Hasil untuk "Management information systems"

M. Alavi, D. Leidner

Paola Joseph, Colin Pennington

Background: Athlete' psychological well-being is fundamental to their success and resilience. Recognizing this critical link, understanding how to enhance mental health for improved sport performance is invaluable to the athletic community. Aims: This article aims to concisely examine the intricate relationship between athlete mental health and performance, synthesizing current information and strategies to optimize both. Methods: A systematic literature review was conducted to identify, select, and analyze relevant studies on the impact of mental health on athletic performance across various sports. A thematic narrative synthesis summarized findings, informing practical recommendations. Results: Findings consistently show that elite and collegiate athletes face comparable or heightened risks of mental health disorders due to intense competition, public scrutiny, and career uncertainties. Conversely, strong team cohesion and social support significantly enhance athletes' mental well-being and performance. Practical strategies include ensuring access to specialized mental health professionals, integrating mindfulness and relaxation techniques (e.g., Progressive Muscle Relaxation), utilizing mental health checklists for self-assessment, and implementing comprehensive coaching guidelines. These guidelines emphasize open communication, mental health education for coaches, and proactive stress management within training programs. Conclusion: The profound connection between mental health and athletic performance necessitates a proactive, multifaceted approach. At the same time, current research provides valuable insights, but limitations exist, including potential publication bias and a narrow time frame in many studies. Future research should prioritize longitudinal studies to understand long-term effects, explore technological interventions for accessibility, and rigorously evaluate student support services. These efforts will contribute to more effective, evidence-based support systems for athletes.

Dragoş Vicoveanu, Ovidiu Gherman, Iuliana Șoldănescu et al.

Personal health records (PHRs) are digital repositories that allow individuals to access, manage, and share their health information. By enabling patients to track their health over time and communicate effectively with healthcare providers, personal health records support more personalized care and improve the quality of healthcare. Their integration with emerging technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain, enhances the utility and security of health data management, facilitating continuous health monitoring, automated decision support, and secure, decentralized data exchange. Despite their potential, PHR systems face significant challenges, including privacy concerns, security issues, and digital accessibility problems. This paper discusses the fundamental concepts, requirements, system architectures, and data sources that underpin modern PHR implementations, highlighting how they enable continuous health monitoring through the integration of wearable sensors; mobile health applications; and IoT-enabled medical devices that collect, process, and transmit data to support proactive care and personalized treatments. The benefits and limitations of PHR systems are also discussed in detail, with a focus on interoperability, adoption drivers, and the role of advanced technologies in supporting the development of secure and scalable health information systems for a smarter world.

Manar Ashkanani, Alanoud AlAjmi, Aeshah Alhayyan et al.

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic violations. This paper proposes an adaptive traffic control and optimization system that dynamically adjusts signal timings in response to real-time traffic situations and volumes by applying machine learning algorithms to images captured through video surveillance cameras. This system is also able to capture the details of vehicles violating signals, which would be helpful for enforcing traffic rules. Benefiting from advancements in computer vision techniques, we deployed a novel real-time object detection model called YOLOv11 in order to detect vehicles and adjust the duration of green signals. Our system used Tesseract OCR for extracting license plate information, thus ensuring robust traffic monitoring and enforcement. A web-based real-time digital twin complemented the system by visualizing traffic volume and signal timings for the monitoring and optimization of traffic flow. Experimental results demonstrated that YOLOv11 achieved a better overall accuracy, namely 95.1%, and efficiency compared to previous models. The proposed solution reduces congestion and improves traffic flow across intersections while offering a scalable and cost-effective approach for smart traffic and lowering greenhouse gas emissions at the same time.

Pika Krištof Mirt, Karmen Erjavec, Sabina Krsnik et al.

Abstract BackgroundKnee osteoarthritis (KOA) requires long-term treatment that faces significant barriers, including inadequate physiotherapy services, especially in Slovenia and comparable European countries. Mobile health apps offer a promising solution to improve accessibility and adherence to KOA treatment. ObjectivesThis study aimed to identify expectations of patients with KOA for app-based therapy, determine the functional requirements, and assess the main barriers and benefits of using mobile apps for KOA management. It also examined these factors about demographic data (gender, age, and education level) and motivation to perform knee exercises. MethodsA mixed methods approach was used, integrating quantitative data from a structured questionnaire and qualitative data from in-depth interviews. The purposive sample comprised 82 patients with symptomatic KOA graded 1‐3 on the Kellgren-Lawrence scale, excluding those with cognitive impairments, wheelchair dependency, significant comorbidities, or language barriers. ResultsThe analysis revealed that 53.7% (44/82) of patients preferred smartphones, while 40.2% (33/82) favored PCs for remote KOA management, citing accessibility and convenience. Exercise videos received the highest rating (µ=9.45), followed by goal setting and tracking (µ=8.95) and regular e-messages (µ=8.83). Telephone consultations with physiotherapists were also highly valued (µ=8.41). Significant differences were observed in the perceived importance of key disease information (F9PF9PF9PF1P ConclusionsMobile health apps for KOA management should be designed with a user-centered approach, prioritizing accessibility, motivation, and effective communication. Key functionalities include high-quality exercise videos, goal setting, symptom tracking, and regular electronic reminders. Mitigating user-reported barriers and integrating age-specific adaptations can enhance adherence and therapeutic outcomes. The findings highlight the potential of mobile health technologies to optimize KOA self-management and improve patient quality of life, particularly in health care systems with limited physiotherapy accessibility, such as those in Slovenia.

Phumzile D. Mogoale, Agnieta Pretorius, Refilwe C. Mogase et al.

Background: Artificial intelligence (AI) technology is transforming education through personalised learning and creates dynamic, adaptive learning environments that cater to each student’s unique strengths and challenges. Developed countries have largely integrated AI technologies into their learning institutions, while the discipline is in its infancy in developing countries such as South Africa (SA). Objectives: This study aims to contextualise and recommend the strategy that institutions of higher learning in SA can adopt to integrate AI into their institutions. Method: A systematic literature review (SLR) method was followed. Publications published between 2018 and 2024 in the Multidisciplinary Digital Publishing Institute (MDPI) and Taylor Francis Online databases using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Following an initial search, 114 documents were retrieved, and, using inclusive criteria, 29 papers were chosen for analysis. The databases were selected because of their unique benefits in terms of accessibility, material breadth, and researcher-specific functions, unlike other sources. Results: Results show that to integrate AI, the following should be considered: planning, collaborations, training, and ethical standards to guarantee responsible use and productivity. This will enhance teaching and learning, well preparing students for a future whereby AI is widely used in the workplace. Conclusion: To integrate AI into learning institutions, a tailored approach needs to ensure that the AI technology improves teaching, enhances administrative procedures, and adheres to the institution’s rules and regulations. Contribution: This article forms a theoretical and methodological contribution to advancing knowledge that may inform policy and practice makers.

Kai Yin, Xiangjue Dong, Chengkai Liu et al.

Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios. To bridge this gap, we introduce DisastIR, the first comprehensive IR evaluation benchmark specifically tailored for disaster management. DisastIR comprises 9,600 diverse user queries and more than 1.3 million labeled query-passage pairs, covering 48 distinct retrieval tasks derived from six search intents and eight general disaster categories that include 301 specific event types. Our evaluations of 30 state-of-the-art retrieval models demonstrate significant performance variances across tasks, with no single model excelling universally. Furthermore, comparative analyses reveal significant performance gaps between general-domain and disaster management-specific tasks, highlighting the necessity of disaster management-specific benchmarks for guiding IR model selection to support effective decision-making in disaster management scenarios. All source codes and DisastIR are available at https://github.com/KaiYin97/Disaster_IR.

Guanghui YE, Kai TU, Lina HU, Li HAN, Zhiming FENG

[Purpose/Significance] Limited by the constraints of traditional literature classification systems, scientific and technical literature information resources face problems such as inadequate disclosure and resource utilization. At the same time, high-quality user-generated data cannot yet be integrated as data elements into services related to scientific and technical literature services, which prevents these services from adapting to the context of the open science and meeting the diverse knowledge needs of readers. This study aims to harness the technological breakthrough potential of AI to build a consumer-end data system for scientific and technical literature information resources driven by AI and experts. This will help to overcome the shortcomings of traditional services, such as the lack of supporting reading information and low interactivity between users, with the hope of promoting the optimization process of scientific and technical literature information resource services. [Method/Process] First, the study analyzes the four-dimensional value representation of the consumer-end data systems for scientific and technical literature information resources, including the intrinsic value, the tool value, the academic value, and the future value of annotation data. Then, following the processing flow of consumer-end data, namely the collection phase, utilization phase, and management phase, the paper proposes principles for the construction of consumer-end data systems. Furthermore, the paper deconstructs and analyzes the risks associated with the involvement of AI in the construction of consumer-end data systems, including four types of risks: machine algorithm risks, annotation content risks, annotation data risks and application risks. Finally, based on the degree of AI involvement in data annotation work, three innovative models of AI plus expert collaborates with user to accomplish data annotation for scientific and technical literature information resources are designed: the AI plus expert-assisted data annotation model, the AI plus expert collaborative data annotation model, and the AI plus expert-led data annotation model. [Results/Conclusions] Under the AI plus expert-assisted data annotation model, AI acts as a tool to complete surface-level information processing based on rules set by experts to assist users in data annotation. In the AI plus expert collaborative data annotation model, AI completes the review of pre-annotation tags for scientific and technical literature information resources, transforming users from a self-generated tag mode to an AI-generated data tag evaluation and selection mode, with experts assisting in the final review of data tag quality. In the AI plus expert-led data annotation model, users provide data annotation requirements, experts guide the process, and data annotation is automatically completed by the AI4S platform.

Zijie Huang, Wanjia Zhao, Jingdong Gao et al.

Learning complex physical dynamics purely from data is challenging due to the intrinsic properties of systems to be satisfied. Incorporating physics-informed priors, such as in Hamiltonian Neural Networks (HNNs), achieves high-precision modeling for energy-conservative systems. However, real-world systems often deviate from strict energy conservation and follow different physical priors. To address this, we present a framework that achieves high-precision modeling for a wide range of dynamical systems from the numerical aspect, by enforcing Time-Reversal Symmetry (TRS) via a novel regularization term. It helps preserve energies for conservative systems while serving as a strong inductive bias for non-conservative, reversible systems. While TRS is a domain-specific physical prior, we present the first theoretical proof that TRS loss can universally improve modeling accuracy by minimizing higher-order Taylor terms in ODE integration, which is numerically beneficial to various systems regardless of their properties, even for irreversible systems. By integrating the TRS loss within neural ordinary differential equation models, the proposed model TREAT demonstrates superior performance on diverse physical systems. It achieves a significant 11.5% MSE improvement in a challenging chaotic triple-pendulum scenario, underscoring TREAT's broad applicability and effectiveness.

Aref Ayati, Mohammad Mahdi Hashemi, Mohsen Saffar et al.

Regarding the concepts of urban management, digital transformation, and smart cities, various issues are presented. Currently, we like to attend to location allocation problems that can be a new part of digital transformation in urban management (such as locating and placing facilities, locating and arranging centers such as aid and rescue centers, or even postal hubs, telecommunications, electronic equipment, and data centers, and routing in transportation optimization). These issues, which are seemingly simple but in practice complex, are important in urban environments, and the issue of accurate location allocation based on existing criteria directly impacts cost management, profit, efficiency, and citizen satisfaction. In recent years, researchers have used or presented various models and methods for location allocation problems, some of which will be mentioned in this article. Given the nature of these problems, which are optimization problems, this article will also examine existing research from an optimization perspective in summary. Finally, a brief conclusion will be made of the existing methods and their weaknesses, and suggestions will be made for continuing the path and improving scientific and practical research in this field.

Harish Gadey, Lucas Vander Wal, Robert Joseph

The United States DOE Office of Integrated Waste Management program is planning for the transportation, storage, and eventual disposal of spent nuclear fuel and high-level radioactive waste from nuclear power plant sites across the United States. The Next Generation System Analysis Model is an agent-based simulation toolkit that is used for system level simulation and analysis of the SNF inventory in the United States. This tool was developed as part of a collaborative effort between Argonne National Laboratory and Oak Ridge National Laboratory. The analyst using NGSAM has the ability to define several factors like the number of storage facilities, capacity at each facility, transportation schedules, shipment rates, and other conditions. The primary purpose of NGSAM is to provide the system analyst with the tools to model the integrated waste management system and gain insights on waste management alternatives, impact of storage choices, generating cost estimates, and developing an integrated approach with emphasis on flexibility.

Alexey Terentyev, Alexey Marusin, Sergey Evtyukov et al.

The performance of this study involves the use of the zoning method based on the principle of the hierarchical relationship between probabilities. This paper proposes an analytical model allowing for the design of information and analysis platforms in intelligent transport systems. The proposed model uses a synthesis of methods for managing complex systems’ structural dynamics and solves the problem of achieving the optimal balance between the information situations existing for the object and the subject under analysis. A series of principles are formulated that govern the mathematical modeling of information and analysis platforms. Specifically, these include the use of an object-oriented approach to forming the information space of possible decisions and the division into levels and subsystems based on the principles of technology homogeneity and information state heterogeneity. Using the proposed approach, an information and analysis platform is developed for sustainable transportation system management, that allows for the objective, multivariate forecasting-based record of changes in the system’s variables over time for a particular process, and where decision-making simulation models can be adjusted in relation to a particular process based on an information situation existing for a particular process within a complex transport system. The study demonstrates a mathematical model that solves the optimal balance problem in organizationally and technically complex management systems and is based on vector optimization techniques for the most optimal decision-making management. The analysis involves classical mathematical functions with an unlimited number of variables including traffic volume, cargo turnover, safety status, environmental performance, and related variables associated with the movement of objects within a transport network. The study has produced a routing protocol prescribing the optimal vehicle trajectories within an organizationally and technically complex system exposed to a substantial number of external factors of uncertain nature.

Mohammad Dolatabadi, Alberto Borghetti, Pierluigi Siano

In this paper, a new method to address the scheduling problem of a renewable energy community while considering network constraints and users' privacy preservation is proposed. The method decouples the optimization solution into two interacting procedures: conic projection (CP) and linear programming (LP) optimization. A new optimal CP method is proposed based on local computations and on the calculation of the roots of a fourth-order polynomial for which a closed-form solution is known. Computational tests conducted on both 14-bus and 84-bus distribution networks demonstrate the effectiveness of the proposed method in obtaining the same quality of solutions compared with that by a centralized solver. The proposed method is scalable and has features that can be implemented on microcontrollers since both LP and CP procedures require only simple matrix-vector multiplications.

Monika Borzuchowska, Dorota Kilańska, Remigiusz Kozłowski et al.

<i>Introduction</i>: The outbreak of the COVID-19 pandemic was a period of uncertainty and stress for healthcare managers due to the lack of knowledge (about the transmission of the virus, etc.) and also due to the lack of uniform organisational and treatment procedures. It was a period where the ability to prepare for a crisis, to adapt to the existing conditions, and to draw conclusions from the situation were of critical importance to keep ICUs (intensive care units) operating. The aim of this project is to compare the pandemic response to COVID-19 in Poland during the first and second waves of the pandemic. This comparison will be used to identify the strengths and weaknesses of the response, including challenges presented to health professionals and health systems and ICUs with COVID-19 patients according to the European Union Resilience Model (2014) and the WHO Resilience Model (2020). The WHO Resilience model was suitable to the COVID-19 situation because it was developed based on this experience. <i>Methods</i>: A matrix of 6 elements and 13 standards assigned to them was created using the EC and WHO resilience guidelines. <i>Results</i>: Good governance in resilient systems ensures access to all resources without constraints, free and transparent flow of information, and a sufficient number of well-motivated human resources. <i>Conclusions</i>: Appropriate preparation, adaptation to the existing situation, and effective management of crisis situations are important elements of ensuring the resilience of ICUs.

Mahdy Mortazavi, Hosein Mohammadidoost, Raziyeh Dashti

IntroductionGovernance refers to focusing on processes through which collective groups can be managed. In this regard, non-public institutional mechanisms such as civil society have been increasingly expanded in governance processes (Bozzini and Enjolras 2011; Rhodes, 1997). In other words, the administration of affairs in a framework of non-hierarchical, systematic, and collaborative relationships and interactions that includes the real cooperation and interaction of all actors and stakeholders in a field, including government, private and civil society institutions, is referred to as governance.Due to the excessive expansion of the public sector in Iran, the third sector has not been able to have a proper position in responding to the needs of citizens as it deserves (Research Center Parliament, 2008); on the other hand, the public sector itself has not had sufficient efficiency and effectiveness due to its numerous administrative and economic problems, large size and high costs (Danai Fard and Abbasi, 2007). The endowment system is no exception to this rule. Endowment is an institution through which a significant part of the problems and bottlenecks of society can be recognized and the property and assets of good people can be used voluntarily to solve them. But so far, the impact of endowments on the growth and development of the country's economy, reducing economic inequalities and using the endowment capacity in difficult economic conditions has not been tangible (Khaksar Astana et al., 2014). Therefore, the main goal of this research is to formulate the governance model of the Endowment system in Iran, and the research question is, what is the governance model of the Endowment system in Iran? Research MethodologyThis research has been conducted with a qualitative approach, qualitative content analysis was used to analyze the data, and structural-interpretive modeling was used to validate the components of the model. Philosophically, this research has an interpretive approach and is developmental in terms of orientation.In this research, we use qualitative content analysis with a thematic analysis unit (Sandelowski, 1995). In this regard, we have used the method of Braun and Clark (2006):Stage 1: Familiarizing yourself with your data "repeatedly reading the data" and actively reading the data (looking for meanings and patterns).Stage 2: Creating initial conceptual identifiers from the dataStage 3: Categorizing different identifiers in the form of selective identifiers and sorting the identified data summary.Stage 4: Reviewing the primary themes createdStage 6: Defining and naming sub-themesStage 6: Review, comparison, and participation of experts, and final analysis and report writing (Hajipour et al., 2015).The structural-interpretive modeling strategy is a suitable method for identifying and designing the model of complex relationships between the components of a phenomenon (Attri et al, 2013). This method was first proposed and introduced by Warfield (1974).The data collection tool in the qualitative section was semi-structured documents and interviews, and in this stage, 22 experts were selected using non-probability sampling and snowball methods. In the stage of structural-interpretive modeling, the number of samples was 14 experts. Also, to measure the reliability and validity of the research findings, it was indicated that the coefficient of Cohen's kappa is 0.7544, and since it is more than 0.7, it indicates the appropriate reliability of the findings. Research FindingsThe governance model of the endowment system with 3 main themes of strategic factors, infrastructural factors, and consequences and effects, as well as 11 sub-themes of data and information management, spatial oriented strategic plan, structure and model of the endowment administration, coherence and integration, rule of law, accountability, transparency, participation, independence, outcomes, and effects and 46 components were compiled, and the dimensions of the model were organized in four levels Discussion and ConclusionThe three key axes and bottlenecks of endowment governance are explained below:Management of data and information instead of the management of endowments:To implement the trustee-based governance of endowments, information on endowments and real and legal trustees (charities), including the intentions, geography, qualifications of trustees, and the like, should be collected in the territorial arena (Charities Regulator, 2018).The structure and model of endowment administration:In the trustee-centered governance model, each endowment is considered a self-sufficient unit with a legal personality, and a board of trustees, a trustee, and a supervisor can be considered for its management. Therefore, in contrast to state-oriented governance, which has a simple approach, in trustee-oriented governance, a complex, non-hierarchical approach based on the endowment is dominant.The spatial-oriented strategic plan:Having a program based on local requirements, needs and capacities can provide further development and progress. It is obvious that endowments also have specific and special intentions in the field of land in different regions, and the spatial-oriented strategic plan should be formulated and implemented in accordance with this issue, and each region and sometimes each endowment (large endowment) has its own special plan (Chew, 2009). References:Attri, R., Devi, N. & Sharma, V. (2013). Interpretive structural modeling (ISM) approach: An overview. Research Journal of Management Sciences, 2(2), 3-8.Bozzini, E. & Enjolras, B. (Eds.). (2011). Governing ambiguities. Baden-Baden: Nomos Verlag.Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-10.Charities Regulator. (2018(. Charities Governance Code, Dublin.Chew, C. (2009). Strategic Positioning in Voluntary and Charitable Organizations. Publishing RoutledgeDanai Fard, H. & Abbasi, T. (2007). Administrative reforms in Iran: An analysis of government downsizing. Daneshvar Raftar, 15(29), 102-121.Hajipour, B., Moutamani, A. & Tayyebi Abolhasani, A.H. (2015). The combination of success factors for the commercialization of advanced technology products. Innovation Management, 5(4), 54-19.Khaksar Astane, H., Rahnama, Ali. & Ibrahim, H. (2014). Pathology of the position of the endowment institution in strengthening the country's economy. The first knowledge-based conference on resistance economy.Research Center Parliament. (2008). Pathology of non-governmental organizations in Iran. Rhodes, R. (1997). Understanding governance: Policy networks, governance, reflexivity, and accountability. Buckingham, PA: Open University Press.Sandelowski, M. (1995). Sample size in qualitative research. Res Nurs Health, 18(2), 179- 83.Warfield, J.N. (1974). Developing interconnection matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetic, 4(1), 81-87.

Wenyu Zhang, Yu Shen, Xuanyuan Wang et al.

As the main body of resource aggregation, Virtual Power Plant (VPP) not only needs to participate in the external energy market but also needs to optimize the management of internal resources. Different from other energy storage, hydrogen energy storage systems can participate in the hydrogen market in addition to assuming the backup supplementary function of electric energy. For the Virtual Power Plant Operator (VPPO), it needs to optimize the scheduling of internal resources and formulate bidding strategies for the electric-hydrogen market based on external market information. In this study, a two-stage model is constructed considering the internal and external interaction mechanism. The first stage model optimizes the operation of renewable energy, flexible load, extraction storage, and hydrogen energy storage system based on the complementary characteristics of internal resources; the second stage model optimizes the bidding strategy to maximize the total revenue of the electricity energy market, auxiliary service market and hydrogen market. Finally, a typical scenario is constructed and the rationality and effectiveness of the strategy are verified. The results show that the hybrid VPP with hydrogen storage has better economic benefits, resource benefits and reliability.

Julian Senoner, Bernhard Kratzwald, Milan Kuzmanovic et al.

To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribution of operational data -- so-called distributional shifts. Distributional shifts can harm the performance of predictive models when deployed to future customer orders with new specifications. The literature provides limited advice on how such distributional shifts can be addressed in operations management. Here, we propose a data-driven approach based on adversarial learning and job shop scheduling, which allows us to account for distributional shifts in manufacturing settings with high degrees of product customization. We empirically validate our proposed approach using real-world data from a job shop production that supplies large metal components to an oil platform construction yard. Across an extensive series of numerical experiments, we find that our adversarial learning approach outperforms common baselines. Overall, this paper shows how production managers can improve their decision-making under distributional shifts.

Chi-Lin Li, Chung-Han Hsieh

This paper introduces a unified framework for adaptive portfolio management, integrating dynamic Black-Litterman (BL) optimization with the general factor model, Elastic Net regression, and mean-variance portfolio optimization, which allows us to generate investors views and mitigate potential estimation errors systematically. Specifically, we propose an innovative dynamic sliding window algorithm to respond to the constantly changing market conditions. This algorithm allows for the flexible window size adjustment based on market volatility, generating robust estimates for factor modeling, time-varying BL estimations, and optimal portfolio weights. Through extensive ten-year empirical studies using the top 100 capitalized assets in the S&P 500 index, accounting for turnover transaction costs, we demonstrate that this combined approach leads to computational advantages and promising trading performances.

Tjeerd de Vries, Alexis Akira Toda

How should financial institutions hedge their balance sheets against interest rate risk when managing long-term assets and liabilities? We address this question by proposing a bond portfolio solution based on ambiguity-averse preferences, which generalizes classical immunization and accommodates arbitrary liability structures, portfolio constraints, and interest rate perturbations. In a further extension, we show that the optimal portfolio can be computed as a simple generalized least squares problem, making the solution both transparent and computationally efficient. The resulting portfolio also reduces leverage by implicitly regularizing the portfolio weights, which enhances out-of-sample performance. Numerical evaluations using both empirical and simulated yield curves support the feasibility and accuracy of our approach relative to existing methods.

Mykola Beshley, Natalia Kryvinska, Halyna Beshley

Energy-efficient Radio Resource Management (RRM) for 5G and beyond networks has become a key research challenge due to increasing Small Cells (SCs) densities and the high Quality of Experience (QoE) requirements of business users. Ensuring QoE and energy efficiency is essential in mobile networks, but these goals are often opposing and rarely addressed simultaneously in existing solutions. In this paper, we propose to include the QoE criterion in the RRM technique for 5G and beyond multi-layer networks, which will allow ordering individual QoE for business users. We developed a new radio resource allocation and optimization method to address changing user QoE requirements and reduce energy consumption in multi-layer 5G networks. The proposed method differs from the known ones in that it considers the QoE requirements of business users and load localization to optimally distribute the service process between Macro Cells (MCs) and SCs. This method uses a Voronoi diagram to energy-efficiently design the 5G Radio Access Network (RAN) by switching SCs to sleep mode when they are not serving active users. As a result, a balance is struck between user QoE requirements and network energy efficiency. Based on simulations, it is proved that the proposed method allowed more efficient use of accessible radio resources by 25&#x0025; and reduced the energy consumption of the 5G RAN by 8.7&#x0025; to provide the ordered QoE for users compared to traditional RRM methods.