Hasil untuk "Management information systems"

Keerththanan Vickneswaran, Mariangel Garcia Andarcia, Hugo Retief et al.

Sustainable water resource management in transboundary river basins is challenged by fragmented data, limited real-time access, and the complexity of integrating diverse information sources. This paper presents WaterCopilot-an AI-driven virtual assistant developed through collaboration between the International Water Management Institute (IWMI) and Microsoft Research for the Limpopo River Basin (LRB) to bridge these gaps through a unified, interactive platform. Built on Retrieval-Augmented Generation (RAG) and tool-calling architectures, WaterCopilot integrates static policy documents and real-time hydrological data via two custom plugins: the iwmi-doc-plugin, which enables semantic search over indexed documents using Azure AI Search, and the iwmi-api-plugin, which queries live databases to deliver dynamic insights such as environmental-flow alerts, rainfall trends, reservoir levels, water accounting, and irrigation data. The system features guided multilingual interactions (English, Portuguese, French), transparent source referencing, automated calculations, and visualization capabilities. Evaluated using the RAGAS framework, WaterCopilot achieves an overall score of 0.8043, with high answer relevancy (0.8571) and context precision (0.8009). Key innovations include automated threshold-based alerts, integration with the LRB Digital Twin, and a scalable deployment pipeline hosted on AWS. While limitations in processing non-English technical documents and API latency remain, WaterCopilot establishes a replicable AI-augmented framework for enhancing water governance in data-scarce, transboundary contexts. The study demonstrates the potential of this AI assistant to support informed, timely decision-making and strengthen water security in complex river basins.

Peter Amorese, Morteza Lahijanian

Nonlinear ordinary differential equations (ODEs) are powerful tools for modeling real-world dynamical systems. However, propagating initial state uncertainty through nonlinear dynamics, especially when the ODE is unknown and learned from data, remains a major challenge. This paper introduces a novel continuum dynamics perspective for model learning that enables formal uncertainty propagation by constructing Taylor series approximations of probabilistic events. We establish sufficient conditions for the soundness of the approach and prove its asymptotic convergence. Empirical results demonstrate the framework's effectiveness, particularly when predicting rare events.

JI Mingyi, WANG Zining, XIAO Shengjie et al.

A robust secure beamforming (BF) algorithm based on imperfect channel state information of eavesdroppers was proposed to improve the secrecy performance for satellite-terrestrial integrated network (STIN), in which satellite network organically coexisted with the terrestrial network. Firstly, for the scenario in which satellite and terrestrial networks shared the spectral resources and adopted BF to serve Earth stations (ESs) and terrestrial users, respectively, a joint optimization problem was formulated to maximize the achievable secrecy rate threshold of the ES, whereas satisfying the quality of service (QoS) requirements, the secrecy outage probability and the transmit power budgets. To tackle this mathematically intractable problem, the zero-forcing criterion was employed to reduce complexity, and then an iterative algorithm based on the Bernstein inequality and the Dinkelbach method was proposed to obtain satisfactory solutions. Finally, computer simulation results confirm that the proposed algorithm achieves superior performance with a fast convergence rate, and can strike a favorable balance between secrecy performance and computational complexity.

Nikolay Gueorguiev, Atanas Nachev, Yavor Boychev et al.

The reliability of the electro-power equipment of electrical power transmission systems is essential in ensuring an uninterrupted power supply with the necessary voltage and frequency stability. This is especially important when performing lengthy procedures requiring the serviceability of the electrical equipment used, such as those related to foundries and metallurgical processes, or with the processes of testing complex means for the remote control of electromagnetic radiation within the implementation of the Sustainable development of the Competence Center “Quantum Communication, Intelligent Security Systems and Risk Management” (QUASAR) Project, funded with the participation of the EU under the “Research, Innovation and Digitalization for Smart Transformation” Program 2021.2027 according to procedure BG16RFPR002-1.014. One of the main issues in this case is related to the availability of information regarding the technical condition of the deployed or reserve energy resources. In this connection, this study proposes methods for determining the quantity of operational equipment that is either in use or in storage, based on the reliability testing of a representative sample of it.

Chelsey H. Hill, Jorge E. Fresneda, Murugan Anandarajan

Abstract The “wisdom of the crowd” (WoC) refers to the notion that collective human knowledge is capable of outperforming even individual expert knowledge. This study investigates the application of this phenomenon to lexicon-based sentiment analysis of text data. Lexicons are frequently used to classify the sentiment of text data, particularly in the absence of sentiment class label information. We propose leveraging some of the most popular, publicly-available lexicons created in the last half century to improve sentiment analysis performance. Specifically, this research argues that the collective information provided by the thirteen lexicons included in the crowd constitutes a WoC situation that can more accurately predict the sentiment in the majority of example cases when compared to individual lexicons, lexicon ensembles, and machine learning methods. Thirteen popular sentiment-labeled text datasets, comprised of different types of text data and covering a variety of domains, are used to test this research proposition. We show that the WoC sentiment analysis achieves greater performance than individual lexicons, which are considered to be ‘experts’, and a lexicon ensemble approach. In comparing our novel approach to sentiment analysis against popular machine learning approaches, the proposed WoC method achieves superior results in the majority of examples. By overcoming many of the limitations of other approaches with high accuracy, the WoC method can provide organizations with real-time, reliable, and accurate sentiment analysis.

Ivan Kudrenko, Almas Mukhametov, Emin Shahin Aslanov

Abstract Global economic and environmental challenges in the development of contemporary society underscore the importance of addressing issues related to the structural modification of supply chains within the context of a circular economy. The goal of the study was to identify structural modifications in supply chains in a circular economy, as well as to assess the effectiveness of adapting them to new management approaches through the introduction of information technologies. To achieve this, the research employs a range of statistical methods, including Six Sigma, Pareto analysis, Theory of Constraints, and regression analysis, to identify and address weaknesses within the supply chain. Mathematical computations are utilized to evaluate the effectiveness and necessity of implemented technologies within the supply chain links. The findings of the study demonstrated that the adaptation of supply chains to the conditions of a circular economy significantly reduces operational errors and improves service quality. Specifically, the implementation of blockchain technologies substantially enhances process transparency, increases trust among supply chain participants, and reduces data management costs. However, the transition to circular models encounters several significant challenges, not only of an economic nature but also of social and legal dimensions. The scientific significance of the research lies in the systematization of data on the main directions of supply chain evolution within the framework of a circular economy and the development of a methodology for evaluating the effectiveness of supply chain adaptation to new economic and technological prospects for the development of economic systems. The proposed approaches will enable companies to manage business processes more efficiently, optimize resource management systems, and enhance their resilience to external environmental challenges. Addressing these tasks is a key factor in the successful adaptation of enterprises to dynamic changes in the global economy. The practical application of the results will allow companies not only to reduce operational costs but also to improve the quality of products and services, thereby enhancing their competitiveness in the global market.

Amir Farakhor, Iman Askari, Di Wu et al.

Optimal power management of battery energy storage systems (BESS) is crucial for their safe and efficient operation. Numerical optimization techniques are frequently utilized to solve the optimal power management problems. However, these techniques often fall short of delivering real-time solutions for large-scale BESS due to their computational complexity. To address this issue, this paper proposes a computationally efficient approach. We introduce a new set of decision variables called power-sharing ratios corresponding to each cell, indicating their allocated power share from the output power demand. We then formulate an optimal power management problem to minimize the system-wide power losses while ensuring compliance with safety, balancing, and power supply-demand match constraints. To efficiently solve this problem, a parameterized control policy is designed and leveraged to transform the optimal power management problem into a parameter estimation problem. We then implement the ensemble Kalman inversion to estimate the optimal parameter set. The proposed approach significantly reduces computational requirements due to 1) the much lower dimensionality of the decision parameters and 2) the estimation treatment of the optimal power management problem. Finally, we conduct extensive simulations to validate the effectiveness of the proposed approach. The results show promise in accuracy and computation time compared with explored numerical optimization techniques.

Wan Yu, Habib Hamam

Financial management assumes a pivotal role as a fundamental information system contributing to enterprise development. Nonetheless, prevalent methodologies frequently encounter challenges in proficiently overseeing diverse information streams inherent to financial management. This study introduces an innovative paradigm for enterprise financial management centered on the transformation of user information signals. In its initial phases, the methodology augments the Transformer network and self-attention mechanism to extract features pertaining to both users and financial data, fostering a more cohesive integration of financial and user information. Subsequently, a reinforcement learning-based alignment method is implemented to reconcile disparities between financial and user information, thereby enhancing semantic alignment. Ultimately, a signal conversion technique employing generative adversarial networks is deployed to harness user information, elevating financial management efficacy and, consequently, optimizing overall financial operations. The empirical validation of this approach, achieving an impressive mAP score of 81.9%, not only outperforms existing methodologies but also underscores the tangible impact and enhanced execution prowess that this paradigm brings to financial management systems. As such, this work not only contributes to the state of the art but also holds promise for revolutionizing the landscape of enterprise financial management.

Ahmad MohdAziz Hussein, Abdulrauf Garba Sharifai, Osama Moh’d Alia et al.

Abstract The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.

Eunju Yi, Do-Hyung Park

Deciding on a career and securing employment at an ideal company represent significant challenges for students. Employment is not only a personal achievement but also a measure of success for universities and governments. To transform students into competitive applicants, various activities are provided by universities, governments, and companies. These activities may leave students either excited about the prospects or overwhelmed by the experience. The aim of this study is to explore the relationship between college experiences and post-graduation employment through an analysis of a five-year activity log. Specifically, students' diverse activities were categorized into six core competencies: skill reinforcement, leadership and teamwork, globalization, organizational commitment, job exploration, and autonomous implementation. We used logistic regression to examine how these competencies relate to employment status, and ANOVA analysis to assess their impact on initial salaries. The findings reveal that while competencies in skill improvement, job exploration, and organizational commitment were not statistically significant, those in leadership and teamwork, globalization, and autonomous implementation were crucial for securing employment. Additionally, globalization, job exploration, and autonomous implementation competencies influenced annual salary levels. Furthermore, a comparison of students completing either a single major or a convergent major revealed that job exploration competency significantly impacts the annual salary level.

Belachew Desalegn, Belachew Desalegn, Bimrew Tamrat et al.

Recent research generally reports that the intermittent characteristics of sustainable energy sources pose great challenges to the efficiency and cost competitiveness of sustainable energy harvesting technologies. Hence, modern sustainable energy systems need to implement a stringent power management strategy to achieve the maximum possible green electricity production while reducing costs. Due to the above-mentioned characteristics of sustainable energy sources, power management systems have become increasingly sophisticated nowadays. For addressing the analysis, scheduling, and control problems of future sustainable power systems, conventional model-based methods are completely inefficient as they fail to handle irregular electric power disturbances in renewable energy generations. Consequently, with the advent of smart grids in recent years, power system operators have come to rely on smart metering and advanced sensing devices for collecting more extensive data. This, in turn, facilitates the application of advanced machine learning algorithms, which can ultimately cause the generation of useful information by learning from massive data without assumptions and simplifications in handling the most irregular operating behaviors of the power systems. This paper aims to explore various application objectives of some machine learning algorithms that primarily apply to wind energy conversion systems (WECSs). In addition, an enhanced proportional integral (PI) (2DoF) algorithm is particularly introduced and implemented in a doubly fed induction generator (DFIG)-based WECS to enhance the reliability of power production. The main contribution of this article is to leverage the superior qualities of the PI (2DoF) algorithm for enhanced performance, stability, and robustness of the WECS under uncertainties. Finally, the effectiveness of the study is demonstrated by developing a virtual reality in a MATLAB-Simulink environment.

Yuchao Liao, Tosiron Adegbija, Roman Lysecky

Embedded systems continue to rapidly proliferate in diverse fields, including medical devices, autonomous vehicles, and more generally, the Internet of Things (IoT). Many embedded systems require application-specific hardware components to meet precise timing requirements within limited resource (area and energy) constraints. High-level synthesis (HLS) is an increasingly popular approach for improving the productivity of designing hardware and reducing the time/cost by using high-level languages to specify computational functionality and automatically generate hardware implementations. However, current HLS methods provide limited or no support to incorporate or utilize precise timing specifications within the synthesis and optimization process. In this paper, we present a hybrid high-level synthesis (H-HLS) framework that integrates state-based high-level synthesis (SB-HLS) with performance-driven high-level synthesis (PD-HLS) methods to enable the design and optimization of application-specific embedded systems in which timing information is explicitly and precisely defined in state-based system models. We demonstrate the results achieved by this H-HLS approach using case studies including a wearable pregnancy monitoring device, an ECG-based biometric authentication system, and a synthetic system, and compare the design space exploration results using two PD-HLS tools to show how H-HLS can provide low energy and area under timing constraints.

Yoonjae Noh, Jong-Min Kim, Soongoo Hong et al.

The stock index is actively used for the realization of profits using derivatives and via the hedging of assets; hence, the prediction of the index is important for market participants. As market uncertainty has increased during the COVID-19 pandemic and with the rapid development of data engineering, a situation has arisen wherein extensive amounts of information must be processed at finer time intervals. Addressing the prevalent issues of difficulty in handling multivariate high-frequency time-series data owing to multicollinearity, resource problems in computing hardware, and the gradient vanishing problem due to the layer stacking in recurrent neural network (RNN) series, a novel algorithm is developed in this study. For financial market index prediction with these highly complex data, the algorithm combines ResNet and a variable-wise attention mechanism. To verify the superior performance of the proposed model, RNN, long short-term memory, and ResNet18 models were designed and compared with and without the attention mechanism. As per the results, the proposed model demonstrated a suitable synergistic effect with the time-series data and excellent classification performance, in addition to overcoming the data structure constraints that the other models exhibit. Having successfully presented multivariate high-frequency time-series data analysis, this study enables effective investment decision making based on the market signals.

Benildes Maculan, Maristela Sanches Lima Mesquita

A pesquisadora Dahlberg trouxe diversas contribuições para o campo da organização do conhecimento, sobretudo com o desenvolvimento da Teoria Analítica do Conceito e o estabelecimento de um conjunto de categorias que agrupam e estruturam os conceitos representativos de um dado domínio. Neste estudo, destacamos o conceito de comunidade discursiva para a análise de domínio e apresentamos as bases e princípios teóricos da Teoria Analítica do Conceito e das categorias de Dahlberg para a construção de sistemas de organização do conhecimento do tipo Mapa Conceitual. Como produto da aplicação desses fundamentos, elaboramos um mapa para o recorte temático do conhecimento homeopático, em especial, da representação do uso das altas diluições nas formulações aplicadas à agricultura.  A metodologia se caracteriza como exploratória, descritiva e aplicada, com abordagem qualitativa, empregando uma proposta metodológica de construção de mapa conceitual a partir de propostas de Gonçalves (2010), Moraes (2014) e Moresi et al. (2019) e apoia-se em procedimentos terminográficos. Como resultado, espera-se fornecer subsídios para a modelização de domínios na forma de mapas conceituais, fortalecendo o uso desse artefato na elucidação de conceitos e suas relações em diversos contextos.

Yassine Hamdi, Deniz Gündüz

In image compression, with recent advances in generative modeling, the existence of a trade-off between the rate and the perceptual quality has been brought to light, where the perception is measured by the closeness of the output distribution to the source. This leads to the question: how does a perception constraint impact the trade-off between the rate and traditional distortion constraints, typically quantified by a single-letter distortion measure? We consider the compression of a memoryless source $X$ in the presence of memoryless side information $Z,$ studied by Wyner and Ziv, but elucidate the impact of a perfect realism constraint, which requires the output distribution to match the source distribution. We consider two cases: when $Z$ is available only at the decoder or at both the encoder and the decoder. The rate-distortion trade-off with perfect realism is characterized for sources on general alphabets when infinite common randomness is available between the encoder and the decoder. We show that, similarly to traditional source coding with side information, the two cases are equivalent when $X$ and $Z$ are jointly Gaussian under the squared error distortion measure. We also provide a general inner bound in the case of limited common randomness.

Federica Gaglione, David Ania Ayiine Etigo

Starting from the relationship between urban planning and mobility management, TeMA has gradually expanded the view of the covered topics, always following a rigorous scientific in-depth analysis. This section of the Journal, Review Notes, is a continuous update about emerging topics concerning relationships among urban planning, mobility, and environment, thanks to a collection of short scientific papers written by young researchers. The Review Notes are made up of five parts. Each section examines a specific aspect of the broader information storage within the main interests of the TeMA Journal. In particular: the Town Planning International Rules and Legislation. Section aims at presenting the latest updates in the territorial and urban legislative sphere. The theme of energy and its related energy consumption is a leading theme in the European scientific debate for the continuous pursuit of urban development. In this direction, the contribution of this review notes illustrates on the one hand optimization models and decision support tools produced so far to improve the energy organization at different urban scales and on the other highlights within the cards, strategies and actions carried out forward from the European Union to have a cognitive and operational framework on energy planning and on how to accelerate the sustainability of urban systems.

Hosea Opedes, Sander Mücher, Jantiene E. M. Baartman et al.

Analyzing the dominant forms and extent of land cover changes in the Mount Elgon region is important for tracking conservation efforts and sustainable land management. Mount Elgon’s rugged terrain limits the monitoring of these changes over large areas. This study used multitemporal satellite imagery to analyze and quantify the land cover changes in the upper Manafwa watershed of Mount Elgon, for 42 years covering an area of 320 km². The study employed remote sensing techniques, geographic information systems, and software to map land cover changes over four decades (1978, 1988, 2001, 2010, and 2020). The maximum likelihood classifier and post-classification comparison technique were used in land cover classification and change detection analysis. The results showed a positive percentage change (gain) in planted forest (3966%), built-up (890%), agriculture (186%), and tropical high forest low-stocked (119%) and a negative percentage change (loss) in shrubs (−81%), bushland (−68%), tropical high forest well-stocked (−50%), grassland (−44%), and bare and sparsely vegetated surfaces (−14%) in the period of 1978–2020. The observed changes were concentrated mainly at the peripheries of the Mount Elgon National Park. The increase in population and rising demand for agricultural land were major driving factors. However, regreening as a restoration effort has led to an increase in land area for planted forests, attributed to an improvement in conservation-related activities jointly implemented by the concerned stakeholders and native communities. These findings revealed the spatial and temporal land cover changes in the upper Manafwa watershed. The results could enhance restoration and conservation efforts when coupled with studies on associated drivers of these changes and the use of very-high-resolution remote sensing on areas where encroachment is visible in the park.

Effrosyni Giama

Buildings are responsible for approximately 30–40% of energy consumption in Europe, and this is a fact. Along with this fact is also evident the existence of a defined and strict legislation framework regarding energy efficiency, decarbonization, sustainability, and renewable energy systems in building applications. Moreover, information and communication technologies, along with smart metering for efficient monitoring, has come to cooperate with a building’s systems (smart buildings) to aim for more advanced and efficient energy management. Furthermore, the well-being in buildings still remains a crucial issue, especially nowadays that health and air quality are top priority goals for occupants. Taking all the above into consideration, this paper aims to analyze ventilation technologies in relation to energy consumption and environmental impact assessment using the life cycle approach. Based on the review analysis of the existing ventilation technologies, the emphasis is given to parameters related to the efficient technical design of ventilation systems and their adequate maintenance under the defined guidelines and standards of mechanical ventilation operation. These criteria can be the answer to the complicated issue of energy efficiency along with indoor air quality targets. The ventilation systems are presented in cooperation with heating and cooling system operations and renewable energy system applications ensuring an energy upgrade and reduced greenhouse gas emissions. Finally, the mechanical ventilation is examined in a non-residential building in Greece. The system is compared with the conventional construction typology of the building and in cooperation with PVs installation in terms of the environmental impact assessment and energy efficiency. The methodology implemented for the environmental evaluation is the Life Cycle Analysis supported by OpenLca software.

Mathieu Servillat, François Bonnarel, Catherine Boisson et al.

We present here a provenance management system adapted to astronomical projects needs. We collected use cases from various astronomy projects and defined a data model in the ecosystem developed by the IVOA (International Virtual Observatory Alliance). From those use cases, we observed that some projects already have data collections generated and archived, from which the provenance has to be extracted (provenance "on top"), and some projects are building complex pipelines that automatically capture provenance information during the data processing (capture "inside"). Different tools and prototypes have been developed and tested to capture, store, access and visualize the provenance information, which participate to the shaping of a full provenance management system able to handle detailed provenance information.

Wei Liu, Zhilu Lai, Kiran Bacsa et al.

In this paper, we propose a probabilistic physics-guided framework, termed Physics-guided Deep Markov Model (PgDMM). The framework targets the inference of the characteristics and latent structure of nonlinear dynamical systems from measurement data, where exact inference of latent variables is typically intractable. A recently surfaced option pertains to leveraging variational inference to perform approximate inference. In such a scheme, transition and emission functions of the system are parameterized via feed-forward neural networks (deep generative models). However, due to the generalized and highly versatile formulation of neural network functions, the learned latent space often lacks physical interpretation and structured representation. To address this, we bridge physics-based state space models with Deep Markov Models, thus delivering a hybrid modeling framework for unsupervised learning and identification of nonlinear dynamical systems. The proposed framework takes advantage of the expressive power of deep learning, while retaining the driving physics of the dynamical system by imposing physics-driven restrictions on the side of the latent space. We demonstrate the benefits of such a fusion in terms of achieving improved performance on illustrative simulation examples and experimental case studies of nonlinear systems. Our results indicate that the physics-based models involved in the employed transition and emission functions essentially enforce a more structured and physically interpretable latent space, which is essential for enhancing and generalizing the predictive capabilities of deep learning-based models.