Hasil untuk "Management information systems"

Petar Nikolov, Guenka Petrova

Objective: Mobile health applications (mHealth apps) can support effective asthma self-management interventions, improving patients’ quality of life while reducing costs for healthcare systems. This article aims to identify trends in app features relevant to the future development of reliable mHealth asthma support applications that can meet the challenges of the new century. Methods: The search of the PubMed database was performed during August–September 2025. The database was searched electronically using the following keywords: digital AND asthma AND apps. Thirty review articles and international studies were included. The studies encompassed the period from 2015 to September 2025. Only articles available in the English language were included. Results: All selected apps were compared across categories such as availability for unrestricted use, functionality and design, ease of use, and information management and medical accuracy. Features preferred by asthma and allergy patients were also reviewed, including guidance for emergency situations, asthma action plans, and notifications from clinics. Despite the wide variety of medical apps available, only a limited number have been tested in clinical environments, and few have been translated into languages other than English. Conclusion: mHealth apps have considerable potential for asthma self-management not only in adults but also in children, and they can improve quality of life and symptom control compared with conventional treatment methods. App design, maintenance, and update practices vary widely across apps and platforms. This article will assist in identifying apps that are most suitable for specific user groups and may help guide the future development of robust and easy-to-use mHealth apps for asthma management.

Jessada Sresakoolchai, Chayutpong Manakul, Ni-Asri Cheputeh

Railway tight and wide gauges are critical factors affecting the safety and reliability of railway systems. Undetected tight and wide gauges can lead to derailments, posing significant risks to operations and passenger safety. This study explores a novel approach to detecting railway tight and wide gauges by integrating accelerometer data, machine-learning techniques, and building information modeling (BIM). Accelerometers installed on axle boxes provide real-time dynamic data, capturing anomalies indicative of tight and wide gauges. These data are processed and analyzed using supervised machine-learning algorithms to classify and predict potential tight- and wide-gauge events. The integration with BIM offers a spatial and temporal framework, enhancing the visualization and contextualization of detected issues. BIM’s capabilities allow for the precise mapping of tight- and wide-gauge locations, streamlining maintenance workflows and resource allocation. Results demonstrate high accuracy in detecting and predicting tight and wide gauges, emphasizing the reliability of machine-learning models when coupled with accelerometer data. This research contributes to railway maintenance practices by providing an automated, data-driven methodology that enhances the proactive identification of tight and wide gauges, reducing the risk of derailments and maintenance costs. Additionally, the integration of machine learning and BIM highlights the potential for comprehensive digital solutions in railway asset management.

Yuanzhu Wang, Rajah Rasiah

The Pearl River Basin, one of southern China’s most vital water ecosystems, has experienced significant changes in ecosystem service values (ESV) due to rapid urbanization, posing challenges to water resource management and governance. Despite the importance of water-related ecosystem services (WES) in the region, comprehensive assessments of their spatiotemporal dynamics and drivers remain scarce. This study addresses this gap by employing Geographic Information Systems (GIS) and a human disturbance index to evaluate the spatiotemporal evolution of ESV in the basin from 2000 to 2020, with a focus on water resources. Using the ecosystem service value equivalent approach, adjusted for biomass factors, we assess the impacts of land use/cover changes—particularly in water bodies, forests, and croplands—on ESV. The findings reveal an overall decline in ESV by approximately 15 %, with water-related ecosystem services showing marked sensitivity to human activity, especially in urbanizing areas, where declines of up to 30 % were observed in some regions. Geographic detector analysis highlights that population density, GDP, vegetation cover, and human activity collectively drive changes in water-linked ESV. These findings underscore the need for sustainable water resource management strategies that balance ecological stability with socio-economic development, offering critical guidance for integrated water-ecology-society governance in the Pearl River Basin.

SangHyun Lee, Ali Danandeh Mehr, Daniel Moriasi et al.

Abstract High‐resolution gridded data sets provide valuable opportunities to enhance drought forecasting, but applying complex machine learning algorithms across large spatial domains is computationally challenging. This study presents a novel hybrid approach for forecasting the gridded Standardized Precipitation‐Evapotranspiration Index (SPEI) across the U.S. Southern Plains (SP), with lead times of 1 and 3 months. We developed a clustering‐based method using 21 centroid grid cells, each representing a unique cluster of similar grid cells based on various hydrologic characteristics, to train and evaluate multilayer perceptrons (MLPs), long short‐term memory (LSTM), and genetic programming (GP). Based on the superior performance of the trained MLPs in terms of Nash‐Sutcliffe efficiency and root‐mean‐square error, they were extended to corresponding grid cells for each cluster, enabling spatially adaptive drought prediction at a high resolution. The use of discrete wavelet transform (DWT) further enhanced model accuracy by capturing key temporal patterns in the SPEI series. Notably, our results showed that physical and hydrologic attributes strongly influenced input selections. While a 12‐month lag period worked well in regions with weaker seasonality, areas with strong seasonality benefited from selection of effective lags by using mutual information. For 3‐month‐ahead forecasts, including decomposed potential evapotranspiration in addition to precipitation as inputs improved accuracy in drier regions but decreased accuracy in humid areas. The forecast maps based on the hybrid DWT‐MLP models effectively captured the spatial variability of drought, with high correlations to observed values, demonstrating their effectiveness for regional drought early warning systems to inform water resources management adaptations.

Adrien Banse, Giannis Delimpaltadakis, Luca Laurenti et al.

With the increasing ubiquity of safety-critical autonomous systems operating in uncertain environments, there is a need for mathematical methods for formal verification of stochastic models. Towards formally verifying properties of stochastic systems, methods based on discrete, finite Markov approximations -- abstractions -- thereof have surged in recent years. These are found in contexts where: either a) one only has partial, discrete observations of the underlying continuous stochastic process, or b) the original system is too complex to analyze, so one partitions the continuous state-space of the original system to construct a handleable, finite-state model thereof. In both cases, the abstraction is an approximation of the discrete stochastic process that arises precisely from the discretization of the underlying continuous process. The fact that the abstraction is Markov and the discrete process is not (even though the original one is) leads to approximation errors. Towards accounting for non-Markovianity, we introduce memory-dependent abstractions for stochastic systems, capturing dynamics with memory effects. Our contribution is twofold. First, we provide a formalism for memory-dependent abstractions based on transfer operators. Second, we quantify the approximation error by upper bounding the total variation distance between the true continuous state distribution and its discrete approximation.

Arkadiusz Ciach

Research objectives and hypothesis/research questions The aim is to critically analyze the challenges and inequalities in the management of the financing of the tasks of local government units (LGUs) in Poland, with particular emphasis on the impact of legislative, political, and financial factors on the effectiveness of their tasks. Research questions: 1. Does the presence of councilors employed in units subordinate to local government units lead to a conflict of interest, which negatively impacts the transparency and independence of financial decisions made? 2. Does the amount of subsidies and subsidies awarded depend solely on the economic situation of municipalities, or is it also influenced by political links between local authorities and the ruling party at the central level? 3. As a result of underestimating the educational subsidy, are local government units forced to redirect their funds to finance educational tasks at the expense of other public activity areas? 4. Do the currently used algorithms for the distribution of subsidies reflect the real needs of local government units, and, as a result, there is an optimal allocation of public funds? 5. Is there equal access for local government units to European and national funds? Research methods 1. Analysis of empirical data: Examination of data from local government units (LGUs) between 2019 and 2023. 2. Comparative analysis: Evaluation of financial indicators for LGUs based on their size, own revenues, and political affiliations. 3. Statistical analysis: Investigation of differences in the allocation of financial resources to identify disparities. 4. Analysis of source documents: Review legal documents, Supreme Audit Office (NIK) reports, and local budget data from LGUs. 5. Case study: Analysis of municipalities in the Radomsko focusing on underestimating educational subsidies and conflicts of interest. 6. Critical literature review: Examination of domestic and international literature to provide context and identify relevant issues. Main results 1. The amount of subsidies and grants awarded often depends on the political affiliations of local authorities with the ruling party. 2. Educational subsidies fall short of covering actual educational costs, straining resources for other public responsibilities. 3. Councilors employed by subordinate LGU units cause conflicts of interest, harming transparency and financial independence. 4. Under governmental support programs, grant allocation processes lacked transparency and clear criteria, enabling abuses and discretionary fund distribution. 5. Financial support was unevenly distributed, worsening inequalities between wealthier and poorer regions. Implications for theory and practice For theory: the research brought a new perspective to the analysis of decentralization and self-government, showing the impact of political, legislative, and financial factors on the functioning of local governments. In particular, the results confirm the importance of political distribution theory, pointing to the practice of favoring individuals associated with the ruling party, reflecting the phenomenon of political allocation of resources. The problems of unequal allocation of resources and underestimation of education subsidies bring new elements to the theory of distributive justice, highlighting the imbalance in access to public resources between regions. For practice: research indicates an urgent need for legislative reforms aimed at simplifying and stabilizing the regulations governing the activities of local government units. Recommendation for the introduction of more transparent mechanisms for allocating public funds. Emphasize the importance of support for less developed local government units, which would reduce regional inequalities and make more efficient use of available funds.

L. V. Narkevich, P. V. Tereliansky

The article presents the developed information and analytical platform for working capital management according to the methodology of a systematic approach, considering the specifics of textile production and modern requirements of preventive crisis management. The project is presented, and an effective algorithm for preventive crisis management of working capital is adapted on the basis of an information and analytical environment using progressive analysis techniques. The author’s approach is presented to the regulation of the analytical information formation in the designated block of anti-crisis management, integrated into the analytical environment of the enterprise through the interrelations and interdependencies of quantitative and qualitative parameters of the use efficiency of working capital. The anti-crisis management project of organization working capital on the basis of a systematic approach allowed to identify the disproportions of individual parameters of the efficiency of working capital management, typical financial problems associated with a high level of operational, including production and financial cycles, which together poses a real threat to the sustainable development of the enterprise (innovation, investment, market and financial stability of development). In addition, due to this project, it became possible to develop a set of interrelated measures to improve the efficiency of working capital management in the format of optimization tasks in the stocks’ regulation of raw materials, finished products, accounts receivable. The results of the analytical study indicate the need to use innovative approaches to managing the turnover of working capital in the production and financial cycles with access to high-margin design solutions for the sustainable development of the enterprise.

Mohammad Dolatabadi, Alireza Zakariazadeh, Alberto Borghetti et al.

Encouraging citizens to invest in small-scale renewable resources is crucial for transitioning towards a sustainable and clean energy system. Local energy communities (LECs) are expected to play a vital role in this context. However, energy scheduling in LECs presents various challenges, including the preservation of customer privacy, adherence to distribution network constraints, and the management of computational burdens. This paper introduces a novel approach for energy scheduling in renewable-based LECs using a decentralized optimization method. The proposed approach uses the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method, significantly reducing the computational effort required for solving the mixed integer programming (MIP) problem. It incorporates network constraints, evaluates energy losses, and enables community participants to provide ancillary services like a regulation reserve to the grid utility. To assess its robustness and efficiency, the proposed approach is tested on an 84-bus radial distribution network. Results indicate that the proposed distributed approach not only matches the accuracy of the corresponding centralized model but also exhibits scalability and preserves participant privacy.

Dhani Ariatmanto, Anggi Muhammad Rifai

The pervasive issue of fake news spreading rapidly on online platforms. causing a concerning dissemination of misinformation. The influence of fake news has become a pressing social problem, shaping public opinion in important events such as elections. This research focuses on detecting and classifying fake news using the Random Forest algorithm by investigating the impact of feature extraction techniques on classification accuracy, this study specifically employs the TF-IDF method. For this purpose, we used 44,898 English-language articles from the ISOT fake news dataset. The dataset is cleaned using tokenization and stemming then split into 75% training and 25% testing. The TF-IDF vectorizer technique was applied to convert text into numeric as feature extraction. This study has implemented a Random Forest classifier to predict real and fake news. The proposed model contributes to overall classification precision by comparing it to the existing models. This fake news detection highlights the efficacy of the TF-IDF vectorizer and Random Forest combination which achieved an impressive accuracy rate of 99.0%. This contribution highlights an effective strategy for combating misinformation through precise text classification.

Leif Azzopardi, Vishwa Vinay

This paper introduces the concept of accessibility from the field of transportation planning and adopts it within the context of Information Retrieval (IR). An analogy is drawn between the fields, which motivates the development of document accessibility measures for IR systems. Considering the accessibility of documents within a collection given an IR System provides a different perspective on the analysis and evaluation of such systems which could be used to inform the design, tuning and management of current and future IR systems.

Mostafaali Ayubirad, Hamid R. Ossareh

The prediction-based nonlinear reference governor (PRG) is an add-on algorithm to enforce constraints on pre-stabilized nonlinear systems by modifying, whenever necessary, the reference signal. The implementation of PRG carries a heavy computational burden, as it may require multiple numerical simulations of the plant model at each sample time. To this end, this paper proposes an alternative approach based on machine learning, where we first use a regression neural network (NN) to approximate the input-output map of the PRG from a set of training data. During the real-time operation, at each sample time, we use the trained NN to compute a nominal reference command, which may not be constraint admissible due to training errors and limited data. We adopt a novel sensitivity-based approach to minimally adjust the nominal reference while ensuring constraint enforcement. We thus refer to the resulting control strategy as the modified neural network reference governor (MNN-RG), which is significantly more computationally efficient than the PRG. The computational and theoretical properties of MNN-RG are presented. Finally, the effectiveness and limitations of the proposed method are studied by applying it as a load governor for constraint management in automotive fuel cell systems through simulation-based case studies.

Ghais Kharmanda, Jun Shao, Hussam Al Sakkaf et al.

Ikhwan Arief, Muhammad Farhandika, Ahmad Syafruddin Indrapriyatna et al.

In this research, we use sentiment analysis to refine the user interface (UI) and user experience (UX) of the Bukalapak application, a leading online trading platform in Indonesia. We focus our scrutiny on 4,462 reviews related to the UI within a larger dataset of 246,947. Almost a third of these critiques express dissatisfaction, predominantly pointing out issues related to the UI design and its functionality. The critiques underscore the potential of sentiment analysis as a tool to uncover areas of user-centric design that need improvement. To address these issues, it is necessary to incorporate user feedback and sentiment analysis into the design workflow, allowing a more in-depth understanding of user needs and facilitating more effective service enhancements. Embracing a user-centered methodology allows for UI fine-tuning, leading to better functionality and increased user contentment. Our investigation reveals a positive link between design refinements and usability ratings, indicating improved user experience satisfaction. To summarize, this research highlights the essential contribution of user feedback and sentiment analysis to detect and correct UI shortfalls, thus augmenting UX and contributing to the triumph of platforms like Bukalapak within Indonesia's dynamically changing e-commerce environment.

Jieao Zhu, Zhongzhichao Wan, Linglong Dai et al.

Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of EM channel characteristics, degrees-of-freedom, and system capacity. However, these works do not clarify how to integrate EIT knowledge into the design and optimization of wireless systems. To fill in this gap, in this paper, we propose an EIT-based statistical channel model with simplified parameterization. Thanks to the simplified closed-form expression of the EMCF, it can be readily applied to various channel modeling and inference tasks. Specifically, by averaging the solutions of Maxwell's equations over a tunable von Mises distribution, we obtain a spatio-temporal correlation function (STCF) model of the EM channel, which we name as the EMCF. Furthermore, by tuning the parameters of the EMCF, we propose an EIT-based covariance estimator (EIT-Cov) to accurately capture the channel covariance. Since classical MMSE estimators can exploit prior information contained in the channel covariance matrix, we further propose the EIT-MMSE channel estimator by substituting EMCF for the covariance matrix. Simulation results show that both the proposed EIT-Cov covariance estimator and the EIT-MMSE channel estimator outperform their baseline algorithms, thus proving that EIT is beneficial to wireless communication systems.

Paul Alexander Bilokon

In academic literature portfolio risk management and hedging are often versed in the language of stochastic control and Hamilton--Jacobi--Bellman~(HJB) equations in continuous time. In practice the continuous-time framework of stochastic control may be undesirable for various business reasons. In this work we present a straightforward approach for thinking of cross-asset portfolio risk management and hedging, providing some implementation details, while rarely venturing outside the convex optimisation setting of (approximate) quadratic programming~(QP). We pay particular attention to the correspondence between the economic concepts and their mathematical representations; the abstractions enabling us to handle multiple asset classes and risk models at once; the dimensional analysis of the resulting equations; and the assumptions inherent in our derivations. We demonstrate how to solve the resulting QPs with CVXOPT.

Michael D. Ekstrand, Ben Carterette, Fernando Diaz

Current practice for evaluating recommender systems typically focuses on point estimates of user-oriented effectiveness metrics or business metrics, sometimes combined with additional metrics for considerations such as diversity and novelty. In this paper, we argue for the need for researchers and practitioners to attend more closely to various distributions that arise from a recommender system (or other information access system) and the sources of uncertainty that lead to these distributions. One immediate implication of our argument is that both researchers and practitioners must report and examine more thoroughly the distribution of utility between and within different stakeholder groups. However, distributions of various forms arise in many more aspects of the recommender systems experimental process, and distributional thinking has substantial ramifications for how we design, evaluate, and present recommender systems evaluation and research results. Leveraging and emphasizing distributions in the evaluation of recommender systems is a necessary step to ensure that the systems provide appropriate and equitably-distributed benefit to the people they affect.

Zhenhan Huang, Fumihide Tanaka

Financial portfolio management (PM) is one of the most applicable problems in reinforcement learning (RL) owing to its sequential decision-making nature. However, existing RL-based approaches rarely focus on scalability or reusability to adapt to the ever-changing markets. These approaches are rigid and unscalable to accommodate the varying number of assets of portfolios and increasing need for heterogeneous data input. Also, RL agents in the existing systems are ad-hoc trained and hardly reusable for different portfolios. To confront the above problems, a modular design is desired for the systems to be compatible with reusable asset-dedicated agents. In this paper, we propose a multi-agent RL-based system for PM (MSPM). MSPM involves two types of asynchronously-updated modules: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). An EAM is an information-generating module with a Deep Q-network (DQN) agent, and it receives heterogeneous data and generates signal-comprised information for a particular asset. An SAM is a decision-making module with a Proximal Policy Optimization (PPO) agent for portfolio optimization, and it connects to multiple EAMs to reallocate the corresponding assets in a financial portfolio. Once been trained, EAMs can be connected to any SAM at will, like assembling LEGO blocks. With its modularized architecture, the multi-step condensation of volatile market information, and the reusable design of EAM, MSPM simultaneously addresses the two challenges in RL-based PM: scalability and reusability. Experiments on 8-year U.S. stock market data prove the effectiveness of MSPM in profit accumulation by its outperformance over five different baselines in terms of accumulated rate of return (ARR), daily rate of return (DRR), and Sortino ratio (SR). MSPM improves ARR by at least 186.5% compared to constant rebalanced portfolio (CRP), a widely-used PM strategy. To validate the indispensability of EAM, we back-test and compare MSPMs on four different portfolios. EAM-enabled MSPMs improve ARR by at least 1341.8% compared to EAM-disabled MSPMs.

Dengya Zhu, Shastri L Nimmagadda, Kok Wai Wong et al.

Relevance judgment of human assessors is inherently subjective and dynamic when evaluation datasets are created for Information Retrieval (IR) systems. However, a small group of experts' relevance judgment results are usually taken as ground truth to "objectively" evaluate the performance of the IR systems. Recent trends intend to employ a group of judges, such as outsourcing, to alleviate the potentially biased judgment results stemmed from using only a single expert's judgment. Nevertheless, different judges may have different opinions and may not agree with each other, and the inconsistency in human relevance judgment may affect the IR system evaluation results. In this research, we introduce a Relevance Judgment Convergence Degree (RJCD) to measure the quality of queries in the evaluation datasets. Experimental results reveal a strong correlation coefficient between the proposed RJCD score and the performance differences between the two IR systems.

Simen A. Steindal RN, PhD, professor, Mari O. Ohnstad RN, MNSc, Ørjan Flygt Landfald MSc et al.

Introduction Educational institutions worldwide have implemented learning management systems (LMSs) to centralise and manage learning resources, educational services, learning activities and institutional information. LMS has mainly been used by teachers as storage and transfer of course material. To effectively utilise digital technologies in education, there is a need for more knowledge of student experiences with digital technology, such as LMSs and especially regarding how LMSs can contribute to student engagement and learning. Objective This study aimed to gain knowledge about postgraduate nursing students’ experiences with the use of LMS in a subject in an advanced practice nursing master's programme. Methods A qualitative method with a descriptive design was employed. Two focus group interviews were performed with eight postgraduate nursing students from an advanced practice nursing programme at a university college in Norway. Data were analysed using qualitative content analysis. Results Three themes emerged from the data material: 1) A course structure that supports learning; 2) LMS tools facilitate preparation, repetition and flexibility; and 3) own responsibility for using the LMS for preparation before on-campus activities. Conclusion The course structure within the LMS seemed to be important to enhance postgraduate students’ ability to prepare before on-campus activities. Implementation and use of LMS tools can facilitate preparation, repetition and flexibility, especially when postgraduate students study difficult topics. Postgraduate students seem to have different views regarding their own responsibility for using the LMS to prepare before on-campus activities.

Ghina Dwi Salsabila, Erwin Budi Setiawan

Personality provides a deep insight of someone and has an important part in someone’s job performance. Predicting personality through social media has been studied on several research. The problem is how to improve the performance of personality prediction system. The purpose of this research is to predict personality on Twitter users and increase the performance of the personality prediction system. An online survey using Big Five Inventory (BFI) questionnaire has been distributed and gathered 295 Twitter users with 511,617 tweets data. In this research, we experiment on two different methods using Support Vector Machine (SVM), and the combination of SVM and BERT as the semantic approach. This research also implements Linguistic Inquiry Word Count (LIWC) as the linguistic feature for personality prediction system. The results showed that combination of these two methods achieve 79.35% accuracy score and with the implementation of LIWC can improve the accuracy score up to 80.07%. Overall, these results showed that the combination of SVM and BERT as the semantic approach with the implementation of LIWC is recommended to gain a better performance for the personality prediction system.