Adaptive trust management framework in P2P grid computing

The aim of the study was to investigate the application and importance of Learning and Adaptive Management by the Ministry of Women Affairs, Community, Small and Medium Enterprises Development (MWACSMED) and beneficiaries when implementing development programs in Zaka district. The study investigated the objectives of the programs implemented by MWACSMED in Zaka district, the extent to which learning, risk management and adaptive management mechanisms are used by MWACSMED Zaka district are effective. The study also examined the challenges faced MWACSMED and beneficiaries with development programs in Zaka district. The study also aimed at developing a model for learning, risk management, adaptive management, monitoring, and evaluation for the efficient and effective implementation of development programs and the Monitoring, Evaluation, Learning, Adaptive and Risk Management model (MELARM) was developed. The researcher chose to conduct his research in this area because Zaka district is one of the districts in the country that is heavily reliant on subsistence farming, with farmers lacking adequate rain and inputs. Furthermore, there is no other industry or activity in the district that contributes to people's well-being. According to Chingarande and Matondi (2020:3), Zaka district is in a province that is reliant on activities such as livestock production, irrigated farming, wildlife conservation and smallholder agricultural production to support its population. Zimbabwe, where Zaka district is in the province of Masvingo used to be one of Southern Africa’s most vibrant, productive, and resilient countries, but the country has been facing economic and political challenges for close to two decades linked to decades of poor governance and corruption. Approximately 63% of Zimbabwean households are living in poverty and 16% in extreme poverty (USAID Country Development Cooperation Strategy 2016-2021:4). Given the above challenges in Zimbabwe, it is important to investigate whether decisionmakers are investing in long-term management approaches such as Adaptive Management. Decision-makers tend to be reluctant to invest in long-term management approaches which cover the concept of Adaptive Management and choose small-scale experiments instead. Moreover, they do not utilize opportunities to adjust and make improvements in their approach to management. Managers have often been making decisions based on funding opportunities that are short-term and political requests, rather than using the actual objectives of a program. The study used the quantitative approach in the form of a questionnaire that was designed using Google forms and shared through a link that was send to research participants through emails and WhatsApp. The sample population for the research questionnaire was 10 officials from MWACSMED and 7 beneficiaries of the programs managed by the Ministry in Zaka district. As shown by the study, the Ministry has not yet fully recognized the use of Learning and Adaptive Management to address the complexity and uncertainty of development programs. The Ministry has been managing programs through traditional monitoring and evaluation systems. The results of the study show that the Ministry was not using Adaptive Management approaches or learning thorough monitoring and evaluation systems, which may be because of socio-economic and political reasons. The introduction of adaptive programming and management calls for using information and learning to inform adjustments during implementation, which requires monitoring and evaluation systems that go beyond reporting on pre-specified indicators The study made recommendations for Learning and Adaptive Management mechanisms that are essential for development projects. The study also drafted a model known as the Monitoring, Evaluation, Learning, Adaptive and Risk Management Framework (MELARM). These recommendations are all based on the findings of the study. In general, government departments should avoid traditional thinking about program cycles, and they should merge program design, implementation, and evaluation through Learning and Adaptive Management. Government departments/agencies should also identify and document how procurement, contracting, work planning, reporting and risk management can foster adaptive programming and management to achieve intended development goals.

Facilitating adaptive management in a government program: A household energy efficiency case study

Quantifying the contribution of habitat dynamics relative to intrinsic population processes in regulating species persistence remains an ongoing challenge in ecological and applied conservation. Understanding these drivers and their relationship is essential for managing habitat‐dependent species, especially those that specialize in transitional habitats. Limitations in the ability of natural disturbance to mediate transitional habitat dynamics have resulted in a decline in early‐ and mid‐successional vegetation structure and prompted the need for aggressive habitat management to replace natural perturbations and increase habitat structural complexity. We describe a collaborative effort with a group of independent land managers to design an adaptive management program for restoring an imperiled ecosystem and recovering declining populations of an endemic habitat specialist. We developed a set of integrated, hierarchical models to estimate management‐mediated transition rates among vegetation classes in two dominant scrub communities and the species response (local colonization and extinction probabilities) as a function of habitat state. Models were fit using a long‐term data set of habitat and occupancy observations from 361 Florida scrub‐jay territories across two Florida counties. Occupancy model results correspond closely to previous approaches of estimating differential survival and reproductive success associated with habitat conditions, with highest colonization and lowest extinction rates estimated for those habitat states found to have the highest rates of survival and reproduction. In addition to offering an innovative approach for jointly modeling habitat and species population dynamics, the program we describe will also be of interest from a management perspective by providing guidance for developing collaborative, adaptive management frameworks from the ground up. We engaged land managers via workshops to specify objectives and desired state‐variable conditions, identify management alternatives, and elicit consensus opinions on model parameters. Treating expert opinions as pseudo‐observations to define Dirichlet priors allowed us to make use of existing management knowledge. Formal learning was then accumulated by updating transition probability estimates as management activities were implemented over the study period. We believe this adaptive management framework provides a useful approach for increasing our understanding of complex ecological relationships and hope that it will be adopted by others who have interest in informing management and conservation efforts.

Marine hydrokinetic (MHK) energy offers a promising new source of renewable ocean energy. However, the young industry is faced with significant challenges. Most notable is the challenge of regulatory uncertainty that is thought to hamper the successful deployment of new tidal energy technologies. Adaptive management may be one approach to deal with uncertainty and inform permitting decisions for hydrokinetic projects. In this study, we apply the concept of adaptive management to the Cobscook Bay Tidal Energy Project in Maine to better understand and inform permitting decisions. Using a social science approach of observation, interviews, and document analysis, we examine (1) agency roles and authority, (2) agency interactions, (3) regulatory change, and (4) challenges faced in the regulatory and permitting process for MHK development at the federal and state level. We found four institutional factors favorable to an adaptive approach. These include experimentation and learning, institutionalized choice to correct avoidable error, a strong commitment to interagency coordination, and an emphasis on early proactive engagement with project developers. We also identified institutional challenges or vulnerabilities. These include conflicting agency cultures, high financial costs, and long timeframes associated with baseline data collection. Lessons learned from this study can assist regulators, policymakers, and project developers design and implement an actively adaptive management approach that can move new renewable ocean energy development forward in a way that is socially acceptable and environmentally responsible.

This paper introduces an implementation example of an adaptive power management framework applicable to a digital TV set-top box which has energy-saving capability. The energy-saving set-top box supports a deep-sleep passive standby state in which almost all hardware functional modules are turned off except those required for a fast wake-up. Eventually, in the deep-sleep passive standby state, the power consumption level of the set-top box goes down to less than 1 watt. However, if the set-top box stays in the deep-sleep passive standby state, it can miss out the updated version of applications and EPG information. But, such shortcomings can be overcome using a mechanism for controlling power-management state transition based on set-top box usage patterns, which was proposed in the preliminary work related to this paper. This paper proposes two different methods of implementing the adaptive power management framework for the energy-saving set-top box. And this paper also shows some demonstration results.

Microseira wollei has grown to problematic densities within Lake St. Clair, located between Lake Erie and Lake Huron. Though stakeholders have issued a call-to-action for the management of M. wollei, no substantive plan exists, which was the impetus for this adaptive management framework. Field surveys, emerging technologies, laboratory evaluations, and a literature review were integrated to develop this site-specific frame-work. Adaptive management consists of a series of iterative steps to define and characterize the issue; identify goals, plan, and prioritize; determine a plan; implement management; measure the outcomes; and adapt and re-fine the plan. M. wollei is widely distributed within the study area and across large sections of the lake bed. Due to its large spatial extent, long-term management should focus on regaining and maintaining uses within Lake St. Clair that have been affected by shoreline accumulations. Mechanical removal is anticipated to be the most effective tool; however, the potential addition of algaecides or nutrient sequestration products could be explored for increased effectiveness. The planning and execution of a substantive management plan for M. wollei in Lake St. Clair are outlined and described, but effective management will require a concerted effort on a scale that matches the scope of the problem.

PurposeThe purpose of this paper is to develop a conceptual framework for extending an understanding of resilience in complex adaptive system (CAS) such as supply chains using the adaptive cycle framework. The adaptive cycle framework may help explain change and the long term dynamics and resilience in supply chain networks. Adaptive cycles assume that dynamic systems such as supply chain networks go through stages of growth, development, collapse and reorientation. Adaptive cycles suggest that the resilience of a complex adaptive system such as supply chains are not fixed but expand and contract over time and resilience requires such systems to navigate each of the cycles’ four stages successfully.Design/methodology/approachThis research uses the adaptive cycle framework to explain supply chain resilience (SCRES). It explores the phases of the adaptive cycle, its pathologies and key properties and links these to competences and behaviors that are important for system and SCRES. The study develops a conceptual framework linking adaptive cycles to SCRES. The goal is to extend dynamic theories of SCRES by borrowing from the adaptive cycle framework. We review the literature on the adaptive cycle framework, its properties and link these to SCRES.FindingsThe key insight is that the adaptive cycle concept can broaden our understanding of SCRES beyond focal scales, including cross-scale resilience. As a framework, the adaptive cycle can explain the mechanisms that support or prevent resilience in supply chains. Adaptive cycles may also give us new insights into the sort of competences required to avoid stagnation, promote system renewal as resilience expands and contracts over time.Research limitations/implicationsThe adaptive cycle may move our discussion of resilience beyond engineering and ecological resilience to include evolutionary resilience. While the first two presently dominates our theorizing on SCRES, evolutionary resilience may be more insightful than both are. Adaptive cycles capture the idea of change, adaptation and transformation and allow us to explore cross-scale resilience.Practical implicationsKnowing how to prepare for and overcoming key pathologies associated with each stage of the adaptive cycle can broaden our repertoire of strategies for managing SCRES across time. Human agency is important for preventing systems from crossing critical thresholds into imminent collapse. More importantly, disruptions may present an opportunity for innovation and renewal for building more resilience supply chains.Originality/valueThis research is one of the few studies that have applied the adaptive cycle concept to SCRES and extends our understanding of the dynamic structure of SCRES

Understanding the behavioral ecology of wildlife that experiences negative interactions with humans and the outcome of any wildlife management intervention is essential. In the Cape Peninsula, South Africa, chacma baboons ( Papio ursinus ) search for anthropogenic food sources in both urban and agricultural areas. In response, the city of Cape Town and private farmers employ “rangers” to keep baboons within the Table Mountain National Park. In this study, we investigated the success of rangers' intervention in keeping baboons in their natural habitat. Based on our findings in year one, we recommended adjustments to the rangers' management strategy in year two. We recommended improved consensus of actions toward baboons (that is, when/where to herd them), and the construction of a baboon‐proof fence around one of the farms that provided a corridor to urban areas. During the 2 months following recommendations, these interventions combined resulted in a significant reduction in the time baboons spent in both urban and agricultural land. Our case study illustrates the importance of integrating research findings into ongoing management actions to improve both human livelihoods and baboon conservation through an adaptive management framework. We expect similar approaches to be beneficial in a wide range of species and contexts.

Objective setting, performance measures, and accountability are important components of an adaptive-management approach to river-enhancement programs. Few lessons learned by river-enhancement practitioners in the United States have been documented and disseminated relative to the number of projects implemented. We conducted scripted telephone surveys with river-enhancement project managers and practitioners within the Upper Mississippi River Basin (UMRB) to determine the extent of setting project success criteria, monitoring, evaluation of monitoring data, and data dissemination. Investigation of these elements enabled a determination of those that inhibited adaptive management. Seventy river enhancement projects were surveyed. Only 34% of projects surveyed incorporated a quantified measure of project success. Managers most often relied on geophysical attributes of rivers when setting project success criteria, followed by biological communities. Ninety-one percent of projects that performed monitoring included biologic variables, but the lack of data collection before and after project completion and lack of field-based reference or control sites will make future assessments of ecologic success difficult. Twenty percent of projects that performed monitoring evaluated >or=1 variable but did not disseminate their evaluations outside their organization. Results suggest greater incentives may be required to advance the science of river enhancement. Future river-enhancement programs within the UMRB and elsewhere can increase knowledge gained from individual projects by offering better guidance on setting success criteria before project initiation and evaluation through established monitoring protocols.

Adaptive management and environmental decision making: A case study application to water use planning

Long-term, flexible therapy strategies are needed for chronic diseases like cardiovascular ailments, diabetes, and chronic kidney disease and they have to deal with patient heterogeneity, changing physiological states and privacy constraints. This research suggests a framework for privacy-preserving and personalized artificial intelligence that combines Federated Learning (FL), a Res-HyperTransformerNet deep prediction model, Personalized Multi-Agent Reinforcement Learning (PMARL) and Explainable Artificial Intelligence (XAI) for adaptive chronic disease management. The framework is structured with two main aims: (i) to create an accurate and privacy-aware predictive model for chronic disease risk using diverse data sources, and (ii) to permit adaptive, individual therapy optimization through multi-agent reinforcement learning. Federated learning is utilized to conduct the training of Res-HyperTransformerNet over distributed Internet of Medical Things (IoMT) nodes without the requirement of transferring raw patient data. The embeddings that are created from patient data are then passed on to a PMARL module, where several agents optimize therapy dimensions like medications, diet, physical activity, and mental health interventions independently. To make the clinical support more understandable, the SHAP-based explainability method is used for both predictive and decision-making parts. The framework is tested using two public datasets—the CDC Chronic Disease dataset and the UCI Chronic Kidney Disease Risk Factor dataset. The performance is measured by employing classification metrics (accuracy, precision, recall, F1-score, MCC) as well as reinforcement learning metrics (reward score, convergence steps, episode return) and federated system metrics (communication overhead, convergence rounds, and training time). The experiments indicate that the proposed framework demonstrated improved performance in terms of predictive accuracy and policy convergence speed compared to the baseline deep learning and reinforcement learning models, and at the same time it is more cost-effective when it comes to communication in a federated setting. This means that the proposed method suggests the potential of being a reproducible and open AI framework for adaptive chronic disease therapy management. The combination of ResNet and Transformer blocks, i.e., Federated Res-HyperTransformerNet, achieved strong performance in both datasets, namely CDC Chronic Disease (Dataset 1) and UCI CKD Risk Factor (Dataset 2). The obtained accuracy for Dataset 1 was 98.61% and for Dataset 2, it was 97.75%, under the conducted experimental settings.

Managing sustainable development in recreational and protected areas. The Dovre case, Norway

An optimization-based adaptive resource management framework for economic Grids: A switching mechanism

The rapid advancements in heterogeneous parallel architectures, consisting of CPUs, GPUs, FPGAs, have introduced significant challenges in efficient resource management for high-performance computing systems. Static and heuristic-based approaches fail to address the adaptability required for handling varying workloads and hardware configurations which results in suboptimal performance and energy inefficiency. This research proposes a machine learning-driven adaptive resource management framework that dynamically optimizes task scheduling, resource allocation, and data placement. The framework employs regression models & reinforcement learning algorithms to predict workload behaviors, resource utilization, and task execution times in real time. Experimental results on heterogeneous testbed demonstrate a 21% reduction in task execution time, 18% improvement in energy efficiency, and 38% decrease in fault recovery time compared to conventional methods. These findings highlight the framework’s ability to improve resource utilization while maintaining reliability and minimizing energy overhead. The work advances the field by introducing a unified approach that integrates machine learning for runtime optimization across heterogeneous systems. Practical implications include its applicability to large-scale scientific simulations and deep learning tasks, where adaptive resource management is critical. Future study can focus on enhancing prediction accuracy by advanced deep learning techniques and extending the framework to handle emerging hardware accelerators and edge computing environments.

ABSTRACTInterdisciplinary pain management combines multiple disciplines of professionals to understand the biological and psychosocial factors causing a patient's pain and to determine the best treatments among many to administer. To improve current and future pain outcomes, the developed adaptive interdisciplinary pain management framework employs approximate dynamic programming with state transition and outcome models estimated from actual patient data. The sequential treatment structure and observational nature of the data lead to a form of endogeneity, which results in biased model parameter estimates when developing state transition and outcome models. This research develops a process based on the inverse probability of treatment weighted method to address the endogeneity in estimating state transition and outcome models. This article discusses a general method developed for independent treatments. The proposed approach can potentially be employed for adaptive treatment in other sequential health care applications based on observational data. Our approach is demonstrated using data from the Eugene McDermott Center for Pain Management at the University of Texas Southwestern Medical Center at Dallas.