participation or use of adaptive management. The findings highlight opportunities for the industry to mature further to achieve UN 2030 goals for integrated water resource management and ecosystem restoration.

Compared with conventional phased array radar, co-located MIMO radar can effectively control transmitting beam width by means of sub-array division, providing greater flexibility in system resource management. To realize the effective resource management for the co-located MIMO radar in multi-target tracking, the resource management optimization model is proposed, where four working parameters can be controlled, including sub-array number, sampling period, transmitting waveform energy and working mode. The objective function integrates the average time and energy resource consumption of the system, and constraints are used to ensure that targets can be effectively detected. When solving the optimization problem, consider fully the influence of sub-array number on the other working parameters, the originally four-dimensional optimization problem is decomposed into several three-dimensional optimization problems, and finally the adaptive resource management algorithm for the co-located MIMO radar is obtained. The simulation results demonstrate that the proposed algorithm can control the working parameters of the co-located MIMO radar effectively. Furthermore, with the proposed adaptive resource management algorithm the average resource consumption of the co-located MIMO radar can be reduced effectively.

This paper describes ongoing investigations at Microsoft Research into algorithms for adaptive real-time scheduling and resource management. We are implementing a system which will allow sets of independently authored real-time applications to be scheduled, providing both initial resource negotiation and dynamic feedback to applications about their resource consumption. Instead of relying on human beings to pre-compute scheduling and resource requirements, applications will be able to dynamically adapt their run-time behavior to the resources available. Under overload conditions, user preferences and policies will be applied in order to bring the total resource requirements within available resources. This will permit chosen applications to continue executing perfectly even while others execute with dynamically renegotiated resource grants or are gracefully shut down, as per user preferences. We believe that this approach will lead to real-time scheduling and resource management algorithms which are appropriate for workstation and home multi-media environments.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Rainfed agriculture supports a large proportion of farming communities worldwide but remains highly vulnerable to climate variability, erratic rainfall, soil degradation, and resource constraints. Increasing frequency of droughts, unpredictable rainfall distribution, and temperature fluctuations threaten farm productivity and rural livelihoods in these regions. Climate adaptive crop and resource management strategies offer practical solutions to enhance resilience, improve resource-use efficiency, and sustain agricultural productivity under changing climatic conditions. This review examines climate-smart crop management practices, soil and water conservation measures, diversified farming systems, and institutional interventions that support sustainable rainfed agriculture. The article also discusses challenges associated with adoption and highlights future pathways for strengthening farm sustainability.Integrating climate-adaptive technologies with community participation and policy support can significantly enhance productivity,income stability,and resilience of rainfed farming systems

A 3-level architecting approach to adaptive resource management in mobile augmented reality systems (MARS) is elaborated, which is based on comprehensive data structuring and analyzing of their specific hard- and software features. At the conceptual modeling level an ontology of adaptive MARS resources is constructed, and at the logical modeling level a generic algorithmic model is proposed, which can be instantiated in the collection of specific methods and metrics. As a physical model the reference software architecture for adaptive resource management in MARS is designed, and this approach is implemented partly as a software prototype. It is tested successfully to solve the task of adaptive image resolution on mobile device, according to changes of computational load that finally enables better video stream quality in MARS.