Adaptive Management Research Articles

Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning

This paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. We propose Enhanced Sub-Slice (eSS), a machine learning pipeline that enables granular resource allocation through network sub-slicing, reducing service denial risks and enhancing user experience. This leads to the introduction of Company Network as a Service (CNaaS), a new enterprise service model for mobile network operators (MNOs). The framework was evaluated using Google Colab for machine learning implementation and MATLAB/Simulink for dynamic testing. The results demonstrate that ONSSO improves MNO collaboration through real-time resource information sharing, reducing orchestration delays and advancing adaptive 5G network management solutions.

Analysis of Causes for Extension of Time in Construction Projects: A Case Study of Kageshwori Manohara Municipality

Extension of Time (EoT) claims are a common occurrence in construction projects worldwide, often leading to disputes due to insufficient attention to delay causes during the contract initiation phase. However, in the context of Kageshwori Manohara Municipality, these challenges are particularly pronounced due to localized factors. This study identifies key causes of EoT through qualitative and quantitative analyses, utilizing Principal Component Analysis (PCA), Kaiser-Meyer-Olkin (KMO) testing, Bartlett's Test of Sphericity, Eigenvalue and variance analysis, and reliability assessments using Cronbach's Alpha. Principal Component Analysis (PCA) helped to categorize twelve major EoT causes into three components: Design and Consultant-Related Issues (Eigenvalue: 13.892, Variance: 55.57%), Project Management and Execution Challenges (Eigenvalue: 1.146, Variance: 4.58%), and External and Unforeseen Conditions (Eigenvalue: 1.000, Variance: 4.00%). Significant causes include inadequate design information, poor project management, and unforeseen challenges like land acquisition disputes. These causes were ranked using the Relative Importance Index (RII) to prioritize their impacts on project time extensions. The authors believe that present findings illuminate the need for improved design and planning processes, along with adaptive project management practices to mitigate EoT claims and enhance project performance within the municipality.

Multiscale Granger dependencies in the precipitation network of the island of Sicily

The island of Sicily has been displaying unusual rainfall behavior and unexpected extreme precipitation events in recent decades. In this study, we investigate the Granger causal (GC) dependencies in the network of precipitation measurement sites of Sicily at different timescales (every 10 min, 1 h, 6 h, 12 h, and 24 h). We study, across seasons and years, different parameters that characterize the GC dependencies: the total in/out-degree of nodes, the total in/out strength of nodes, the total number of links in the network, the number of eastward/westward links, the strength of eastward/westward links, and the maximum strength of links. We then investigate GC statistic intensities, focusing on the temporal evolution of maximum values over multiple timescales. Our study of precipitation patterns in Sicily indicates that, since 2013, the southern regions near Mount Etna (Catania, Siracusa, and Ragusa) have been increasingly affected, while the western areas (Trapani, Palermo, and Agrigento) have been the most affected. Granger causality networks reveal scale-invariant dependencies, with stronger and sparser connections at timescales that extend beyond 6 h, with a notable westward flow of predictive information. These patterns, which are consistent across seasons, suggest localized perturbation fronts, with stronger links indicating a more significant influence on westward predictions. This study highlights shifts in Sicily’s water cycle that call for adaptive management strategies in the face of the increasing frequency of extreme events.

Structural diversity is an important predictor of forest productivity responses to drought

Abstract The response of forest productivity to drought depends on biodiversity and other stand attributes. Biodiversity can regulate productivity through niche complementarity (i.e. greater diversity better utilizes resources to determine ecosystem functioning) and mass‐ratio effects (i.e. dominance drives ecosystem functioning). However, when these forest attributes occur with drought, stress results are mixed. One emerging biodiversity metric is structural diversity, the variation in vegetation size and form. There is a lack of understanding of the interactive effects of structural diversity and drought on forest productivity. Addressing these knowledge gaps can improve the projection of forest dynamics and structural diversity‐based management for drought adaptation. We compiled multiple datasets including repeated survey data of 13,059 trees in 299 plots across southcentral US forests stressed by the 2010–2015 drought. We used the Random Forest algorithm to examine how structural diversity and other factors influenced three aspects of productivity measured by above‐ground wood biomass: ingrowth, growth, and net change accounting for mortality. Our results showed that (1) Among the eight stand attributes studied, structural diversity was among the top three variables showing the greatest main and interactive effects of drought on forest productivity, except for the interactive effect on ingrowth; (2) The drought‐related losses in forest growth and biomass net change were exacerbated in forests with greater structural diversity; (3) Compared with the species and functional diversity metrics, functional identities had greater interactive effects with drought on forest productivity (except for the ingrowth). Synthesis and applications: Productivity losses related to drought increased with greater structural diversity and decreased with functional identities characterized by conservative resource strategies, such as a high leaf carbon–nitrogen ratio. Thus, structural diversity and functional identity could be considered in projecting and managing forest dynamics under climate change. The comparisons between functional identity and the diversity metrics support the important role of the mass‐ratio effect in forest productivity during drought.

Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River

Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. The record-breaking 2019 flood in the Ottawa River basin, which exceeded the 100-year flood threshold, underscores the escalating impact of climate change on hydrological extremes. These unprecedented events highlight the limitations of traditional ML models, which rely heavily on historical data and often struggle to predict extreme floods that lack representation in past records. This calls for integrating more comprehensive datasets and innovative approaches to enhance model robustness and adaptability to changing climatic conditions. This study introduces the Next-Gen Group Method of Data Handling (Next-Gen GMDH), an innovative ML model leveraging second- and third-order polynomials to address the limitations of traditional ML models in predicting extreme flood events. Using HEC-RAS simulations, a synthetic dataset of river flow discharges was created, covering a wide range of potential future floods with return periods of up to 10,000 years, to enhance the accuracy and generalization of flood predictions under evolving climatic conditions. The Next-Gen GMDH addresses the complexity and limitations of standard GMDH by incorporating non-adjacent connections and optimizing intermediate layers, significantly reducing computational overhead while enhancing performance. The Gen GMDH demonstrated improved stability and tighter clustering of predictions, particularly for extreme flood scenarios. Testing results revealed exceptional predictive accuracy, with Mean Absolute Percentage Error (MAPE) values of 4.72% for channel width, 1.80% for channel depth, and 0.06% for water surface elevation. These results vastly outperformed the standard GMDH, which yielded MAPE values of 25.00%, 8.30%, and 0.11%, respectively. Additionally, computational complexity was reduced by approximately 40%, with a 33.88% decrease in the Akaike Information Criterion (AIC) for channel width and an impressive 581.82% improvement for channel depth. This methodology integrates hydrodynamic modeling with advanced ML, providing a robust framework for accurate flood prediction and adaptive floodplain management in a changing climate.

Determinants of burnout syndrome among undergraduate nursing students in Poland: a cross-sectional study

BackgroundNursing students worldwide are exposed to burnout syndrome resulting from constant academic and clinical stressors. The consequences of student burnout can be related to the student’s mental and physical health. Burnout may affect the learning process, achieving professional skills, and future effectiveness while working with a patient and, eventually, might lead to the decision to quit the job. This study aims to analyze the intensity of burnout syndrome among undergraduate nursing students based on their year of study (1), establishing the impact of stress, anxiety, insomnia, and selected sociodemographic variables on the level of burnout (2) and establishing a student profile predestined to be affected by burnout (3).MethodsA cross-sectional study was conducted among 841 bachelor’s degree nursing students from March 2022 to June 2022. The Maslach Burnout Inventory, Athens Insomnia Scale, Modified Hospital Anxiety and Depression Scale, and Perceived Stress Scale were used.ResultsHigh levels of emotional exhaustion were found in 40.9% and depersonalization in 20.33%, while low personal accomplishment was found in 16.88% of the students. Students of nursing in Year 2 presented the highest levels of emotional exhaustion and depersonalization and achieved significantly higher scores on the Athens Insomnia Scale. The determinants of burnout syndrome identified by the regression analysis were: sociodemographic factors (age, gender, and financial resources), the factors connected with the studying environment (year of studies and clinical and academic stressors), and emotional/mental/interpersonal factors (anxiety, depression, insomnia, and intensity of stress related to life situations).ConclusionThe academic community should be encouraged to create a more supportive environment for students, which will ultimately prevent dropouts and increase the number of nursing professionals in the Polish healthcare system. We recommended carrying out regular screening tests for burnout, insomnia, anxiety, stress, and depression (e.g., once a year) among students. Depending on the results of the tests, implementation of interventions aiming at reducing the negative outcomes of burnout syndrome and promoting well-being and adaptive management mechanisms should be considered.

Coral reef thermal microclimates mapped from the International Space Station

Abstract Satellite sea surface temperature (SST) is critical for describing marine environments. Traditional SST data, such as those provided by the Group for High Resolution Sea Surface Temperature (GHRSST) program, are valuable, but have a relatively coarse spatial resolution for mapping coral reef thermal environments. Hence, fine resolution SST from orbit would be of great utility to the coral reef research community and speed the pathway to an increased understanding of how, when, and where thermal stress afflicts individual reefs. Such data would support adaptive management, especially so for the design of marine protected areas. Flying aboard the International Space Station, the NASA ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) instrument may already fill this niche with a spatial resolution 204 times finer than GHRSST. To evaluate ECOSTRESS thermal data over reef environments, we deployed 21 temperature loggers over three years across two reef sites in the Red Sea. We compared temperature retrievals from both the coarse resolution GHRSST and the fine resolution, experimental, ECOSTRESS, to this in-situ logger dataset. While temperature data from both orbital platforms correlated strongly with the logger recordings, only ECOSTRESS, with its 70-m pixels, could construct thermal microclimate maps capturing the dynamic temperature fluctuations experienced by our studied reefs. We contend that ECOSTRESS represents a significant advancement in the capability to monitor heat stress on reefs from orbit.

Leveraging Community-based Approaches for Enhancing Resource Allocation in Fog Computing Environment

Efficient resource allocation in fog computing environments is essential to address the increasing demand for high-performance and adaptable network services. Traditional methods lack granular differentiation based on traffic characteristics often resulting in suboptimal bandwidth utilization and elevated latency. To enhance network efficiency, this study applies a community-based resource allocation approach leveraging the Louvain algorithm to dynamically cluster network nodes with similar traffic demands. By forming communities based on bandwidth and latency needs, this approach enables a targeted resource distribution, aligning each community with optimized pathways that address specific requirements. The results indicate notable performance gains, including a 14% increase in bandwidth utilization affecting the download and a reduction in latency by an average of 23% for time-sensitive applications. These improvements highlight the effectiveness of the proposed approach in managing diverse network demands, improving data flow stability, and enhancing the overall performance of fog computing infrastructures. These findings underscore the potential for community-based resource allocation to support scalable, adaptable, and secure resource management, positioning it as a viable solution to meet the complex needs of IoT and other distributed network systems.

Dynamics of Surface Chlorophyll and the Asymmetric Response of the High Productive Zone in the Peruvian Sea: Effects of El Niño and La Niña

ABSTRACTThis study analyzes over 26 years of satellite data to explore the seasonal and interannual variability of surface chlorophyll‐a from September 1997 to March 2024, focusing on the impacts of the El Niño‐Southern Oscillation (ENSO). We investigate how these phases affect the High Productivity Zone (HPZ) during two seasonal periods: The Warm Season (December to April) and the Cold Season (June to September). From an ENSO‐neutral perspective, the HPZ is more extensive and exhibits higher chlorophyll concentrations during the Warm Season. Conversely, in the Cold Season, low chlorophyll concentrations are found and the area of the HPZ is reduced by 88,000 km2. Under El Niño conditions, chlorophyll‐a concentrations decrease consistently along the near‐offshore region, but a counter‐intuitive increase is observed within the 25 km from shore. La Niña phase enhances chlorophyll in the near‐offshore region while reducing it in the very nearshore waters. This study further highlights the asymmetric response of various physical drivers (such as upwelling strength and light) to different ENSO phases. This finding underscores the intricate interplay between ENSO and marine productivity, where the response can vary seasonally. These findings underscore a spatially complex response of the Peruvian coastal productivity to ENSO phases. Understanding these dynamics is vital for predicting future shifts in coastal productivity and for developing adaptive management strategies to mitigate the impacts of climate variability on marine resources.

Identifying socio-ecological dynamics in water systems through adaptive cycles

Investigating the dynamics of water systems is challenging but essential for identifying weaknesses and strengths that enhance adaptation and transformation, contributing to governance. We propose integrating approaches to the adaptive cycle framework to understand socio-ecological dynamics, system interactions, and variables that impact a water system as a reservoir in the Brazilian semi-arid region. Key findings include the identification of non-linearity in the socio-ecological dynamics and the simultaneous use of adaptation and transformation mechanisms to prevent water collapse. We also emphasize the role of institutions in creating resilience, which can steer the system toward desired or undesired regimes. Additionally, understanding relationships among systems at various planning levels and their variables is crucial, as they can influence water systems fast or slowly. The outcomes show the importance of considering a reservoir at the local planning level within a river basin, state, and nation. This consideration helps improve water governance and policymaking.

Degradation Adaptive Energy Management Strategy for FCHEV Based on the Rule-DDPG Method: Tailored to the Current SOH of the Powertrain

Degradation Adaptive Energy Management Strategy for FCHEV Based on the Rule-DDPG Method: Tailored to the Current SOH of the Powertrain

Qualitative Findings of Adaptive Recovery Management Checkups (RMC-Adaptive) to Improve Retention across the Oud Service Cascade

Qualitative Findings of Adaptive Recovery Management Checkups (RMC-Adaptive) to Improve Retention across the Oud Service Cascade

Local ecological predictions as input to adaptive management of natural plant communities

Local ecological predictions as input to adaptive management of natural plant communities

Integration of land use resilience and efficiency in China: Analysis of spatial patterns, differential impacts on SDGs, and adaptive management strategies

Integration of land use resilience and efficiency in China: Analysis of spatial patterns, differential impacts on SDGs, and adaptive management strategies

Assessment of macrobenthos in evaluating the restoration effects of artificial mangrove planting on tidal flats in Zhejiang, China.

Artificially planted mangroves are widely used for tidal flat restoration, but their effectiveness in restoring these ecosystems remains unclear. Macrobenthos, as key indicators of tidal flat ecosystem health, can reflect changes in environmental conditions and quality resulting from the introduction of artificial mangroves. This study aimed to investigate the ecological restoration effects of artificially planted mangroves in northern China by surveying macrobenthic communities across four regions in June 2023. We compared the macrobenthic community structures in mangrove zones with those in Spartina alterniflora or unvegetated mudflat control zones by estimating habitat quality indices and community structure analyses. Additionally, we explored the relationship between community structures and environmental factors to identify key drivers of community structure changes. Significant differences in species abundance and biomass were observed across the four regions, with mudflat zones exhibiting the highest values, followed by mangrove and Spartina alterniflora zones. Beta diversity was higher in mangrove zones compared to control zones, driven primarily by turnover components. The AMBI index indicated excellent ecological conditions in 18-year-old mangrove zone on Maoyan Island, while poorer conditions were observed in the mangrove zone of Zhoushan City. Environmental factors, such as sand content, organic carbon content, and elevation, significantly influenced macrobenthic community distribution. Overall, our findings suggest that the 18-year-old mangrove zone on Maoyan Island is ecologically stable and demonstrates significant restoration effects. Using macrobenthos to assess the restoration efficacy of mangroves proves to be both effective and feasible. Furthermore, while the mangroves have successfully established and grown, their long-term survival depends on the active involvement of local stakeholders, highlighting the importance of long-term monitoring and adaptive management strategies.

What happens when you turn weed management off? A long-term appraisal of the effectiveness of Pteridium aquilinum (L.) kuhn control treatments and the role of sheep grazing.

Long-term strategies are needed for the ecological restoration of land invaded by perennial weed species comprising of two parts: (1) control of the invasive species and (2) restoration of native vegetation meeting agricultural/conservation objectives. We investigated this within a statistically-rigorous, 28-year experiment at a site where Pteridium aquilinum had invaded an acid-grass/heathland. Where P. aquilinum-control treatments were applied for 20 years (1993-2012) very good P. aquilinum control was achieved by cutting (once or twice yearly) or repeat applications of the herbicide asulam. Vegetation restoration treatments (±sheep-grazing, seed addition) were also included and an acid-grass/grass heath was stablished. All three P. aquilinum-control treatments were very effective. In 2013 all P. aquilinum-control treatments were stopped and P. aquilinum recovery was monitored along with changes in the understory flora. All P. aquilinum performance variables remained significantly lower than untreated controls but P. aquilinum recovery varied between P. aquilinum-control treatments; slowest in the asulam treatment, intermediate where cut twice yearly and fastest where cut once yearly. P. aquilinum recovery was also faster in ungrazed plots, especially when cut once yearly. The underlying plant community remained relatively stable albeit with a reduction in species diversity. These good results were obtained from a large number of interventions and it is recommended that an adaptive management approach be used with (1) the aim of reducing frond density to ≤2 fronds m-2, (2) continued monitoring, and (3) re-applying control treatments when frond density exceeds this value. We also found that sheep grazing at low densities slowed P. aquilinum recovery, especially in the spray and cut twice yearly treatments almost certainly by trampling. However it is suggested that this needs further investigation using a range of different species of greater body-weights and at increased densities.

Act for the best, prepare for the worst—Defining strategic mesopredator triggers that safeguard apex predator eradication benefits

Abstract Eradicating invasive species is critical for supporting threatened species recovery. In multi‐invaded ecosystems, it may only be feasible to remove one or some of the several invasive species that are present. However, this may not deliver the desired benefits, and shifting the complement of threats in some cases can have unexpected adverse consequences. A priori evaluation of potential outcomes, and identifying critical thresholds driven by other invasives, can inform decision‐making and decision triggers for adaptive management responses to ensure benefits are realised. We use population viability analyses informed by field data to evaluate the potential outcomes of a single species removal from a multi‐invaded ecosystem, Christmas Island. We assess whether (1) removing an invasive apex predator (cats; Felis catus) in isolation would allow a threatened seabird species (Indian Ocean red‐tailed tropicbird; Phaethon rubricaudra westralis) to recover; and based on evidence that this bird species is susceptible to rat predation (2) find the nest predation threshold for a remaining invasive mesopredator (Rattus rattus) that should trigger complementary rat control to ensure seabird population protection. The analyses indicate that with cat eradication, the tropicbird population is expected to recover quickly and benefit long term. The current empirical rates of nest failure due to rat predation are insufficient to cause tropicbird population decline. However, if nest failure from rat predation should exceed 40%, the tropicbird population is predicted to decline by 1% (SD ± 0.25) per year. If rat predation caused 70% or more nests to fail, the tropicbird population is predicted to decline to extinction in 17 years (~1–2 generations). Synthesis and applications: Our results illustrate the value of PVAs as a tool for understanding potential invasive species management outcomes in multi‐invaded ecosystems. Defining thresholds of ecologically significant rat impact informs the setting of decision triggers, which in combination with targeted monitoring, can help managers prepare to adapt to deficient or undesirable outcomes in a timely and appropriate way.

Understanding How Companies Utilize Technological Innovation for Competitive Advantage: A Qualitative Inquiry

This qualitative study investigates the intricate relationship between technological innovation and competitive advantage within contemporary business environments. Employing a qualitative research design, the study conducts a comprehensive review and synthesis of existing literature to explore the underlying mechanisms and contextual factors shaping this relationship. Drawing upon key concepts from the resource-based view (RBV) and dynamic capabilities perspective, the research aims to uncover how firms leverage innovation to create valuable, rare, and difficult-to-imitate resources that confer sustained competitive advantage. The research design includes systematic data collection from scholarly literature encompassing peer-reviewed journal articles, books, conference proceedings, and reports, followed by rigorous data analysis techniques such as thematic analysis. Ethical considerations are paramount throughout the study to ensure the integrity of the research process. The findings highlight the critical role of fostering a culture of innovation, overcoming resistance to change, and navigating regulatory and ethical concerns in leveraging technological innovation for competitive advantage. Key insights include the importance of proactive leadership, organizational commitment, and investment in employee development for cultivating an innovative culture. Additionally, the study underscores the need for agile and adaptive innovation management practices to respond effectively to dynamic market conditions and emerging opportunities. Overall, this research contributes to a deeper understanding of how firms can harness technological innovation to enhance competitiveness and drive long-term growth in today's dynamic business landscape.

E-wallet Delivery Technology Architecture Adoption: A Review

E-wallet is a fintech digital tool that allows to make cashless, quick, and easy transactions, and to review and analyze payment histories. Meanwhile, the expansion of digital wallet usage has contributed to a surge in overspending where users do not benefit from generated expenditure data to improve personal financial management. The objective of this study is to compare existing delivery technologies operational features to ensure the best suited delivery technology is adapted into the proposed adaptive money management embedded e-wallet design. A review was conducted to find relevant articles published between 2017 to 2021. The main inclusion criteria were English articles that discuss the applications of NFC, QR code, Digital (online)-only, and/or SMS. The search resulted in 159 articles but only 12 met all the inclusion criteria. The review highlights the advantages and disadvantages of using the three technologies in financial applications. This review suggests that QR payment is most popular, secure, fast, and cost-effective compared to NFC and SMS, as delivery technology best suited for the adaptive management embedded e-wallet that incorporated the four blocks of Fintech technologies namely, blockchain, AI, IoT and RPA. Overall, a QR code enabled e-wallet can expedite and automate the process of analyzing transactions, thus providing a solution to secure transactions and better money management.

How does the water flow? And does data follow it?

Climate adaptation and effective flood management require accurate and accessible data on water levels and water flow. In consultation with the Danish Geological Survey (GEUS), the Danish municipalities association (KL), and the Danish Environmental Protection Agency as well as the private companies WatsonC, DRYP, and WSP, Denmark's Environmental Portal (DMP) has developed an IT system for water levels, water flow and groundwater level measurements. The system makes it possible to compile data from different loggers in near real time. The system supports authorities such as the Danish Meteorological Institute (DMI) and municipalities in their work to prepare flood warnings and implement climate adaptation plans. It also enables a broader use of data that was originally collected for specific purposes, such as mass transport calculations and crop cutting, and it opens up new solutions at the societal level. As groundwater rises and climate adaptation problems grow, it is essential to have access to integrated data on both watercourses and groundwater level measurements. The new system supports the work of municipalities and utilities on these challenges, and makes it possible to share data effectively across public authorities. In addition, the system opens up the development of commercial IT solutions that can help tackle future climate adaptation challenges.