Adaptive Management Research Articles

Risk Assessment of Bird Collisions with a Wind Turbine Based on Flight Parameters

The study addresses the challenge of bird collisions with wind turbines by developing an autonomous risk assessment method. The research uses data from the stereoscopic Bird Protection System (BPS) to anticipate potential collision threats by analysing flight parameters and distance from turbines. The danger factor depends on the flight characteristics of the identified bird species and the parameters of the wind turbine control system. The paper proposes an online quantitative risk assessment model that operates in real time, with the aim of minimising unnecessary turbine shutdowns while improving bird conservation. The model is validated through field data from bird flights. The findings suggest that adaptive management of turbine operations based on real-time bird flight data can significantly reduce collision risks without compromising energy production efficiency. The research underscores the balance between ecological considerations and the economic viability of wind energy, proposing an adaptive strategy that reduces unnecessary turbine stoppages while ensuring the safety of avian species.

Microservice-based digital twin system towards smart manufacturing

Digital Twin (DT) is a promising technology that offers versatile services to enhance manufacturing intelligence. However, the agility, reliability and analysis capabilities of existing DT services are severely challenged when applied and deployed at large-scale production lines. To address the aforementioned issues, a microservice-based DT system with redundant architecture is proposed. First, a scalable microservice-based DT system compatible with standard and tailored plug-and-play DT services is constructed for DT protocol adaptation, stream processing, information and model management. Concurrently, a generic information model is proposed to represent the entire production lifecycle from design, operation, and maintenance in a structured manner. Second, an industrial multi-task DT model is introduced, leveraging the aforementioned architecture, to effectively achieve parallel monitoring of surface roughness and tool wear. Finally, industrial manufacturing cases are introduced to verify the feasibility and effectiveness of the proposed system. The results show that heterogeneous DT data are transferred and managed reliably, with a mean absolute percentage error of 1.28% for surface roughness prediction, and 85.71% accuracy in tool wear diagnosis.

Reintroduction of spring‐run Chinook salmon in the San Joaquin River: Evaluating genetic and phenotypic effects of captive breeding

AbstractCaptive‐breeding programs are increasingly implemented to curb population declines but can have unintended negative effects on genetic diversity and phenotypes. Through an analysis of ~10 years of genetic monitoring, pedigree information, and phenotypic data, we evaluated a captive‐breeding program that uses adaptive management to reduce inbreeding and improve captive broodstock performance. A captive‐breeding program was established in 2012 to reintroduce spring‐run Chinook salmon to the southern edge of their range in California's San Joaquin River (SJR), using fish produced each year at the nearby Feather River Hatchery (FRH). We found that the SJR program adequately captured the genetic diversity of the FRH source population and that mate pairings guided by genetic relatedness reduced inbreeding. However, the SJR broodstock reared in captivity had smaller body size at maturity in comparison to the FRH source broodstock which matures at sea, but this effect disappeared when SJR juveniles were released from captivity to mature at sea. Phenotypic traits of SJR female parents also influenced reproductive performance; older mothers and those with smaller eggs had offspring with lower survival. These findings demonstrate that adaptive genetic monitoring and pedigree information can be powerful tools for reducing risks of captive breeding and evaluating program effectiveness.

Advancing urban sustainability transitions: A framework for understanding urban complexity and enhancing integrative transformations

Rapid urbanization and industrialization with their negative repercussions draw attention to the need for urban sustainability. This research built on the urban systems understanding linked with resource utilization, environmental effects, people-environment interactions, and adaptive management by integrating quantitative modelling of urban metabolism with a qualitative investigation into human responses. A hybrid methodology was applied in a typical industrial city of Jinchang, China, which exemplified a model of how complex urban dynamics link to and inform sustainability transitions. The study found that Jinchang City had taken on an unsustainable trajectory underpinned by continuous growth of material consumption, whereas a potential for sustainability transitions was also observed in decoupled negative material outputs, improved material efficiency, and environment regeneration collectively. The challenges in industrial cities have been framed in terms of development model lock-in, conceptual and institutional constraints, innovation and human capital deficiencies, and governance failure. The research proposed a government-led transformation system that integrates multi-level and multi-perspective transformations and management employing discipline insights to facilitate urban sustainability transitions. This involves governance, economic and technological innovations, social transformations, and implementations, in which urban governance and public engagement are essential elements for adapting to undesirable difficulties and exploring locally appropriate strategies. This research provided a theoretical and empirical basis along with scientific and policy implications, which contributed to the developing interdisciplinary knowledge on the understanding of complex urban systems and provided insights into real-world sustainability solutions.

Nonparametric data-driven learning algorithms for multilocation inventory systems

We study a multilocation inventory system with unknown demand distribution using a nonparametric approach. The system consists of multiple distribution centers and customer locations, where products are shipped from the distribution centers to fulfill customer demands. We propose a novel algorithm, DMLI, for adaptive inventory management. Under specific conditions, we establish that the average expected T-period regret of DMLI converges to the optimal rate of O(1/T).

Reliability and resilience of environmental flows under uncertainty: reconsidering water year types and inconsistent flow requirements in California

Abstract Environmental water allocation in California is a complex legal process involving various government agencies and stakeholders. E-flow requirements can be based on annual runoff typologies called water year types (WYTs), which dictate water volume, timing, and duration. In this study, we examined hydropower licensing documents of the major water and power projects in the Central Sierra Nevada to catalog e-flow requirements by WYT. In this study case, we identify how WYT classification systems and categories vary across and within different basins. Additionally, we assessed the impacts of climate change on hydrology, the frequency of WYTs identified, and the reliability and resilience of e-flows using future projections (2031–2060) of 10 Global Circulation Models (GCMs). We then propose a potential adaptation strategy using a 30 year moving percentiles approach to recalculate WYTs. We identified eight WYT classifications systems were identified, and their WYT distributions statistically significantly changes across all GCMs, even though most GCMs indicate no statistically significant change in hydrology. Disparities in future impacts are observed among and within hydropower projects, with some river reaches showing negative impacts on reliability and resilience. The adaptation strategy can generally boost resilience and improve reliability, but simply updating existing WYT thresholds without flexible regulatory frameworks reconsidering WYTs and e-flows thresholds, may not yield substantial improvements. Challenges in managing e-flows in California within regulatory and hydroclimatic contexts are intricate due to the lack of standardized approaches, leading to inconsistencies and potential conflicts among stakeholders, that will likely be exacerbated by climate change. Thus, we emphasize that targeted, site-specific, and adaptive management strategies are crucial, besides the need for a harmonized and consistent approach to defining and applying WYT categories and methods and/or e-flow assessments.

Crafting Leaders in the Digital Age: How Adaptive Management Strategies Revolutionize Leadership Development in Islamic Schools

Crafting Leaders in the Digital Age: How Adaptive Management Strategies Revolutionize Leadership Development in Islamic Schools

Long life spans can mitigate the genetic effects of strays from temporary conservation hatchery programs.

While conservation fish hatchery programs can be a valuable tool providing demographic support, they can also cause domestication, risking unintended fitness consequences to both target and connected populations. When conservation hatcheries are used over a fixed amount of time, the interaction between program duration and species life history might determine the scale of fitness effects and the effectiveness of mitigation measures. We develop a mathematical model to quantify the effectiveness of approaches to mitigate unintended fitness consequences in such temporary conservation hatchery programs. We parameterize our model to represent a conservation aquaculture-based recovery program for white sturgeon (Acipenser transmontatus) in the Nechako River (British Columbia), which might impose genetic risks on the adjacent populations within the Fraser River. We find that, over a period spanning 50-200 years of hatchery operation, the life history characteristics of white sturgeon, particularly late age of maturity and longevity, reduce the genetic risks of conservation hatcheries when compared to shorter-lived species. The genetic impacts of the hatchery accumulate slowly providing increasing the potential for adaptive management in this system.

How changes projected by climate models can inform climate adaptation and marine sanctuary management: A collaborative prototype methodology

Coral reefs are highly important ecosystems providing habitat for biodiverse marine life and numerous benefits for humans. However they face immense risks from climate change. To date, Representative Concentration Pathway (RCP) climate models have aided global discussions on possible policy responses to adapt to change, but tailored climate projections at a useful scale for environmental managers are often prohibitively expensive to produce. Our research addresses this problem by presenting a novel type of collaborative, participatory research that integrates 1) site specific climate metrics from the Community Earth System Model version 2 large ensemble (CESM2-LE), 2) ecosystem response models to determine Degree Heating Months and coral bleaching impacts, and 3) collaborative social science data from environmental manager engagement to see how managers in one of the most visited marine sanctuaries in the world are enacting adaptive governance, stewarding reefs through climate impacts of the future. Our research is valuable to decision-makers seeking opportunities for innovative policy responses to climate impacts focused on experimentation and dialogue.

Plant Pathology - Crossing the Boundaries with Novel Approaches and Perspectives for Scientists

The review "Plant Pathology: Crossing the Boundaries: Novel Approaches and Perspectives for Scientists" explores the evolving field of plant pathology, emphasizing the necessity for innovative strategies and interdisciplinary collaboration to address the challenges posed by plant diseases. It highlights the integration of cutting-edge technologies, such as genomics and omics techniques, with ecological perspectives to better understand disease dynamics and develop sustainable management solutions. Traditional methods are insufficient to tackle the complex nature of plant diseases that threaten agriculture and ecosystems. The review points to the transformative potential of advanced technologies. Genomics offers deep insights into the genetic structures of pathogens and their interactions with host plants, crucial for identifying disease-resistant varieties and developing targeted treatments. Omics techniques, including transcriptomics, proteomics, and metabolomics, provide comprehensive views of molecular changes during plant-pathogen interactions, aiding in the identification of biomarkers for early disease detection and understanding mechanisms of plant resistance and susceptibility. Moreover, the review underscores the importance of an ecological approach to plant pathology. Understanding disease dynamics in the context of ecological systems reveals how environmental factors like climate change and biodiversity influence the emergence and spread of diseases. This ecological perspective is essential for developing robust and adaptable disease management strategies. The review advocates for crossing disciplinary boundaries and promoting collaboration among scientists from diverse fields. Such interdisciplinary efforts are crucial for advancing understanding and creating effective control strategies. Collaboration among molecular biologists, ecologists, agronomists, and other specialists can lead to innovative solutions addressing the root causes of plant diseases and reducing their impact on agriculture and natural ecosystems. In summary, the review emphasizes the need for novel approaches that combine cutting-edge technologies with ecological insights. Interdisciplinary collaboration is essential to enhance the understanding of plant diseases and to develop sustainable management practices. This comprehensive approach is vital for ensuring food security and maintaining ecosystem resilience in the face of emerging plant disease threats. The integration of these innovative strategies aims to meet the global demand for sustainable agricultural productivity and the health of natural ecosystems.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

A hybrid Bi-level management framework for caching and communication in Edge-AI enabled IoT

The proliferation of IoT devices has led to a surge in network traffic, resulting in higher energy usage and response delays. In-network caching has emerged as a viable solution to address this issue. However, caching IoT data faces two key challenges: the transient nature of IoT content and the unknown spatiotemporal content popularity. Additionally, the use of a global view on dynamic IoT networks is problematic due to the high communication overhead involved. To tackle these challenges, this paper presents an adaptive management approach that jointly optimizes caching and communication in IoT networks using a novel bi-level control method called BC3. The approach employs two types of controllers: a global ILP-based optimal controller for long-term timeslots and local learning-based controllers for short-term timeslots. The long-term controller periodically establishes a global cache policy for the network and sends specific cache rules to each edge server. The local controller at each edge server solves the joint problem of bandwidth allocation and cache adaptation using deep reinforcement learning (DRL) technique. The main objective is to minimize energy consumption and system response time with utilizing the global and local observations. Experimental results demonstrate that the proposed approach increases cache hit rate by approximately 12% and uses 11% less energy compared to the other methods. Increasing the cache hit rate can lead to a reduction in about 17% in response time for user requests. Our bi-level control approach offers a promising solution for leveraging the network's global visibility while balancing communication overhead (as energy consumption) against system performance. Additionally, the proposed method has the lowest cache eviction, around 19% lower than the lowest eviction of the other comparison methods. The eviction metric is a metric to evaluate the effectiveness of adaptive caching approach designed for transient data.

Probabilistic economic analysis of a weather-based adaptive disease management strategy-the case of myrtle rust in New Zealand nurseries

Probabilistic economic analysis of a weather-based adaptive disease management strategy-the case of myrtle rust in New Zealand nurseries

Neuromorphic engineering in GaN HEMTs exploiting dendritic dislocations for neuromodulation behaviors and adaptive intelligent power forecasting systems

Gallium nitride (GaN), a prominent III-V semiconductor, plays a pivotal role in the advancement of sophisticated power systems. This study presents an innovative neuromorphic engineering approach utilizing the unique dendritic dislocations extending from bulk to channel within GaN High Electron Mobility Transistors (HEMTs) for three-dimensional charge modulation. This approach dynamically alters charge states and channel potentials in response to multiple parametric stimuli, effectively mimicking neural processing and transmission functions, as well as emulating direct and indirect neuromodulation behaviors observed in human nervous system. The advanced neuromodulation emulation enhances biomimetic diversity, laying a solid foundation for the design of intelligent systems. Comprehensive material and electrical analyses reveal the charging and discharging behaviors of the defect energy levels, underlying the neuromorphic working mechanisms. Additionally, by incorporating artificial neural network algorithms, an intelligent power forecasting system that leverages the device’s diverse neuromorphic traits is developed for real-time and adaptive power management, optimizing efficiency and accuracy in balancing supply and demand, thereby enhancing the cost-effectiveness and sustainability of power systems. The findings highlight the neuromorphic capabilities of GaN devices and open avenues for further exploration into the intelligent power management.

Using adaptive cycles and panarchy to understand processes of touristification and gentrification in Valencia, Spain

This paper takes a multi-level approach to gain a better understanding of (tourism) gentrification and tourism excesses in three popular tourist neighbourhoods in Valencia, Spain. This city radically changed tourism policies in 2015, from a top-down approach that was focused on economic growth, towards one in which localhood and community development are stimulated. However, the change has done little to mitigate issues related to high levels of gentrification and touristification. This issue has been investigated using adaptive cycles and panarchy as a framework. Using these concepts has helped clarify how current policies mainly stimulate bottom-up innovations to overcome the lack of creative capacity at the local level (in other words, the ‘poverty trap’). Yet they insufficiently address processes that relate to the worldviews and higher governance levels that contribute to maintaining the current economic growth-oriented tourism ecosystem (the ‘rigidity trap’). The implications of our findings and ways forward conclude the paper.

Trends of maximum annual sea surface temperature in the Eastern China Seas

The increasing ocean warming due to climate change significantly threatens regional marine ecosystems by raising the frequency and severity of extreme temperature events. This study examines patterns and trends of maximum annual sea surface temperature (Tmax) in the Eastern China Seas from 1985 to 2022. The results show a significant warming trend in Tmax, exceeding the global average, with notable differences between southern and northern regions. The northern Tmax warming rate is faster, with occurrence times significantly advancing, while the southern Tmax warming rate is slower, with occurrence times significantly delayed. The southern Tmax and its timing are closely correlated with the annual maximum air temperature and its timing. In the north, Tmax timing is influenced by latent heat flux (QLH); a significant increase in August QLH inhibits the continued rise of SST, causing Tmax to advance. The study also highlights a significant increase in marine heatwaves at Tmax timing, with higher Tmax indicating a higher occurrence probability. By elucidating these Tmax trends and dynamics, our study enhances understanding of regional climate impacts, supporting targeted conservation efforts and adaptive ecosystem management strategies in the Eastern China Seas.

Cormorant management in Denmark: A retrospective analysis using the Systems Approach Framework

Integrated Coastal Management (ICM) aims to ensure sustainable management of coastal areas. The Systems Approach Framework (SAF) provides a structure for an ICM process that includes scenario simulations of management options for complex systems. Through the SAF stakeholders are identified and engaged at an early stage. Further, the SAF guides a process to assess the ecological sustainability, economic efficiency, and social equity of management options, even for complex issues. Implementation of the European Union's Birds Directive (BD) in 1979 instigated protective management plans to protect a waning population of cormorant (Phalacrocorax carbo sinensis). Since then, multiple adjustments were made to the management plans to mitigate arising issues and conflicts. In this study, we used the SAF to conduct a retrospective analysis of cormorant management in Europe, Denmark, and a local community in order to identify which ICM elements were put into effect and their effectiveness. The analysis comprised the two initial steps of the SAF: Issue Identification and System Design. Data and information were extracted from EU and Danish management plans, available literature and interviews with stakeholders. The management process was divided into two periods related to the motivation: i), 1979–2002 focusing on cormorant protection promoted by nature conservations organisations, and ii), 2003–2020 focusing on ad hoc adaptive management to mitigate negative effects of an exponentially increasing cormorant population on other sectors and related income and activities. Our study revealed that cormorant management at all levels lacked elements important for a sustainable ICM process. Implementation of the BD was a top-down process leading to a single sector solution, influenced by a few highly organised stakeholders with strong views. The ecological issue of protecting the species was resolved, but the lack of Systems Thinking and specific success targets, for which indicators could have been developed, led to new conflicts and negative user perceptions. In response to increasing and new emerging conflicts, more stakeholders became involved during the second period. Thorough mapping of stakeholders, institutions and governance structures at the outset could have provided for a more transparent, inclusive, and participatory process. In addition, the management plans did not involve cross-sectorial, bio-economic assessments. The latter would have allowed deliberations of potential ecological and socio-economic consequences of different management options, which could have enhanced transparency and equity in the decision-making and thus could have facilitated compliance and long-term sustainability during implementation.

A unified approach to long-term population monitoring of grizzly bears in the Greater Yellowstone Ecosystem

Long-term wildlife research and monitoring programs strive to maintain consistent data collections and analytical methods. Incorporating new techniques is important but can render data sets incongruent and limit their potential to discern trends in demographic parameters. Integrated population models (IPMs) can address these limitations by combining data sources that may span different periods into a unified statistical framework while providing a holistic view of population dynamics. We developed an IPM in a Bayesian framework for grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem. We coupled demographic data with multiple, independent population count data to link annual changes in abundance with vital rates over 4 decades (1983–2023). Abundance increased threefold from an estimated 270 individuals in 1984 to 1030 individuals in 2023. Parameter estimates indicated survival of bears ≥2 years of age was high, contributing to robust population growth during the 1980s (λ = 1.023 [50 % interquartile range = 0.993–1.082]) and 1990s (λ = 1.064 [1.023–1.103]). A slowing of population growth started around 2000 (2000s: λ = 1.030 [0.989–1.068]) and continued into the 2010s (λ = 1.021 [0.985–1.057]), due primarily to reductions in survival of bears <2 years of age. These findings corroborate previous research that identified density-dependent effects as a likely cause. The IPM framework provided greater certainty and understanding regarding the dynamic demographic characteristics of the population and serves as a powerful monitoring tool for this long-lived species. Implementation of the IPM allows timely dissemination of demographic data to help inform adaptive management strategies and policy decisions necessary for the continued management and conservation of this population. This robust and flexible monitoring system allows scientists to investigate the effects of a changing ecosystem on population dynamics, incorporate new data sources and statistical models, and respond to changes in monitoring needs for the population. We highlight the efficacy of the IPM in estimating and tracking demographic parameters for a long-lived species, while accommodating shifts in monitoring techniques and data collections typical of long-term wildlife conservation programs worldwide.

Systematic Literature Review: The Evolution of Adaptive Governance and Practice in the Context of the Environmental Crisis

This paper aims to clarify the understanding of the conceptual evolution of the adaptive governance framework, followed by an examination of implementation actions in the environmental setting. Since three approaches are recognized to be essential in navigating complex socio-ecological systems, namely adaptive management, adaptive co-management, and adaptive governance. This might lead to confusion and misunderstandings in adaptive concepts and practice. In this study, a systematic literature review was conducted using the Scopus database. A literature search was conducted in Mei 2023 using the keyword "Adaptive Governance" as the search term. As a result, despite sharing the same purpose and philosophy, each approach's work process has distinct features and scales. Adaptive governance emerges as a response to the need for holistic solutions to overcome uncertainty within the framework of complex socio-ecological systems. This is similar to adaptive co-management, although on a larger scale and context. While adaptive management is the conceptual foundation for other approaches.

Behavior Management Approaches and Teacher- Student Connectedness: A Quantitative Investigation

This study aimed to determine the influence of behavior management approaches on teacher-student connectedness among public elementary school teachers at Jesus V. Ayala Elementary School, Binugao District, Davao City Division. Fifty students were identified using the universal sampling method. The study utilized a descriptive-correlational design, employing two survey questionnaires focused on behavioral management approaches and teacher-student connectedness for data collection. Statistical tools used included mean, Pearson r, and regression analysis. Results indicated that the level of behavioral management approach was high in the democratic approach and preventive approach, and moderate in assertive discipline and intervention approaches. Regarding teacher-student connectedness, levels were moderate in individualized personal interaction and support, and high in empathy and perspective taking. The findings also revealed a strong significant relationship between behavioral management approach and teacher-student connectedness (p &lt; .05). Furthermore, democratic approach, assertive discipline approach, preventive approach, and intervention approach significantly influenced teacher-student connectedness. Specifically, for every unit increase in these domains of behavioral management approach, teacher-student connectedness increased by 1.908, holding other factors constant. Based on these findings, the study recommends that teachers engage in continuous professional development focused on adaptive behavior management strategies to effectively support student behavior and maintain a positive classroom atmosphere, thereby fostering a conducive learning environment for all.