Optimal Resource Allocation Research Articles

Optimal resource allocation in micro-organisms under periodic nutrient fluctuations

Although microorganisms often live in dynamic environments, most studies, both experimental and theoretical, are carried out under static conditions. In this work, we investigate the issue of optimal resource allocation in bacteria growing in periodic environments. We consider a dynamic model describing the microbial metabolism under varying conditions, involving a control variable quantifying the protein precursors allocation. Our objective is to determine the optimal strategies maximizing the long-term growth of cells under a piecewise-constant periodic environment. Firstly, we perform a theoretical analysis of the resulting optimal control problem (OCP), based on the application the Pontryagin’s Maximum Principle (PMP). We determine that the structure of the optimal control must be bang–bang, with possibly some singular arcs corresponding to optimal equilibria of the system. If the control presents singular arcs, then these can only be reached and left through chattering arcs. We also use a direct optimization method, implemented in the BOCOP software, to solve the studied OCP. Our study reveals that the optimal solution over a large time horizon is related to the one over a single period of the varying environment with periodic constraints. Moreover, we observe that the maximal average growth rate attainable under periodic conditions can be higher than the one under a constant environment. We further extend our analysis to conduct a qualitative comparison between the predictions from our model and some recent biological experiments on E. coli. This analysis particularly highlights the mechanisms of action of the ppGpp signaling molecule, thus providing relevant explanations of the experimental observations. In conclusion, our study corroborates previous research indicating that this molecule plays a crucial role in the regulation of resource allocation of protein precursors in E. coli.

Logistics supply of the eco-industrial parks’ residents

This paper provides a comprehensive analysis of the logistics supply for residents of eco-industrial parks and examines its influence on the competitiveness and sustainability of these parks. Eco-industrial parks, as integral components of circular economy systems, facilitate the efficient use of resources, reduction of environmental impact, and optimization of supply chain operations. The purpose of the paper is to assess the logistics systems within eco-industrial parks, identify the challenges and opportunities for enhancing operational efficiency, and explore how these strategies can be adapted to the Ukrainian context, particularly during the ongoing war. The research draws upon international case studies, focusing on the integration of advanced technologies and green logistics practices in China, Europe, and other regions. The methodology used in this paper combines inductive analysis of literature, comparative analysis, and empirical research based on existing models of logistics management within EIPs. A key part of the analysis involves the evaluation of institutional pressures and their role in promoting sustainable supply chain management. The paper examines how these practices, when applied in a coordinated and efficient manner, help to reduce costs and carbon emissions, while also improving resource-sharing between companies located within eco-industrial parks. The results show that implementing circular supply chain management significantly enhances the performance of companies operating within EIPs. Firms that adopt renewable energy sources and optimize their transportation systems using electric vehicles report a 13-14% reduction in operational costs and carbon emissions. The integration of technologies such as Geographic Information Systems and Multi-Criteria Decision-Making tools has also proven beneficial in the site selection and planning of eco-industrial parks, allowing for the optimization of logistics and resource allocation. In Ukraine, which is currently facing critical challenges due to war, adopting these strategies can strengthen industrial clusters, reduce dependence on external resources, and enhance resilience against economic shocks. The scientific novelty of this research lies in the proposed structure of the logistics supply for eco-industrial parks’ residents. The practical implications of this paper include the application of green logistics technologies, the development of self-sustaining energy networks, and the implementation of closed-loop supply chains that promote resource efficiency. Future research directions should focus on the development of logistics optimization models for Ukrainian eco-industrial parks, particularly in war-torn areas, and the assessment of their long-term economic and environmental impact. Additionally, the paper advocates for the expanded use of renewable energy sources and Geographic Information Systems-based planning tools to further improve logistics processes and reduce the ecological footprint of industrial activities. The results of this paper underscore the importance of inter-firm cooperation and the strategic use of logistics to foster both economic and environmental resilience in eco-industrial settings.

Optimizing Higher Education for Entrepreneurship Digital economy and Innovation: A Novel Spatial Analysis Approach

The paper extensively examined the intricate components underpinning innovation ability, culminating in the construction of a linear spatial model delineating innovation and entrepreneurship prowess. This paper analyzed the components of the connotation of innovation ability, then constructs a linear spatial model of innovation and entrepreneurship ability, proposes a multi-objective function model of the utilization efficiency and allocation efficiency of education resources, and uses the grey correlation algorithm The experimental simulation and model solution are carried out. The simulation results show that, through the optimization, the utilization efficiency and allocation efficiency of the educational resources for innovation and entrepreneurship for all are increased by 18.72\% and 20.98\% respectively, and tend to be in equilibrium, which can achieve the optimization of educational resources allocation. Among all people, the correlation value with ideal entrepreneurship is 0.3177, achieving the most excellent innovation and entrepreneurship education.

Evaluation and Comparison of the Performance of Different Hypervisors under Various Workloads of GIADEC

This research study evaluates and compares the performance of Hyper-V, Citrix Xen, and VMware ESXi to aid Ghana Integrated Aluminium Development Corporation (GIADEC) in informed hypervisor selection. The study focuses on identifying the optimal hypervisor for GIADEC, considering factors like effectiveness and efficient resource utilization. It aims to improve resource allocation, hypervisor selection, and performance optimization within GIADEC's IT infrastructure. Using a qualitative approach and extensive literature review, the study supports informed decision-making for hypervisor selection. Through benchmarking with Passmark Performance Test Suite and BootRacer application, each hypervisor underwent rigorous testing across various performance metrics. The study assesses performance aspects including installation and booting times, CPU, memory, disk, and network performance. Results highlight distinct advantages of each hypervisor: Hyper-V for quick virtualization deployment, VMware ESXi for superior CPU, memory, and network performance. Hyper-V excels in memory write and disk sequential read/write speeds, while VMware ESXi demonstrates low memory latency and superior network bandwidth. It is recommended that GIADEC should consider VMware ESXi due to its outstanding overall performance and suitability for diverse workload requirements. This study adds to the knowledge base on hypervisor performance evaluation and supports GIADEC's decision-making process for optimal hypervisor selection.

Top-up Frequency Forecasting for Lao Telecommunication Company Using Time-Series Analysis

The study aimed to forecast the top-up frequency for Lao Telecommunication Company (LTC) customers using time-series analysis. Accurate forecasts help the company optimize resource allocation, improve customer service, and boost revenue by anticipating customer demand. The time-series analysis techniques are employed, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average + exogenous variables (SARIMAX) and exponential smoothing (ETS) models, to analyze historical top-up data. The data is pre-processed, identified patterns, and trained the model to forecast future top-up frequencies. The model's predictions closely aligned with the actual data, indicating its effectiveness. The ARIMA model produced reliable forecasts for short-term top-up frequencies, showing the best performance overall with the lowest RMSE and MAE, and a positive R-squared indicating some ability to explain the data. SARIMAX and EST both perform poorly, with very high RMSE and MAE, and negative R-squared values. The results demonstrated that time-series analysis could enhance decision-making in the telecommunications industry by enabling better forecasting of customer behavior. This helps in optimizing marketing strategies, inventory management, and service availability.

Analisis Efisiensi Operasional, Manajemen Risiko, dan Pengelolaan Sumber Daya terhadap Keberlanjutan Kinerja Keuangan Perusahaan di Indonesia

This study investigates the effects of Operational Efficiency, Risk Management, and Resource Management on the sustainability of corporate financial performance in Indonesian companies. A quantitative research approach was employed, using data from 170 companies across various industries. The data was analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The findings reveal that all three variables have a significant positive impact on financial sustainability. Operational Efficiency emerged as the most influential factor, followed by Risk Management and Resource Management. These results highlight the importance of an integrated approach to corporate strategy, focusing on improving operational processes, mitigating risks, and optimizing resource allocation to achieve long-term financial stability. The study offers practical insights for managers and policymakers on how to enhance corporate financial sustainability in Indonesia.

Data-driven decision-making in public health: The role of advanced statistical models in epidemiology

This paper critically examines the transformative role of data-driven decision-making in public health, focusing on the integration of advanced statistical models in epidemiology. As the volume and complexity of health data increase, leveraging predictive analytics, machine learning, and real-time data integration has become essential for improving public health outcomes. The study explores how these technologies have shifted public health strategies from reactive to proactive approaches, particularly in areas such as disease surveillance, chronic disease management, and health equity. Through comprehensive analysis, the paper identifies key advancements, such as hybrid models that combine traditional epidemiological frameworks with AI, and the integration of multi-modal data sources that enhance predictive accuracy. The findings emphasize the potential of these models to optimize resource allocation, address health disparities, and provide timely interventions. However, challenges such as data quality, algorithmic bias, and the ethical implications of model transparency are highlighted as critical issues requiring ongoing research. The study concludes that for these models to be effectively adopted, there must be a balance between technological innovation and ethical considerations. Recommendations include the need for interdisciplinary collaboration, improved data governance frameworks, and the development of more inclusive models that are generalizable across diverse populations. This research underscores the necessity of combining robust analytical tools with ethical frameworks to enhance the reliability and equity of public health interventions.

Virtualized network functions resource allocation in network functions virtualization using mathematical programming

Network Functions Virtualization (NFV) revolutionizes network services by eliminating the need for dedicated hardware. This virtualization enables flexible and efficient deployment of various network functions like proxies, firewalls, and load balancers. Providing the service requested by the user in the network is done by a sequence of virtual network functions, which are known as service functions chain. One of the main challenges in the development of network functions virtualization architecture is the allocation of resources to the requested network services in network infrastructures, this challenge is called network function virtualization resource allocation problem. Therefore, this paper addresses the resource allocation problem in Network Functions Virtualization (NFV) architectures using mathematical programming techniques. A multi-objective mixed-integer linear programming (MILP) model is proposed to optimize resource allocation for virtual network functions (VNFs). The model incorporates constraints related to node and link resource capacities, as well as delay requirements. The objective functions focus on maximizing network throughput, minimizing node resource costs (CPU cores and memory), reducing capital and operational expenses, and ensuring efficient execution time. These constraints and objective functions are formally defined by mathematical functions. The proposed mathematical model is implemented and solved using the Cplex solver. To evaluate the effectiveness of the proposed mathematical model, various network topologies were evaluated under different parameters. These parameters included the length of Service Function Chains (SFCs), the number and length of flows, node resource capacities, the number of nodes and VNFs. The experimental results demonstrated the model’s ability to efficiently allocate resources to VNFs across these different scenarios.

Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation

Artificial intelligence (AI) holds significant potential to revolutionize healthcare by improving clinical practices and patient outcomes. This research explores the integration of AI in healthcare, focusing on methodologies such as machine learning, natural language processing, and computer vision, which enable the extraction of valuable insights from complex medical imaging and clinical data. Through a comprehensive literature review, the study highlights AI’s practical applications in diagnostics, treatment planning, and predicting patient outcomes. Additionally, ethical issues, data privacy, and legal frameworks are examined, emphasizing the importance of responsible AI usage in healthcare. The findings demonstrate AI’s ability to enhance diagnostic accuracy, streamline administrative tasks, and optimize resource allocation, leading to personalized treatments and more efficient healthcare management. However, challenges remain, including data quality, algorithm transparency, and ethical concerns, which must be addressed to ensure safe and effective AI deployment. Continued research, collaboration between healthcare professionals and AI experts, and the development of robust regulatory frameworks are essential for maximizing AI’s benefits while minimizing risks. This research underscores the transformative potential of AI in healthcare and stresses the need for a multidisciplinary approach to address the ethical and regulatory complexities involved in its widespread adoption

The Role of Political Institutions in the Economic Development of Different Countries: A Comparative Study Between the United Kingdom and China

Institutions hold a vital position in society as they significantly mold human behavior as well as determine the social outcomes of a nation. In the modern world, institutions can take on formal or informal kinds. Notably, the formal ones are governed by several norms and legislation. Such formal institutions often lead to significant outcomes within the development process of a country. This particular research places a concentrated focus on the role of different types of institutions in the process of development, highlighting the impacts that a wide array of institutions have on social and economic outcomes. The study takes into account a democratic institution along with a centrally controlled institution. Through the analysis, it could be found that the centrally controlled institutions that are portrayed by China have a greater level of efficiency, which leads to more optimal allocation of resources and swifter growth, thereby contributing significantly to the overall progress and prosperity of the nation.

Village Government's Role in Advancing Agricultural Development Through Supportive Policies

Background: Indonesia, a tropical country with rich agricultural potential, faces challenges in optimizing its agricultural sector. Specific Background: Despite significant contributions to the economy and employment, issues like low human resource capacity and dependency on food imports hinder productivity. Knowledge Gap: The role of village governments in facilitating agricultural development, particularly in rural areas like Grabagan Village, remains underexplored. Aims: This study analyzes the village government's role in agricultural development in Grabagan Village, focusing on key success indicators. Results: Findings indicate that the village government functions effectively as a facilitator, mobilizer, and regulator, yet faces challenges in resource mobilization and farmer engagement. Novelty: This research enhances understanding of local governance's influence on agricultural outcomes. Implications: The study highlights the need for improved collaboration between the village government and farmers to optimize resource allocation and align agricultural initiatives with local needs for sustainable development. Highlights: The village government serves as a crucial facilitator in agricultural initiatives, providing necessary infrastructure and budget support. Effective mobilization of resources and farmer involvement in planning processes are essential for enhancing agricultural productivity. Addressing challenges in local governance can significantly improve sustainable agricultural development in rural areas. Keywords: Village Government, Agricultural, Farmer Empowerment, Facilitation, Mobilization

Research on the Path of Promoting Regional Financial Balance under the Background of the Digital Economy

The purpose of this paper is to explore the effective path to promote the balanced development of regional financial resources in the context of the digital economy. With the rapid development and wide application of digital technology, the digital economy has become a new engine to promote economic growth, but at the same time, it has also exacerbated the imbalance in the distribution of financial resources among regions. By analyzing the profound impact of the digital economy on the regional economic landscape, the study proposes strategies to optimize resource allocation, promote industrial upgrading, and broaden financing channels by using advanced technologies such as big data, cloud computing, and artificial intelligence, aiming at narrowing the gap in regional financial resources and achieving more balanced development. At the same time, the importance of policy guidance, regional cooperation, and talent cultivation is emphasized to provide theoretical support and practical paths for constructing a new pattern of balanced development of regional financial resources in the era of the digital economy.

Factors affecting household evacuation decision making in response to disaster: Case study from the 2021 South Kalimantan floods, Indonesia

Understanding the factors that influence evacuation decisions is crucial for optimizing resource allocation and formulating an effective emergency plan, particularly in the communication and implementation of warnings. To assist with this endeavour, this study investigates whether selected variables affected evacuation decision-making during the 2021 South Kalimantan, Indonesia, Floods. This is achieved through identifying significant factors in a binomial regression model constructed using a resident survey during the extreme floods of January 2021 and the more typical frequent floods of December 2021. The survey captures sociodemographic, capacity-related, hazard-related and warning-related factors from 375 residents including 205 in West Banjarmasin (urban area) and 170 in Tabuk River (rural area). It was found that families with partial ownership of their house (renting or living with another family) and those with a higher number of members are less likely to evacuate, as are those with a lack of knowledge about evacuation shelters and routes. External factors influencing people's evacuation decisions included the flood height and duration as well as whether there was an unofficial warning to evacuate. The findings suggest the following actions could assist with encouraging safety-seeking behaviour through evacuation: ensure evacuation instructions reach larger families and households and that they are relevant for them; include evacuation instructions with warnings; and conduct regular evacuation drills that include information about the flood risk map and the location and route to relevant evacuation shelters.

A hybrid BWM–CRITIC–VIKOR approach for assessing oil and gas risk scenarios in probabilistic linguistic term set

The oil and gas industry plays a crucial role in supporting energy supply and economic development. Mitigating risks in oil and gas projects is essential for reducing decision-making uncertainties, optimizing resource allocation, and fostering the competitiveness and sustainable growth of enterprises. Therefore, evaluating decision-making risks in the oil and gas sector is of significant practical importance. This paper proposes a comprehensive approach based on the BWM–CRITIC–VIKOR multi-criteria decision-making (MCDM) evaluation model. An evaluation index system encompassing six critical dimensions—technology, economics, environment, security, societal impact, and political factors—is established. To enhance assessment precision, the AHP interval estimation method is employed to identify and eliminate indicators with low contributions. Subsequently, the probabilistic linguistic term set (PLTS) is introduced into the best–worst method (BWM) and CRITIC methods to calculate the weights for each index. This approach helps to circumvent the subjectivity inherent in single subjective weighting methods. To evaluate and rank alternatives, the PLTS is used as an evaluation linguistic tool within the VIKOR framework. Finally, this methodology is applied to decision-making in the oil and gas industry, and its effectiveness and superiority are compared with the AHP–TOPSIS method and the BWM–CRITIC method.

Cyanobacterial blooms management: A simulation-based optimization method

Controlling cyanobacterial blooms is not only an engineering and technical issue but also an optimization problem in environment management. Under budget constraints, a novel simulation-based optimization model for cyanobacterial control is constructed in this study. The simulation model is used for simulating cyanobacteria growth and diffusion processes. The optimization model is utilized to determine the optimal search and treatment path. Through the interactive coupling of simulation modeling and resource allocation optimization, this research provides decision-makers with new operational guidelines for cyanobacterial control. Our test results demonstrate that the initial invasion frequency has a greater economic impact than invasion abundance. The nearby cells to the initial invasion are affected first, and then the influence radiates outward in a diffusion pattern. Using a slow search speed and a treatment frequency of every 10 days can achieve the lowest possible economic losses in most test scenarios. Moreover, we also find that the optimal search and treatment paths revolve around the initial invasion location. This study is a typical interdisciplinary research, which can assist water resource managers in making more accurate decisions regarding cyanobacteria removal paths, removal frequencies, and treatment speeds.

Research on the Relationship between Urban and Rural Educational Resources Allocation and Educational Equity

This comprehensive study delves deeply into the relationship between the allocation of educational resources in urban and rural areas and educational equity. It takes the unbalanced distribution of educational resources between urban and rural regions as its starting point and employs quantitative analysis and field research methods to explore this complex relationship. The findings reveal that the uneven allocation of educational resources has a significant negative impact on educational equity, resulting in a substantial disparity between urban and rural students in terms of access to high-quality education. Policy attention deviation, an imperfect management mechanism, and insufficient investment in education are identified as the key reasons for this unbalanced allocation. Consequently, this study recommends that policymakers increase investment in rural education, reform the management mechanism, and optimize resource allocation to promote the realization of educational equity. The results of this research hold great significance for understanding the unequal distribution of urban and rural educational resources and providing valuable references for policymaking.

Enhancing IT Project Portfolio Management Through Dynamic Dashboard Visualization and Integrated Chatbot and Mathematical ML Model for Strategic Decision Making

This project paper presents an innovative approach to enhance IT Project Portfolio Management (IT PPM) using dynamic dashboard visualization and machine learning (ML) techniques. Effective IT PPM is essential for organizations to align IT projects with business objectives and optimize resource allocation. However, conventional methods often lack innovation and real-time visibility, leading to suboptimal decision-making and costly software investments. The objective of this project is to develop a dynamic dashboard visualization system integrated with a machine learning model derived from the Meta-Portfolio Method (MPM). The MPM leverages advanced analytics and historical project data to dynamically allocate resources and prioritize projects based on risk-return profiles. By incorporating the MPM into the dashboard, organizations gain real-time insights into project status, progress, and resource allocation, enabling informed decision-making and proactive portfolio optimization. The ML model derived from the MPM analyzes historical project data, including success rates, resource intensity, criticality, regulatory compliance, and portfolio diversification, to predict optimal project selection and resource allocation. By dynamically adapting to changing market conditions and project dynamics, the ML model facilitates strategic decision-making and enhances portfolio performance. Through the integration of dynamic dashboard visualization and ML-driven decision support, organizations can streamline IT PPM processes, improve project outcomes, and achieve strategic objectives effectively and efficiently. This innovative approach empowers organizations to navigate the complexities of IT project portfolios with agility, foresight, and confidence.

Integrating IoT and visual question answering in smart cities: Enhancing educational outcomes

Emerging as a paradigmatic shift in urban development, smart cities harness the potential of advanced information and communication technologies to seamlessly integrate urban functions, optimize resource allocation, and improve the effectiveness of city management. Within the domain of smart education, the imperative application of Visual Question Answering (VQA) technology encounters significant limitations at the prevailing stage, particularly the absence of a robust Internet of Things (IoT) framework and the inadequate incorporation of large pre-trained language models (LLMs) within contemporary smart education paradigms, especially in addressing zero-shot VQA scenarios, which pose considerable challenges. In response to these constraints, this paper introduces an IoT-based smart city framework that is designed to refine the functionality and efficacy of educational systems. This framework is delineated into four cardinal layers: the data collection layer, data transmission layer, data management layer, and application layer. Furthermore, we introduce the innovative TeachVQA methodology at the application layer, synergizing VQA technology with extensive pre-trained language models, thereby considerably enhancing the dissemination and assimilation of educational content. Evaluative metrics in the VQAv2 and OKVQA datasets substantiate that the TeachVQA methodology not only outperforms existing VQA approaches, but also underscores its profound potential and practical relevance in the educational sector.

Socio-economic inequalities and heart failure morbidity and mortality: A systematic review and data synthesis.

Socio-economic status (SES) has been associated with incident and prevalent heart failure (HF), as well as its morbidity and mortality. However, the precise nature of the relationship between SES and HF remains unclear due to inconsistent data. This study aims to provide a comprehensive assessment and data synthesis of the relationship between SES and HF morbidity and mortality. We performed a systematic search and data synthesis using six databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines. The included studies comprised observational studies that reported on HF incidence and prevalence, HF hospitalizations, worsening HF (WHF) and all-cause mortality, as well as treatment options (medical, device and advanced HF therapies). SES was measured on both individual and area levels, encompassing single (e.g., income, education, employment, social risk score, living conditions and housing characteristics) and composite indicators. Among the 4124 studies screened, 79 were included, with an additional 5 identified through cross-referencing. In the majority of studies, a low SES was associated with an increased HF incidence (72%) and prevalence (75%). For mortality, we demonstrated that low SES was associated with increased mortality in 45% of the studies, with 18% of the studies showing mixed results (depending on the indicator, gender or follow-up) and 38% showing non-significant results. Similar patterns were observed for the association between SES, WHF, medical therapy prescriptions and the utilization of devices and advanced HF therapies. There was no clear pattern in the used SES indicators and HF outcomes. This systematic review, using contemporary data, shows that while socio-economic disparity may influence HF incidence, management and subsequent adverse events, these associations are not uniformly predictive. Our review highlights that the impact of SES varies depending on the specific indicators used, reflecting the complexity of its influence on health disparities. Assessment and recognition of SES as an important risk factor can assist clinicians in early detection and customizing HF treatment, while also aiding policymakers in optimizing resource allocation.

How artificial intelligence affects international industrial transfer ——Evidence from industrial robot application

The development of high technology represented by artificial intelligence is an essential driver of international industrial transfer. Using country-industry-level industrial robot data and value-added trade data from 2002-2018, this paper empirically examines the impact and mechanism of artificial intelligence on the international industrial transfer undertaken by a country (region). The study shows that the application of artificial intelligence can significantly enhance the international industrial transfer undertaken by a country (region). Further analysis shows that artificial intelligence positively affects international industry transfer through two channels: Improving the quality of labor force and promoting the level technological innovation. Further heterogeneity analysis shows that, first, from the economic development degree perspective, artificial intelligence's role in promoting developing economies is more substantial than that in developed economies. Second, the financial crisis has weakened the impact of artificial intelligence on international industrial transfer from the perspective of time division，especially in developing economies. Third, from the perspective of industry heterogeneity, artificial intelligence has significantly promoted the transfer of medium-low and medium-high technology industries. The research in this paper extends the analysis of the effect of artificial intelligence. It provides realistic insights for optimizing the strategic layout of the robotics industry, achieving industrial structure upgrading, optimizing resource allocation efficiency, and grasping the opportunities of global industrial chain restructuring.