Optimal Resource Allocation Research Articles

Machine Learning-Based Resource Allocation Algorithm to Mitigate Interference in D2D-Enabled Cellular Networks

Mobile communications have experienced exponential growth both in connectivity and multimedia traffic in recent years. To support this tremendous growth, device-to-device (D2D) communications play a significant role in 5G and beyond 5G networks. However, enabling D2D communications in an underlay, heterogeneous cellular network poses two major challenges. First, interference management between D2D and cellular users directly affects a system’s performance. Second, achieving an acceptable level of link quality for both D2D and cellular networks is necessary. An optimum resource allocation is required to mitigate the interference and improve a system’s performance. In this paper, we provide a solution to interference management with an acceptable quality of services (QoS). To this end, we propose a machine learning-based resource allocation method to maximize throughput and achieve minimum QoS requirements for all active D2D pairs and cellular users. We first solve a resource optimization problem by allocating spectrum resources and controlling power transmission on demand. As resource optimization is an integer nonlinear programming problem, we address this problem by proposing a deep Q-network-based reinforcement learning algorithm (DRL) to optimize the resource allocation issue. The proposed DRL algorithm is trained with a decision-making policy to obtain the best solution in terms of spectrum efficiency, computational time, and throughput. The system performance is validated by simulation. The results show that the proposed method outperforms the existing ones.

Long-Term Fine-Grained Forecasts of Emergency Demand Using EMS Big Data and Population Estimates

In recent years, Japan has been facing a super-aging society, impacting various sectors. The issues of overcrowded emergency medical services and the increase in emergency transport cases are particularly severe, necessitating urgent responses. The research group, in collaboration with the Kobe City Fire Department, is conducting a study focusing on long-term fine-grained forecasts of emergency demand. This study aims to provide indicators for the strategic deployment of emergency teams and the scaling of medical facilities. By utilizing regional mesh data, it can capture local characteristics far more precisely than previous city or district-level forecasts, offering significant advantages for local government policy implementation. In the prediction process, there are two stages to leverage national land numerical information and improve the accuracy of the forecast model. The results reveal regional disparities in future emergency demand, enabling optimal allocation of emergency resources and strategic planning for emergency services.

Improvement of Optimal Human Resource Allocation Algorithm Based on Deep Latent Semantic Model

With the advances of technology, the banking industry faces the challenge of processing large-scale, heterogeneous human resource data. Traditional methods have difficulties in providing efficient and accurate analysis and prediction. This paper proposes an optimized human resource allocation algorithm based on deep latent Semantic Model (DLSM), which improves the accuracy and efficiency of data analysis by integrating deep neural network (DNN) and transfer learning technology. The experimental results show that the DLSM model performs well in text classification and information retrieval tasks, with the accuracy, accuracy, recall and F1 scores improving by 7.5%, 5.5%, 6.0% and 5.8%, respectively. This study confirms the excellent performance of DLSM model in HR data processing, provides an efficient and scientifically based solution for recruitment optimization in the banking industry, and enhances the competitiveness of enterprises.

Norwegian and Swedish value sets for the EORTC QLU-C10D utility instrument.

This study aimed to develop utility weights for the European Organization for Research and Treatment of Cancer (EORTC) QLU-C10D, a cancer-specific utility instrument, tailored to the Norwegian and Swedish populations. The utility weights are intended for use in the specific welfare contexts of Norway and Sweden to support more precise healthcare decision-making in cancer treatment and care. This cross-sectional study included 1019 Norwegian and 1048 Swedish participants representative in age and gender of the two general populations. Participants completed a discrete choice experiment involving 960 choice sets, each consisting of two EORTC QLU-C10D health states described by the instrument's domains and the duration of each state. Utility weights were calculated using generalized estimation equation models, and non-monotonic levels were merged to ensure consistent valuation. In the Norwegian participants, the largest utility decrements were seen for the domain of physical functioning (decrement of -0.263 for highest level "very much"), followed by pain (decrement -0.205 for level "very much") and role functioning (-0.139). Among the cancer-specific domains, nausea had the largest utility decrement (-0.124). In the Swedish participants, the largest utility decrements were also observed for physical functioning (-0.207 for the response "very much"), followed by pain (-0.139), role functioning (-0.133), and nausea (-0.119). Emotional functioning also exhibited a sizable utility decrement (-0.115). This study provides the first set of utility weights for the EORTC QLU-C10D specific to Norway and Sweden, reflecting the unique health preferences of these populations. The generated utility decrements can inform cost-utility analyses and optimize resource allocation in cancer care within the Norwegian and Swedish healthcare systems.

Predicting Intensive Care Admission in Children with Acute Asthma: A Meta-Analysis of Predictive Models

Background: Acute asthma is a common cause of pediatric emergency department visits and hospitalizations. Early identification of children at high risk of requiring intensive care unit (ICU) admission is crucial for optimal management and resource allocation. This meta-analysis aimed to evaluate the performance of predictive models for ICU admission in children presenting with acute asthma. Methods: A systematic search of PubMed, Embase, and Cochrane Library was conducted for studies published between 2013 and 2024 that developed or validated predictive models for ICU admission in children with acute asthma. Studies reporting sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) were included. Methodological quality was assessed using the QUADAS-2 tool. Pooled estimates of diagnostic accuracy were calculated using a random-effects model. Results: Six studies (n = 2,850 children) met the inclusion criteria. The predictive models included clinical features (respiratory rate, oxygen saturation, accessory muscle use), lung function measures (peak expiratory flow rate), and blood gas analysis. Pooled sensitivity ranged from 0.71 (95% CI 0.59-0.82) to 0.78 (95% CI 0.72-0.83), specificity from 0.79 (95% CI 0.75-0.83) to 0.86 (95% CI 0.78-0.91), and AUROC from 0.79 (95% CI 0.72-0.86) to 0.88 (95% CI 0.84-0.92). Conclusion: Several predictive models demonstrate moderate to high accuracy in identifying children with acute asthma at risk of ICU admission. However, heterogeneity in model performance highlights the need for further research to validate existing models in diverse populations and develop more robust tools to guide clinical decision-making.

Computational Math Modeling and AI Optimization for Better Decision-Making: Applications in Machine Learning

The integration of computational mathematical modeling, optimization, machine learning (ML), data analysis, and artificial intelligence (AI) offers transformative opportunities to solve complex problems across diverse fields. This paper delves into the development and application of advanced modeling techniques that blend traditional mathematical approaches with advanced AI algorithms to enhance predictive accuracy, optimize resource allocation, and improve decision-making processes in areas such as logistics, finance, engineering, and healthcare. By leveraging data-driven insights, these models can simulate real-world scenarios and identify optimal solutions more effectively than conventional methods. The paper also explores how AI-enhanced modeling can impact broader systems by reshaping industry practices, influencing frameworks, and potentially challenging established societal norms. Eventually, the paper argues for a balanced and informed deployment of AI-driven modeling techniques to maximize their benefits while addressing potential risks and limitations.

Predictive Analytics in ADAS Development: Leveraging CRM Data for Customer-Centric Innovations in Car Manufacturing

The rapid evolution of Advanced Driver Assistance Systems (ADAS) has transformed the automotive landscape, necessitating a shift towards more customer-centric development strategies. This paper explores the integration of predictive analytics with Customer Relationship Management (CRM) data to foster innovations in ADAS development. By harnessing insights from customer interactions, preferences, and feedback, manufacturers can anticipate market demands and tailor ADAS features to enhance user experience. Through a comprehensive analysis of case studies and industry practices, we demonstrate how predictive analytics can improve decision-making processes, facilitate the identification of emerging trends, and optimize resource allocation. The findings underscore the potential of leveraging CRM data to drive customer- focused innovations, ultimately resulting in enhanced vehicle safety, satisfaction, and competitive advantage in the automotive sector.

Cloud Platform Business Scenario Modeling Based on Multi-Model Fusion Recommendation Algorithm

In the era of cloud computing, businesses are increasingly relying on cloud platforms to streamline their operations and deliver services efficiently. Recommendation systems play a pivotal role in suggesting suitable services and resources to enhance user experience and optimize resource allocation. This paper presents a novel approach, the Multi-Model Fusion Recommendation Algorithm (MMFRA), which integrates multiple recommendation models using advanced fusion techniques to enhance the accuracy of recommendations in cloud platform business scenarios. The implementation process of MMFRA involves combining diverse recommendation models, such as collaborative filtering, content-based filtering, and matrix factorization, into a unified framework that leverages their strengths while mitigating individual limitations. This fusion process is designed to achieve higher precision in service recommendations by considering various aspects of user behavior and preferences. Through a comprehensive evaluation in a simulated cloud platform environment, MMFRA demonstrates superior performance in terms of recommendation accuracy and user satisfaction. The proposed algorithm offers significant potential for enhancing the effectiveness of cloud platform services, ultimately benefiting both service providers and users.

Research on Spatial Pattern and Accessibility of Traditional Villages-Taking Henan Province as an Example

Abstract. Chinese culture has a long history. Traditional villages carry the breath of history. The great cultural attraction of traditional villages to people and the implementation of rural revitalization policies, it makes the attention to traditional villages gathered. Henan Province has a deep cultural heritage, he has many traditional villages, widely distributed. Therefore, it is particularly important to study the spatial pattern and accessibility of national-level traditional villages in Henan Province. This study takes 275 national-level traditional villages in Henan Province as research objects, using GIS analysis with accessibility to study the spatial pattern of traditional villages in Henan Province and to demonstrate the accessibility of traditional villages in prefectural-level cities in Henan Province at the municipal scale. Studies have shown that the spatial pattern and accessibility of the distribution of traditional villages in Henan Province are closely related to many factors (transportation road network, distance from the central city, etc.). The accessibility values of traditional villages in each prefecture-level city vary greatly, indicating that there are obvious differences in the accessibility of traditional villages in Henan Province. It intuitively reflects the spatial distribution and accessibility of traditional villages in Henan Province, with a view to providing a reference for the tourism development of traditional villages. Providing an analytical basis for promoting the optimal allocation of traditional village resources.

Computing Unit and Data Migration Strategy under Limited Resources: Taking Train Operation Control System as an Example

There are conflicts between the increasingly complex operational requirements and the slow rate of system platform upgrading, especially in the industry of railway transit-signaling systems. We attempted to address this problem by establishing a model for migrating computing units and data under resource-constrained conditions in this paper. By decomposing and reallocating application functions, optimizing the use of CPU, memory, and network bandwidth, a hierarchical structure of computing units is proposed. The architecture divides the system into layers and components to facilitate resource management. Then, a migration strategy is proposed, which mainly focuses on moving components and data from less critical paths to critical paths and ultimately optimizing the utilization of computing resources. Specifically, the test results suggest that the method can reduce the overall CPU utilization by 27%, memory usage by 6.8%, and network bandwidth occupation by 35%. The practical value of this study lies in providing a theoretical model and implementation method for optimizing resource allocation in scenarios where there is a gap between resource and computing requirements in fixed-resource service architectures. The strategy is compatible for distributed computing architectures and cloud/cloud–edge-computing architectures.

Implementation of the ASEAN Political-Security Community (APSC) Blueprint by Indonesia in Handling Rohi Ngya Refugees in Aceh (Community Security Perspective)

The ongoing humanitarian crisis faced by Rohingya refugees has drawn significant attention across Southeast Asia, particularly regarding Indonesia’s response under the ASEAN Political-Security Community (APSC) Blueprint. This study aims to analyze Indonesia's implementation of APSC policies in handling Rohingya refugees in Aceh, focusing on the balance between community security and humanitarian obligations. Using a qualitative approach, this research employs a case study method, analyzing data from policy reports, official documents, and community feedback. The findings indicate that while Indonesia has made efforts to assist refugees, challenges persist due to limited legal frameworks and mixed local reception. The study reveals that community security concerns, alongside the lack of comprehensive refugee policies, have led to social tensions in Aceh, impacting both the refugee population and the host community. The research concludes that to enhance the effectiveness of APSC policies, Indonesia must foster greater community engagement and optimize resource allocation, thus promoting a more stable environment for both refugees and local residents.

Agentic AI in Predictive AIOps: Enhancing IT Autonomy and Performance

The integration of Agentic Artificial Intelligence (AI) within Predictive AIOps (Artificial Intelligence for IT Operations) is revolutionizing the management of IT systems, significantly enhancing IT autonomy and performance (Smith & Johnson, 2023). This article explores the potential of Agentic AI to empower AIOps platforms in proactively predicting, identifying, and resolving system issues. By leveraging predictive analytics and machine learning, AIOps not only enhances operational efficiency but also minimizes downtime and supports autonomous decision-making in complex IT environments (Lee et al., 2022). We examine the key roles that Agentic AI plays in improving performance metrics, optimizing resource allocation, and reducing the reliance on human intervention in critical system operations (Garcia & Patel, 2024). Additionally, this study investigates the implications for IT infrastructure scalability, long-term resilience, and the evolution toward self-governing systems (Chen, 2023). The findings underscore the transformative impact of Agentic AI on future IT operations, showcasing its potential to foster higher levels of automation and operational intelligence.

On weighted sum‐rate maximization of full‐duplex systems in presence of STAR‐RIS

AbstractReflecting intelligent surface (RIS) is one of the key enabling technologies for beyond fifth generation wireless networks to further improve coverage, spectral‐ and energy‐efficiency of wireless networks. Recently, a novel simultaneous transmission and reflection RIS (STAR‐RIS) technology is introduced which enables more degrees‐of‐freedom in simultaneously serving users in reflection and transmission regions of RIS. This paper investigates a STAR‐RIS assisted two‐user full‐duplex communication system, wherein multi‐antenna base station (BS) and single‐antenna users are subject to maximum power constraints. Firstly, weighted sum rates are computed for three well‐known STAR‐RIS protocols, e.g. energy splitting, mode selection, and time splitting. Then, for each of the cases, weighted sum rate optimization problem is investigated to find optimal resource allocations, i.e. base station's precoding and combining matrices, coefficients of STAR‐RIS, and power allocations. The optimization problem is transferred into multiple convex sub‐problems using equivalent weighted minimum mean‐square‐error forms. Also, by use of the successive convex approximation method, tunable parameters of the STAR‐RIS are optimized. Although the original problem is a non‐convex one, proposed iterative alternating technique achieves an acceptable sub‐optimal performance. Finally, performance of the system is analysed and compared to some baselines to highlight superiority of the STAR‐RIS compared to the conventional RIS schemes.

Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm3) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

REPLANTEAMIENTO DE LA PLANIFICACIÓN DE PROYECTOS MINEROS: USO DE NASA-TLX PARA MEDIR UNA NOVEDOSA INTEGRACIÓN ENTRE OTSM-TRIZ Y DSM

This research introduces an integrated methodology combining OTSM-TRIZ and the Design Structure Matrix (DSM) to enhance project planning and risk management in mining projects. OTSM-TRIZ, through a Network of Problems (NoP), systematically addresses complex challenges, while DSM analyzes task dependencies, optimizing resource allocation and sequencing. Applied in a real-world Chilean mining company with over 120 workers, the methodology improved outcomes by reducing trial-and-error iterations and boosting efficiency. Workers gained valuable insights into managing project complexity. NASA-TLX was used to explore usefulness authors’ proposal. Keywords: Mining Projects, OTSM-TRIZ, DSM, NASA-TLX

Probing an LSTM-PPO-Based reinforcement learning algorithm to solve dynamic job shop scheduling problem

With the growth of personalized demand and the continuous improvement in social productivity, the large-scale and few-variety centralized production model is gradually transitioning towards a personalized model of small batches and multiple varieties, which makes the manufacturing process of the job shop increasingly complex. Furthermore, disruptive events such as machinery failures and rush orders in the job shop increase the uncertainty and variability of the production environment. Traditional scheduling methods are usually based on fixed rules and heuristic algorithms, which are difficult to adapt to constantly changing production environments and demands. This may lead to inaccurate scheduling decisions and hinder the optimal allocation of job shop resources. To solve the dynamic job shop scheduling problem (JSP) more effectively, this paper proposes a Reinforcement Learning (RL) optimization algorithm integrating long short-term memory (LSTM) neural network and proximal policy optimization (PPO). It can dynamically adjust scheduling strategies according to the changing production environment, achieving comprehensive status awareness of the job shop environment to make optimal scheduling decisions. First, a state-aware network framework based on LSTM-PPO is proposed to achieve real-time perception of job shop state changes. Then, the state and action space of the job shop are described within the context of the state-aware network framework. Finally, an experimental environment is established to verify the algorithm’s effectiveness. Training the LSTM-PPO algorithm makes it feasible to achieve better performance than other scheduling methods. By comparing the initial planning time with the actual completion time of the rescheduling decision under different dynamic disturbances, the efficiency of the proposed algorithm is verified for the dynamic JSP

The global, regional, and national patterns of change in the burden of congenital birth defects, 1990–2021: an analysis of the global burden of disease study 2021 and forecast to 2040

Congenital birth defects (CBDs) present enormous challenges to global healthcare systems. These conditions severely impact patients' health and underscore issues related to socioeconomic development and healthcare accessibility and efficiency. Previous studies have been geographically limited and lacked comprehensive global analysis. This study provides global, regional, and national disability-adjusted life years (DALYs) data for four major congenital birth defects-congenital heart defects (CHD), neural tube defects (NTDs), digestive congenital anomalies (DCAs), and Down syndrome (DS) from 1990 to 2021, emphasizing health inequalities. The goal is to offer scientific evidence for optimizing resource allocation, focusing on high-burden populations, and reducing disease burden. This study systematically evaluated the global, regional, and national burden of CBDs and their changes from 1990 to 2021 using the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. To conduct a more focused analysis, four specific CBDs were selected: CHD, NTDs, DCAs, and DS. DALYs were used as the metric, combined with the sociodemographic index (SDI). Analyses included the slope index of inequality and concentration index to measure health inequalities, frontier analysis to estimate achievable outcomes based on development levels, decomposition analysis to identify drivers of disease burden changes, Joinpoint regression analysis to assess temporal trends, and the Bayesian age-period-cohort (BAPC) model to predict future disease burden trends. Compared to 1990, the global burden of the CBDs in 2021 showed a downward trend. Males had a higher burden than females, with the highest burden observed in low-SDI regions. When examining CHD, NTDs, DCAs, and DS specifically, trends in burden changes varied across different CBDs at the global, regional, and national levels. Frontier analysis revealed potential for burden improvement in various countries and territories. Decomposition analysis highlighted differences in disease burden drivers across SDI regions, showing the greatest improvement observed in low-SDI regions. Joinpoint regression analysis indicated a downward trend in DALYs burden across SDI regions, and BAPC model predictions suggested that the burden of CBDs will continue to decline in the future. CBDs pose a major challenge to global public health. Despite an overall decline in disease burden, health inequalities remain prominent, particularly in countries and territories with lower levels of development. Future public health interventions should focus on countries and territories with low levels of development by optimizing healthcare resource allocation, improving basic health infrastructure, enhancing health education, and reducing disease burden inequalities. Global collaboration and data sharing are essential to promote a lifecycle management model for CBDs research and treatment, advancing global health development. This study was supported by the National Natural Science Foundation of China (No. 82270310) and the Jiangsu Provincial Key Research and Development Program (No. BE2023662).

MCMS submersibles: A safe undersea adventure

Abstract. Maritime Cruises Mini-Submarines (MCMS), a company based in Greece, aims to offer safe and deep-sea exploratory experiences to tourists. This paper discusses the primary challenges involved in ensuring tourist safety, particularly focusing on maintaining security in scenarios of mechanical failures or lost communication with the main vessel. We propose comprehensive safety protocols and technological solutions tailored for deep-sea tourist submersibles. The analysis begins with a detailed study of the forces acting on a submersible, considering both operational modes and the impact of ocean currents, utilizing a rigid-body coordinate system and Eulerian angular coordinate transformations to describe the submersibles motion comprehensively. We derive motion and safety parameters by solving the force differential equations, allowing the host ship to predict the submersibles position accurately. In addressing the recovery of lost submersibles, our approach evaluates power loss scenarios and search efficiency, recommending an optimal combination of hydroacoustic and frequency-hopping communication technologies for reliable, low-loss data transmission. We further enhance search methodologies by integrating motion parameters and ocean current dynamics to pinpoint probable locations, adjusting search strategies dynamically based on real-time data, and employing probabilistic models to optimize resource allocation during search operations. A case study in the Caribbean Sea exemplifies applying these methodologies, incorporating localized flow regimes and cooperative communication strategies among multiple submersibles to improve operational efficiency and safety.

Personalized Recommendation Intelligent Fuzzy Clustering Model for theTourism

Personalized recommendations in tourism have transformed the way travellers explore new destinations, offering tailored experiences that align with individual preferences and interests. As the travel industry increasingly harnesses data analytics and artificial intelligence, tourists can now receive customized suggestions that reflect their unique tastes, whether they seek adventure, relaxation, cultural immersion, or culinary exploration. This paper explores the implementation and effectiveness of intelligent tourism management strategies using fuzzy clustering and personalized recommendations. By analyzing various scenarios, including baseline, personalized recommendations, dynamic pricing, crisis management, and enhanced resource allocation, the study demonstrates how advanced data-driven techniques can significantly improve key performance indicators such as visitor satisfaction, total revenue, repeat visitation rates, and operational efficiency. The simulation results reveal that personalized recommendations and optimized resource allocation are particularly effective in enhancing visitor experiences and economic outcomes. Conversely, the analysis underscores the critical need for robust crisis management strategies to maintain performance during adverse events. This research provides valuable insights into the transformative potential of intelligent systems in modern tourism management, offering a pathway towards more resilient and competitive tourism destinations. For instance,personalized recommendations increased average visitor satisfaction from 7.2 to 8.5, total revenue from $500,000 to $600,000, and the repeat visitation rate from 35% to 45%. The dynamic pricing strategy improved visitor satisfaction to 7.8 and revenue to $550,000, while enhanced resource allocation resulted in a satisfaction rate of 7.9 and revenue of $570,000. Conversely, crisis management showed the importance of preparedness, as satisfaction dropped to 6.5 and revenue to $450,000. These results underscore the critical need for robust management strategies.

The Potential Role of Artificial Intelligence in Emergency Medicine and Medical Education

Artificial intelligence (AI) is increasingly recognized for its transformative potential in healthcare, particularly in emergency medicine. The fast-paced, highstakes nature of emergency departments (EDs) demands rapid decision-making, often under significant time and resource constraints. AI-driven solutions have already demonstrated their ability to enhance diagnostic accuracy, improve triage processes, and optimize resource allocation in emergency settings. However, AI’s potential extends beyond clinical practice into the realm of medical education, where large language models (LLMs) may offer novel opportunities for training future emergency medicine professionals.