Proactive Resource Research Articles

Influence of Soil on Crop Performance and Ecosystem Services: A Review

Globally, the soil has a direct or indirect role in effective and sustainable agricultural production. This is due to the soil's multiple benefits to the creation of food, whether it comes from crops or animals. The pH, organic matter content, and nutritional balance of soils all affect how well they perform as vital life supports and nourishment sources. In addition to supporting a variety of living things and aiding in water regulation, these substrates are essential for sequestering carbon, particularly in light of climate change. But because of climate and human influences, soils are under danger, necessitating proactive resource management techniques. Combating degradation of the soil, which is impacted by things like unsustainable agriculture, deforestation, and inadequate irrigation, is essential to stopping desertification. Sustainable practices like nutrient management and integrated soil fertility management, proper tillage operation, etc. aim to increase crop productivity and improve soil fertility. Management techniques and environmental modifications have the potential to boost sustainability and enhance ecosystem service and help create agroecosystems that are robust to stress, resource efficiency, and climate change.

PRORL: Proactive Resource Orchestrator for Open RANs Using Deep Reinforcement Learning

PRORL: Proactive Resource Orchestrator for Open RANs Using Deep Reinforcement Learning

Behind the Scenes: The Untold Stories of School Heads handling Integrated Senior High Schools

The goal of this research was to describe the experiences gained by the school heads in integrated senior high school in Monkayo East District, Davao de Oro, Philippines. Employing phenomenological approach among six participants, in-depth interview was conducted which answered the four fundamental questions. Findings highlighted the critical role of adaptive strategies, including collaborative efforts among staff, proactive resource management, and continuous professional development for educators. These approaches fostered resilience and innovation within educational settings, enhancing the quality of student learning experiences. Moreover, the study underscores aspirations to equip students with 21st-century skills, provide high-quality education, and foster supportive learning environments. These aspirations drove efforts to improve curriculum relevance, infrastructure, and community partnerships. Overall, the research emphasizes the importance of effective leadership and strategic planning in addressing challenges and advancing educational practices in integrated senior high schools.

Comparing resilience strategies for a multistage green supply chain to mitigate disruptions: A two-stage stochastic optimization model

Supply chain disruptions, notably exemplified by the COVID-19 pandemic, have substantial impacts, requiring effective resilience strategies. This study introduces a comprehensive multi-stage, multi-period green supply chain design model, incorporating six resilience strategies to tackle downstream and upstream disruptions. A rigorous two-stage stochastic optimization approach is employed to manage parameter uncertainties and disruptions. The objectives include minimizing disruption costs and CO2 emissions, for a chain that operates under cap-and-trade emission regulation. The findings, derived from a numerical experiment and sensitivity analysis, offer the optimal supply chain structure and effective resilience strategies to mitigate disruptions. The demand structure for essential and non-essential products during disruptions are explored, emphasizing proactive resource allocation. In particular, the impact of facility capacity reductions underscores the importance of capacity management. Sensitivity analyses reflect the trade-offs involved in capacity management strategies, emphasizing the critical need to maintain an optimal level of capacity to prevent service level degradation. Additionally, examination of carbon abatement regulations reveals the intricate balance between environmental responsibility and economic efficiency. In summary, this study addresses supply chain disruptions and offers actionable insights for supply chain managers and policy makers. The research highlights the importance of specific and data-driven resilience strategies to optimize supply chain performance in face of disruptions.

Innovation and Strategy in Tender Marketing PT Chop’s Approach to Enchance Tender Success

Introduction: This paper investigates the importance of innovative tender marketing strategies in enhancing PT Chop’s competitive positioning and ensuring continued success in the tendering process (PT Chop Tender Success Strategies). Data Collection Methods: Using a mixed-methods approach with surveys and interviews with 5 respondents. Data Analysis. The analysis will be employed the qualitative descriptive methods. Results and Discussion: PT Chop’s tender success hinges on financial strength, resourcefulness, experience, and connections. Strategic partnerships and cash flow management keep them competitive. Proactive resource allocation ensures they have the right talent for each project. Their proven track record and innovative solutions build trust with clients. Affiliations with various professionals provide a comprehensive understanding of project requirements. To further excel, PT Chop can target niche markets, invest in advanced project management tools, and leverage online collaboration for better communication. They can also address weaknesses through regular technology training and develop value-added services. Finally, identifying risks during tender prep and implementing sustainable practices will help mitigate threats. Conclusion: Based on the findings, an strategy found to Enchance Tender Success of PT Chop.

Optimizing healthcare resource allocation through data-driven demographic and psychographic analysis

This paper explores the multifaceted nature of healthcare resource allocation, examining demographic and psychographic analysis, data sources and analysis techniques, challenges, future directions, and innovations. Demographic analysis provides insights into population characteristics, healthcare demand, and disparities, informing targeted resource allocation strategies. Psychographic analysis complements demographic data by elucidating patient attitudes, behaviors, and preferences, guiding personalized care delivery and intervention design. A diverse array of data sources, including electronic health records, surveys, and geospatial data, underpins healthcare resource allocation. Advanced analysis techniques such as predictive analytics and machine learning unlock actionable insights from these data sources, enabling proactive resource allocation and optimization. Challenges such as data quality, bias, and ethical considerations underscore the complexity of healthcare resource allocation. However, emerging innovations, including artificial intelligence, precision medicine, and value-based care models, offer transformative potential for optimizing resource allocation and improving patient outcomes. Looking ahead, the integration of innovative technologies and patient-centered care models promises to reshape healthcare resource allocation, emphasizing equity, efficiency, and patient engagement. By navigating challenges thoughtfully and embracing future opportunities, healthcare systems can enhance resource allocation strategies to better meet the evolving needs of patients and communities. This paper explores the multifaceted nature of healthcare resource allocation, examining demographic and psychographic analysis, data sources and analysis techniques, challenges, future directions, and innovations. Demographic analysis provides insights into population characteristics, healthcare demand, and disparities, informing targeted resource allocation strategies. Psychographic analysis complements demographic data by elucidating patient attitudes, behaviors, and preferences, guiding personalized care delivery and intervention design. A diverse array of data sources, including electronic health records, surveys, administrative databases, geospatial data, and social determinants of health data, underpins healthcare resource allocation. Advanced analysis techniques such as predictive analytics, machine learning, spatial analysis, and simulation modeling unlock actionable insights from these data sources, enabling proactive resource allocation and optimization. Keywords: Healthcare Resource Allocation, Demographic Analysis, Psychographic Analysis, Data Sources, Advanced Analysis Techniques, Future Directions.

Evaluating machine learning prediction techniques and their impact on proactive resource provisioning for cloud environments

Evaluating machine learning prediction techniques and their impact on proactive resource provisioning for cloud environments

Proactive Resource Allocation for Cloud Manufacturing: A Dynamic Approach with DNN-Based Hybrid Many Objective Leopard Seal Algorithm

In the domain of cloud manufacturing, diverse services and the escalating demand for efficient and cost-effective service composition selection in cloud manufacturing have spurred day by day. Traditional methods face several challenges such as the risk of convergence issues and suboptimal solutions, high dimensionality of decision space, increased computational complexity, and scalability issues. To overcome these complexities, this paper proposed a novel method named the Deep Neural Network (DNN) based Hybrid Many Objective Leopard Seal (HMOLS) algorithm. In this study, DNN is employed to predict future Quality of Service (QoS) for each service based on historical data and integrated features. These predictions are fed back into the optimization model, influencing the dynamic weight calculation for each objective function. In this work, the Leopard Seal Optimization is implemented for hyperparameter optimization to enhance the performance of the HMOLS algorithm. Various evaluation metrics namely accuracy, throughput, reliability, resource utilization, and scalability are utilized to evaluate the performance of the HMOLS method and compare its performance with existing methods. The experimental outcomes depict the effectiveness of the HMOLS approach for the effective selection of services and fusion for cloud manufacturing. DNN-based predictions utilize deep neural networks to predict resource demand or utilization patterns and enable more accurate allocation decisions. These predictions optimize resource allocation by reallocating resources accordingly and address the challenges of resource utilization. Meanwhile, this method is required to overcome the computational overhead for applicability in cloud manufacturing.

Pre-disaster resource allocation based on network topology and flow features

Protecting transportation systems from severe damage is crucial for their post-disaster functionality. We adopt network topological metrics and generalised topological metrics involving demand and traffic flow characteristics to identify critical links for enhancing post-event transportation network performance. A single-parameter, non-monotonic function is proposed to allocate limited resources based on these metrics. Monte Carlo simulation generates failure scenarios to evaluate residual network performance through network topological measures and network level of service indicators. This work introduces ‘Trip Efficiency’, a novel level of service indicator that considers both demand connectivity and travel efficiency for a network with multiple links interrupted. In Anaheim case study network, betweenness-based resource allocation leads to better performance on network topological measures; while generalised topological metrics result in more connected OD pairs and higher travel efficiency. The experimental results demonstrate the effectiveness of the proposed method in enhancing network resilience against multisite disruptive events through proactive resource allocations.

Healthcare Resource Allocation Optimization

Assuring high-quality patient care and making the best use of the resources at hand requires the efficient allocation of healthcare resources. This study uses a thorough examination of patient admission data, treatment plans, and staff availability to tackle the complex problem of optimizing healthcare resource allocation. We want to reduce wait times, optimize resource use, and eventually improve patient outcomes in healthcare facilities by integrating these essential elements. Our strategy makes use of cutting-edge data analytics tools, such as optimization algorithms and predictive modelling, to find underlying patterns and trends in patient demand, treatment regimens, and staffing needs. Our goal is to create prediction models that anticipate patient admissions, treatment requirements, and staffing levels by analysing historical data and real-time inputs. This will allow for proactive resource allocation techniques. The study's conclusions have important ramifications for healthcare administration since they provide practical advice on how to improve patient care delivery, cut wait times, and make the most use of the resources at hand. Healthcare institutions can enhance resource allocation procedures to meet the changing demands of patient care while guaranteeing the optimal use of resources by integrating technology, data-driven decision-making, and evidence-based practices. In summary, this study lays the groundwork for future investigations and innovations in hospital administration and operations while also supporting current initiatives to enhance healthcare resource allocation procedures. Healthcare organizations can improve patient outcomes and achieve operational excellence while providing high-quality, patient-centred care by adopting data-driven approaches and utilizing technology.

Proactive Resource Allocation for Aggregated VLC/RF Networks With Outdated CSI

Proactive Resource Allocation for Aggregated VLC/RF Networks With Outdated CSI

Harnessing Artificial Intelligence for Early Detection and Management of Infectious Disease Outbreaks

Infectious diseases pose ongoing threats to global public health, demanding advanced detection methods for effective outbreak management. This study explores integrating artificial intelligence (AI) for early detection and management. AI algorithms analyze diverse datasets, including electronic health records and social media, to identify potential outbreaks. Machine learning models predict disease spread and severity, aiding proactive resource allocation. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses AI's role in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study also evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. Ethical considerations are crucial, emphasizing collaboration between public health agencies, healthcare providers, and technology experts. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. This paper advocates for AI integration to enhance infectious disease surveillance, offering a proactive response to safeguard public health.

Intelligent Pooling: Proactive Resource Provisioning in Large-scale Cloud Service

Intelligent Pooling: Proactive Resource Provisioning in Large-scale Cloud Service

Land use planning and climate change adaptation in river-dependent communities in Nigeria

Land use planning and climate change adaptation are critical for ensuring sustainable development and resilience in river-dependent communities in Nigeria. This study examines land use planning and climate adaptation in river-dependent communities in the Lower Niger River region of Nigeria. The research utilizes a mixed-methods approach. Data for the study were gathered from 198 households through surveys, complemented by 17 qualitative interviews and remote sensing techniques to assess land use changes and community perceptions. Findings reveal a notable trend where poor farmers face heightened vulnerability to climate change due to financial constraints, hindering their adoption of adaptive land use strategies. Specifically, more than half of the sampled households possess individual land for agricultural purposes yet lack the resources to effectively mitigate climate-related risks. Among the studied communities, Odekpe emerged as particularly vulnerable, with a prevalence of vulnerability at 32.3% and higher exposure to flood-prone zones.The quantitative analysis delineates community engagement in land management practices, showcasing proactive resource allocation and conflict resolution efforts among over 60% of participants. However, a significant portion (57.6%) lacked awareness of hydrological changes, potentially impeding effective land use planning and adaptation measures.Further analysis through remote sensing and mapping techniques revealed shifts in land use land cover, highlighting a transition from predominantly vegetated areas to built-up regions over the past three decades. The flood vulnerability assessment maps underscored substantial vulnerability across the Lower Niger basin, especially in downstream regions, indicating heightened risks due to proximity to the Niger River and heavy rainfall.Qualitative insights from stakeholder interviews identified challenges impeding effective land use planning, including uncertain climate projections, insufficient data, institutional capacity constraints, and conflicting priorities. Additionally, participants emphasized the need for resilient land use decisions to address urbanization impacts, altered drainage patterns, and diminishing green spaces. The study recommends several key actions to enhance the effectiveness of land use planning and climate adaptation in the Lower Niger River region. These include the imperative to strengthen institutional capacities, improve stakeholder engagement processes, integrate traditional knowledge and practices into planning efforts, fortify legal frameworks governing land use, and prioritize the promotion of sustainable livelihoods among local communities.

Refining solutions of development problems of the Volga-Ural oil and gas province fields using geological and statistical model ranking methods

The purpose of the present research is to provide a comprehensive analysis of data on the geological and physical properties of formations and the fluids saturating them in the Volga-Ural oil and gas province using the methods of geological and statistical model ranking. The discriminant analysis conducted on the basis of qualitative criteria (reservoir type and stratigraphic confinement) identified in all cases the zones of uncertainty, which affect the effectiveness of managerial decision-making in the conditions of analog objects. On this score, the results for six models were refined and updated according to the principle of rank uniqueness value calculation by three methods, both for each model individually and for model systems while using them within the obtained distributions of objects in the axes of canonical discriminant functions. Theoretical and practical recommendations were given regarding the use of geological and statistical models in the development of Volga-Ural oil and gas province fields. The results obtained can be used to solve a wide range of practical problems of proactive resource management, which enable effective determination of the best strategy for the successful extraction of residual and hard-to-recover oil reserves. The proposed parameter ranking table allows both to determine the most unstable parameters with a high degree of probability and to level the factor of heterogeneity and disequilibrium of field data. The conducted study established that identification of object association with a particular group in the axes of canonical discriminant functions leads to the formation of the zone of uncertainty. The latter increases the risks of making ineffective managerial decisions when developing different categories of subsoil users’ assets. Using the methods of ranking geological and statistical models, an algorithm for constructing a hierarchical system is proposed, which allows to expand the application field of the results of geological and statistical modeling in the oil and gas industry as well as to reduce the risk of nonrepresentative results.

PROVIDENT: Development and Validation of a Machine Learning Model to Predict Neighborhood-level Overdose Risk in Rhode Island.

Drug overdose persists as a leading cause of death in the United States, but resources to address it remain limited. As a result, health authorities must consider where to allocate scarce resources within their jurisdictions. Machine learning offers a strategy to identify areas with increased future overdose risk to proactively allocate overdose prevention resources. This modeling study is embedded in a randomized trial to measure the effect of proactive resource allocation on statewide overdose rates in Rhode Island (RI). We used statewide data from RI from 2016 to 2020 to develop an ensemble machine learning model predicting neighborhood-level fatal overdose risk. Our ensemble model integrated gradient boosting machine and super learner base models in a moving window framework to make predictions in 6-month intervals. Our performance target, developed a priori with the RI Department of Health, was to identify the 20% of RI neighborhoods containing at least 40% of statewide overdose deaths, including at least one neighborhood per municipality. The model was validated after trial launch. Our model selected priority neighborhoods capturing 40.2% of statewide overdose deaths during the test periods and 44.1% of statewide overdose deaths during validation periods. Our ensemble outperformed the base models during the test periods and performed comparably to the best-performing base model during the validation periods. We demonstrated the capacity for machine learning models to predict neighborhood-level fatal overdose risk to a degree of accuracy suitable for practitioners. Jurisdictions may consider predictive modeling as a tool to guide allocation of scarce resources.

RLPRAF: Reinforcement Learning-Based Proactive Resource Allocation Framework for Resource Provisioning in Cloud Environment

RLPRAF: Reinforcement Learning-Based Proactive Resource Allocation Framework for Resource Provisioning in Cloud Environment

Insights from home parenteral nutrition infusion data

ObjectivePatients with chronic intestinal failure use home parenteral nutrition infusion support. Non-compliance of home parenteral nutrition treatment is well documented, especially if clinical resources are remote. Objective delivery data from Infusion Pump reports have the potential to support treatment progress and planning. The aim of this study was to report the efficacy and accuracy of the Eitan Insights digital health platform for home parenteral nutrition use (a platform providing data-driven insights from the pump-recorded data). MethodsA prospective, single-center observational study of 20 patients treated with home parenteral nutrition ≥3 d/wk was conducted over 2022. The patients recorded the pre- and postinfusion home parenteral nutrition bag weight, duration of infusion, and alarms. We compared manual records to the pump data. Repeated measures analysis of variance was used for statistical analysis. ResultsA total of 45 data sets were collected, with no adverse events noted. In multiple comparisons between patient factors and descriptive statistics, there was no significant difference between manually recorded and pump-recorded data for volume infused (mean values of manual versus pump were 1707 ± 362 mL and 1708 ± 405 mL; P = 0.939) and infusion duration (mean values of manual versus pump iwere 9h 43 min ± 2.48 SD versus 9h 45 min ± 2.41 SD; P = 0.858). ConclusionThe data collected by the digital platform accurately reflect patients’ infusion data. This connected device has the potential to allow clinicians to be more informed and assess treatment trends and proactive resource planning through the Infusion Pump data insights.

Proactive Resource Allocation Policy for Microsoft Azure Cognitive Search

Modern cloud services aim to find the middle ground between quality of service and operational cost efficiency by allocating resources if and only if these resources are needed by the customers. Unfortunately, most industrial demand-driven resource allocation approaches are reactive. Given that scaling mechanisms are not instantaneous, the reactive policy may introduce delays to latency-sensitive customer workloads and waste operational costs for cloud service providers. To solve this catch-22, we define the proactive resource allocation policy for Microsoft Azure Cognitive Search. In addition to the current resource demand, the proactive policy takes the typical resource usage patterns into account. We gained the following valuable insights from these patterns over several months of production workloads. One, 87% of the workload is stable due to continuous resource demand. Two, 90% of varying demand is predictable based on a few weeks of historical traces. Three, resources can be reclaimed 52% of the time due to extensive idle intervals of varying workload. Given the size and scope of our analysis, we believe that our approach applies to any latency-sensitive cloud service.

Proactive Resource Autoscaling Scheme Based on SCINet for High-Performance Cloud Computing

Proactive Resource Autoscaling Scheme Based on SCINet for High-Performance Cloud Computing