Hasil untuk "Management information systems"

Cristiana Tudor, Robert Sova

Abstract One of the most notable developments in the asset management industry in recent decades has been the growth of algorithmic trading. At the same time, significant structural changes in the industry have occurred, with passive investing gaining momentum. The intersection of these two major trends poses special challenges during market downturns, magnifying portfolio losses and leading to significant outflows. Emerging market (EM) investors have seen two major downturn events in the 2020s, namely the COVID-19 pandemic and the Russia-Ukraine conflict, both of which have strongly affected EM portfolios’ risk-return profiles and increased their correlations with their developed market counterparts, eliminating much or all of EMs’ diversification benefits. This has led to major capital outflows from EM countries, further destabilizing these fragile economies. Against this backdrop, we argue that capital need not exit these riskier markets during periods of turmoil and support this by developing a second-generation Automated Adaptive Trading System (AATS) back-tested on a relevant, diversified EM portfolio that tracks the Morgan Stanley Capital International (MSCI) Emerging Markets Index during a volatile period characterized by negative returns, high risk, and a high correlation with global markets for the buy-and-hold EM portfolio. The system incorporates an Autoregressive Moving Average-Generalized AutoRegressive Conditional Heteroskedasticity model that offers an interpretability advantage over machine-learning methods. The main strength of the AATS is its ability to allow the embedded hybrid forecasting model to adapt to the changing environments that characterize EMs. This is done by implementing a recursive window technique and running a user-specified fitness function to dynamically optimize the mean equation parameters throughout the lead time. Back-testing several configurations of the flexible AATS consistently reveals its superiority while assuring the robustness of the results. We conclude that with the right investment tools, EMs continue to offer compelling opportunities that should not be overlooked. The novel AATS proposed in this study is such a tool, providing active EM investors with substantial value-added through its ability to generate abnormal returns, and can help to enhance the resilience of EMs by mitigating the cost of crises for those countries.

Qingqing Li, Tianshu Wu, Tingfa Xu et al.

Abstract Pavement crack detection is widely spread over road maintenance, ensuring the longevity and safety of infrastructure. Traditional manual inspection methods are time-consuming, labor-intensive, and prone to errors. In response, automated crack detection systems based on deep learning have emerged, offering more efficient and accurate solutions. However, existing models often face challenges such as large model sizes, slow inference speeds, and limited applicability in real-time applications. In this paper, we propose a novel light-weight Crack Regional Segmentation method based on YOLOv11, which introduces attention mechanisms to address challenges in pavement images, such as varying crack sizes, occlusion, and irregular surface textures. By embedding a region-based attention mechanism into the YOLOv11 network, the method enhances the model’s ability to focus on crack features. Specifically, the model network layers are progressively pruned to reduce the number of parameters and floating-point operations, thereby further improving operational efficiency and refining detection in the target regions. Furthermore, to tackle issues with blurred or indistinct crack boundaries, we present a fuzzy information-guided YOLOv11-based model, FIG-YOLO. This model integrates fuzzy logic and fuzzy membership functions to handle uncertainty in crack detection. The fuzzy membership functions are used to quantify the degree of crack features, allowing the model to better distinguish between crack and non-crack regions, especially in cases where crack boundaries are unclear. This approach significantly improves the accuracy of crack detection and segmentation. Extensive experiments demonstrate that our approach effectively addresses challenges such as complex backgrounds and blurred crack edges in pavement images. This research not only provides a novel solution for the automated detection of pavement cracks but also offers insights into the development of intelligent road maintenance systems. With the expansion of large-scale datasets and the advancement of deep learning models, pavement crack detection algorithms are expected to further enhance their accuracy and efficiency, offering significant support for road infrastructure management.

Noor ul Misbah Khanum, Hayssam Dahrouj, Ramesh C. Bansal et al.

Microgrids have emerged as a pivotal solution in the quest for a sustainable and energy-efficient future. While microgrids offer numerous advantages, they are also prone to issues related to reliably forecasting renewable energy demand and production, protecting against cyberattacks, controlling operational costs, optimizing power flow, and regulating the performance of energy management systems (EMS). Tackling these energy management challenges is essential to facilitate microgrid applications and seamlessly incorporate renewable energy resources. Artificial intelligence (AI) has recently demonstrated immense potential for optimizing energy management in microgrids, providing efficient and reliable solutions. This paper highlights the combined benefits of enabling AI-based methodologies in the energy management systems of microgrids by examining the applicability and efficiency of AI-based EMS in achieving specific technical and economic objectives. The paper also points out several future research directions that promise to spearhead AI-driven EMS, namely the development of self-healing microgrids, integration with blockchain technology, use of Internet of things (IoT), and addressing interpretability, data privacy, scalability, and the prospects to generative AI in the context of future AI-based EMS.

Qasim Khadim, Peter Manzl, Emil Kurvinen et al.

The precision, stability, and performance of lightweight high-strength steel structures in heavy machinery is affected by their highly nonlinear dynamics. This, in turn, makes control more difficult, simulation more computationally intensive, and achieving real-time autonomy, using standard approaches, impossible. Machine learning through data-driven, physics-informed and physics-inspired networks, however, promises more computationally efficient and accurate solutions to nonlinear dynamic problems. This study proposes a novel framework that has been developed to estimate real-time structural deflection in hydraulically actuated three-dimensional systems. It is based on SLIDE, a machine-learning-based method to estimate dynamic responses of mechanical systems subjected to forced excitations.~Further, an algorithm is introduced for the data acquisition from a hydraulically actuated system using randomized initial configurations and hydraulic pressures.~The new framework was tested on a hydraulically actuated flexible boom with various sensor combinations and lifting various payloads. The neural network was successfully trained in less time using standard parameters from PyTorch, ADAM optimizer, the various sensor inputs, and minimal output data. The SLIDE-trained neural network accelerated deflection estimation solutions by a factor of $10^7$ in reference to flexible multibody simulation batches and provided reasonable accuracy. These results support the studies goal of providing robust, real-time solutions for control, robotic manipulators, structural health monitoring, and automation problems.

Nathanael Sirili, Furahini Yoram, Veronica Mkusa et al.

Background With the rise of non-communicable diseases in Tanzania, palliative care (PC) is increasingly needed to improve the quality of life for these patients through pain and symptom management and providing psychological care, social and spiritual support. Despite a larger portion of the population having access to healthcare services at primary healthcare (PHC) facilities in Tanzania, PC services are limited and less organised at this level. This study explored the challenges facing the provision of PC and the opportunities for strengthening PC services at PHC facilities in Tanzania.Methods We adopted an exploratory qualitative case study to conduct in-depth interviews with 15 health facilities in charge from 15 purposefully selected PHC facilities in Dar es Salaam City, Tanzania, in August 2023. We analysed the gathered information using qualitative content analysis.Results Two categories emerged from the analysis of the gathered information. These are (1) challenges facing the provision of PC services at PHC facilities and (2) opportunities for strengthening PC services at PHC facilities. The challenges are grouped as provider-level, facility-level and patient-level challenges. The opportunities are organised into three subcategories. These are the increasing demand for PC services, the availability of multiple supporting systems and a functional referral system.Conclusion This study underscores the challenges and opportunities for providing PC services at PHC facilities. These findings call for a collaborative effort from health system players to strengthen the available PC services. The efforts should include expanding the coverage of PC services at the PHC facilities and healthcare providers’ training. Expansion of PC services should include introducing them in places where they are unavailable and improving them where they are not available. PC training should consider preservice training in the health training institutions’ curricula and continued medical education to the existing staff. Furthermore, we recommend community health education to raise awareness of PC services.

Kurashkin Sergei, Kravtsov Kirill, Kukartsev Anatoly et al.

This article presents the development of a resource management system for environmentally sustainable agriculture. The main purpose of the system is to optimize the use of water, fertilizers and energy in agriculture, which significantly reduces the negative impact on the environment and increases production efficiency. The system includes modules for soil monitoring, data collection on resources and climatic conditions, as well as analytical components for processing and analysing the information received. An important element is the user interface, which allows farmers and agronomists to receive recommendations on resource allocation based on up-to-date data and forecasts. The work highlights the current challenges in the development of such systems, including the complexity of technology integration and the need for affordable solutions for various types of farms. The article also discusses the prospects for further research aimed at improving the accuracy of forecasts, expanding monitoring functions and adapting the system for use in small farms. The proposed system makes a significant contribution to the development of environmentally oriented agriculture, providing a tool for sustainable resource management and contributing to the achievement of environmental protection goals.

Karel Pavelka, Martin Landa

This study explores how combining virtual reality (VR) and augmented reality (AR) with geographic information systems (GIS) revolutionizes data visualization. It traces the historical development of these technologies and highlights key milestones that paved the way for this study’s objectives. While existing platforms like Esri’s software and Google Earth VR show promise, they lack complete integration for immersive GIS visualization. This gap has led to the need for a dedicated workflow to integrate selected GIS data into a game engine for visualization purposes. This study primarily utilizes QGIS for data preparation and Unreal Engine for immersive visualization. QGIS handles data management, while Unreal Engine offers advanced rendering and interactivity for immersive experiences. To tackle the challenge of handling extensive GIS datasets, this study proposes a workflow involving tiling, digital elevation model generation, and transforming GeoTIFF data into 3D objects. Leveraging QGIS and Three.js streamlines the conversion process for integration into Unreal Engine. The resultant virtual reality application features distinct stations, enabling users to navigate, visualize, compare, and animate GIS data effectively. Each station caters to specific functionalities, ensuring a seamless and informative experience within the VR environment. This study also delves into augmented reality applications, adapting methodologies to address hardware limitations for smoother user experiences. By optimizing textures and implementing augmented reality functionalities through modules Swift, RealityKit, and ARKit, this study extends the immersive GIS experience to iOS devices. In conclusion, this research demonstrates the potential of integrating virtual reality, augmented reality, and GIS, pushing data visualization into new realms. The innovative workflows and applications developed serve as a testament to the evolving landscape of spatial data interpretation and engagement.

Jungryeol Park, Yituo Feng, Seon-Phil Jeong

Abstract In recent years, the turnover phenomenon of new college graduates has been intensifying. The turnover of new employees creates many difficulties for businesses as it is difficult to recover the costs spent on their hiring and training. Therefore, it is necessary to promptly identify and effectively manage new employees who are inclined to change jobs. So far previous studies related to turnover intention have contributed to understanding the turnover phenomenon of new employees by identifying factors influencing turnover intention. However, with these factors, there is a limitation that it has not been able to present how much it is possible to predict employees who are actually willing to change jobs. Therefore, this study proposes a method of developing a machine learning-based turnover intention prediction model to overcome the limitations of previous studies. In this study, data from the Korea Employment Information Service's Job Movement Path Survey for college graduates were used, and OLS regression analysis was performed to confirm the influence of predictors. And model learning and classification were performed using a logistic regression (LR), k-nearest neighbor (KNN), and extreme gradient boosting (XGB) classifier. A novel finding of this research is the diminished or reversed influence of certain traditional factors, such as workload importance and the relevance of one's major field, on turnover intention. Instead, job security emerged as the most significant predictor. The model's accuracy rates, highest with XGB at 78.5%, demonstrate the efficacy of applying machine learning in turnover intention prediction, marking a significant advancement over traditional econometric models. This study breaks new ground by integrating advanced predictive analytics into turnover intention research, offering a more nuanced understanding of the factors influencing the turnover intentions of new college graduates. The insights gained could guide organizations in effectively managing and retaining new talent, highlighting the need for a focus on job security and organizational satisfaction, and the shifting relevance of traditional factors like job preference.

Ruri Suko Basuki, Jehad A.H Hammad

The main objective of this research is to determine the optimal threshold value in the unknown region in the alpha-matting operation of natural images. Alpha-mating serves to draw matte from the image used in segmentation. The alpha value is very influential on the quality of segmentation which is determined by the level of threshold value accuracy. The determination of the threshold begins by breaking the grayscale image into several sub-images using Region of Interest (RoI). Each sub-image was extracted using the Gray Level Co-occurrence Matrix (GLCM) considered by the parameters of contrast, energy, and entropy at angles of 0°, 45°, 90°, and 135 °. Each feature results in extractions, which are then averaged and normalized in each sub-image. The value is determined as the local threshold value used in the alpha matting operation. Experiments were carried out on 12 natural images from the image-mating dataset to evaluate the performance of the proposed algorithm. The increase in accuracy shows up to 63% by the measurements of experiments, compared to the calculation of adaptive threshold by using the fuzzy CMs Algorithm.

Ioannis Anestis, Elias Pipinis, Stefanos Kostas et al.

This study was designed to bridge extant research gaps regarding the vulnerable and protected local endemic <i>Campanula pelviformis</i>, a wild edible green traditionally consumed in Crete (Greece) with agro-alimentary and medicinal interest as well as ornamental value. The <i>C. pelviformis</i> ecological profile was generated using the climate and temperature conditions prevailing in its wild habitats through mapping of natural distribution linked with online bioclimatic databases in geographical information systems. We tested the germination of seeds from five wild-growing populations at four different temperatures (10, 15, 20 and 25 °C) and under different light conditions (light/dark and darkness), and we performed fertilization trails [integrated nutrient management (INF), chemical fertilization (ChFe), control] examining morphological and physiological characteristics, above- and below-ground macro- and micronutrients and phenol contents, as well as their antioxidant capacity. We found population and temperature effects on seed germination with their interaction being statistically significant. <i>Campanula pelviformis</i> germinated better at 10 and 15 °C (>85% for all populations) with no preference on light conditions (98.75% and 95% in light and dark conditions). The INF application increased root dry mass, chlorophyll content index and chlorophyll fluorescence compared to other treatments and was beneficial for macro- and micronutrient concentrations in above-ground parts compared to previously studied wild-growing material, while below-ground parts were positively impacted by both fertilization types. Total phenols and antioxidant capacity were both increased by ChFe fertilization. The data furnished herein permitted the re-evaluation and upgrade of its sustainable exploitation potential in different economic sectors.

Tossaporn Udomsap, Sakda Chinchouryrang, Siwat Liampipat et al.

Abstract In this paper, a 3-PRS (prismatic, revolute, and spherical) parallel manipulator for platform stabilization is designed. The main purpose of this device is to stabilize visual equipment, which is placed on top of a car to inspect electrical transmission cables, as part of routine maintenance. Due to the bulky and heavy infrared cameras used during inspections, a stabilizer platform has been designed to handle the weight of camera equipment up to 10 kg. This device consists of two major mechanisms. The first mechanism is able to adjust the angle of the camera. Thus, the user can focus the camera along the electric transmission lines. The second mechanism is stabilization. The mechanism serves to stabilize the orientation and position of the camera in the roll, pitch, and heave directions. To test the performance of the stabilization mechanism, the device is fed with the known value of the angle with regard to the input. As such, the device is trying to compensate for the change in angle. The results show that the errors between the input angles and compensated angles are in the range of 0.4–3%. Errors are seen to be within an acceptable range. It is significant that the resultant errors do not affect the orientation of the camera.

David Kröger, Milijana Teodosic, Christian Rehtanz

The large-scale integration of flexible heating systems in the European electricity market leads to a substantial increase of transportation requirements and consecutively grid congestions in the continental transmission grid. Novel model formulations for the grid-aware operation of both individual small-scale heat pumps and large-scale power-to-heat (PtH) units located in district heating networks are presented. The functionality of the models and the contribution of flexible heating systems for transmission grid congestion management is evaluated by running simulations for the target year 2035 for the German transmission grid. The findings show a decrease in annual conventional redispatch volumes and renewable energy sources (RES) curtailment resulting in cost savings of approximately 6 % through the integration of flexible heating systems in the grid congestion management scheme. The analysis suggests that especially large-scale PtH units in combination with thermal energy storages can contribute significantly to the alleviation of grid congestion and foster RES integration.

Sinem Cece, İlker Köse

Introduction: Health service quality refers to all efforts to prevent a negative outcome in the health status of individuals. For this reason, measuring and evaluating the quality of health services is important to increase the quality of services provided. Aim: In this study, Joint Commission International's (JCI) accepted indicator-based health service quality measurement model and the Healthcare Information and Management Systems Society's (HIMSS)-Electronic Medical Record Adoption Model (EMRAM) are discussed. Method: This research used the bag-of-words model (BoW), a text mining method. Result: As a result of the analysis, the similarity of keywords (as unigrams) used in all of the guides was found to be approximately 33%, the bigram similarity was 6% and the trigram similarity was 3%. Conclusion: The fact that the similarity between the two models is not higher can be explained by the fact that, unlike JCI, the HIMSS EMRAM model handles the quality of health services with a digitalization axis. Text mining opens up new research areas as a method for comparing quality standards with new and interesting results.

Baihan Lin

Knowledge management systems (KMS) are in high demand for industrial researchers, chemical or research enterprises, or evidence-based decision making. However, existing systems have limitations in categorizing and organizing paper insights or relationships. Traditional databases are usually disjoint with logging systems, which limit its utility in generating concise, collated overviews. In this work, we briefly survey existing approaches of this problem space and propose a unified framework that utilizes relational databases to log hierarchical information to facilitate the research and writing process, or generate useful knowledge from references or insights from connected concepts. Our framework of bidirectional knowledge management system (BKMS) enables novel functionalities encompassing improved hierarchical note-taking, AI-assisted brainstorming, and multi-directional relationships. Potential applications include managing inventories and changes for manufacture or research enterprises, or generating analytic reports with evidence-based decision making.

Dr. Jean Michel Merkes, Dr. Srinivas Banala

Abstract Cold‐chain management is of high importance in preserving perishable products and in retaining quality. A visible marker on packages indicating complete maintenance of the cold chain assures safe consumption of products by end‐users and assists in reducing waste. Time‐temperature indicators (TTIs) are integrated markers that provide information about exposure of packages to adverse temperature and have been gaining increased attention by consumers. Here we present a methylene‐blue‐based derivative, N,N,N′,N′‐tetramethyl‐N10‐Boc‐phenothiazine‐3,7‐diamine (BocPTDA), that can be used as a solid‐state organic TTI dye, exhibiting an irreversible change from colorless to blue green upon heating. The conversion properties, studied using a silicagel‐coated plate, confirmed that BocPTDA undergoes a color change above 20 °C. At temperatures of 4 °C and below, no visible changes are exhibited, making BocPTDA a well‐suited marker for monitoring abrupt temperature deviations indicating improper cold‐chain management. Thus, application of BocPTDA‐based TTI systems on packages could inform consumers about the cold‐chain maintenance, assuring quality and safe consumption.

Brian D. Titus, Kevin Brown, Heljä-Sisko Helmisaari et al.

Abstract Forest biomass harvesting guidelines help ensure the ecological sustainability of forest residue harvesting for bioenergy and bioproducts, and hence contribute to social license for a growing bioeconomy. Guidelines, typically voluntary, provide a means to achieve outcomes often required by legislation, and must address needs related to local or regional context, jurisdictional compatibility with regulations, issues of temporal and spatial scale, and incorporation of appropriate scientific information. Given this complexity, comprehensive reviews of existing guidelines can aid in development of new guidelines or revision of existing ones. We reviewed 32 guidelines covering 43 jurisdictions in the USA, Canada, Europe and East Asia to expand upon information evaluated and recommendations provided in previous guideline reviews, and compiled a searchable spreadsheet of direct quotations from documents as a foundation for our review. Guidelines were considered in the context of sustainable forest management (SFM), focusing on guideline scope and objectives, environmental sustainability concerns (soils, site productivity, biodiversity, water and carbon) and social concerns (visual aesthetics, recreation, and preservation of cultural, historical and archaeological sites). We discuss the role of guidelines within the context of other governance mechanisms such as SFM policies, trade regulations and non-state market-driven (NSMD) standards, including certification systems. The review provides a comprehensive resource for those developing guidelines, or defining sustainability standards for market access or compliance with public regulations, and/or concerned about the sustainability of forest biomass harvesting. We recommend that those developing or updating guidelines consider (i) the importance of well-defined and understood terminology, consistent where possible with guidelines in other jurisdictions or regions; (ii) guidance based on locally relevant research, and periodically updated to incorporate current knowledge and operational experience; (iii) use of indicators of sensitive soils, sites, and stands which are relevant to ecological processes and can be applied operationally; and (iv) incorporation of climate impacts, long-term soil carbon storage, and general carbon balance considerations when defining sustainable forest biomass availability. Successful implementation of guidelines depends both on the relevance of the information and on the process used to develop and communicate it; hence, appropriate stakeholders should be involved early in guideline development.

Serhat Peker, Gonca Gokce Menekse Dalveren, Yavuz İnal

The aim of this paper is to examine the influence of the content elements of online banner ads on customers’ visual attention, and to evaluate the impacts of gender, discount rate and brand familiarity on this issue. An eye-tracking study with 34 participants (18 male and 16 female) was conducted, in which the participants were presented with eight types of online banner ads comprising three content elements—namely brand, discount rate and image—while their eye movements were recorded. The results showed that the image was the most attractive area among the three main content elements. Furthermore, the middle areas of the banners were noticed first, and areas located on the left side were mostly noticed earlier than those on the right side. The results also indicated that the discount areas of banners with higher discount rates were more attractive and eye-catching compared to those of banners with lower discount rates. In addition to these, the participants who were familiar with the brand mostly concentrated on the discount area, while those who were unfamiliar with the brand mostly paid attention to the image area. The findings from this study will assist marketers in creating more effective and efficient online banner ads that appeal to customers, ultimately fostering positive attitudes towards the advertisement.

Tanushree Roy, Sara Sattarzadeh, Satadru Dey

Cyber-physical-social connectivity is a key element in Intelligent Transportation Systems (ITSs) due to the ever-increasing interaction between human users and technological systems. Such connectivity translates the ITSs into dynamical systems of socio-technical nature. Exploiting this socio-technical feature to our advantage, we propose a cyber-attack detection scheme for ITSs that focuses on cyber-attacks on freeway traffic infrastructure. The proposed scheme combines two parallel macroscopic traffic model-based Partial Differential Equation (PDE) filters whose output residuals are compared to make decision on attack occurrences. One of the filters utilizes physical (vehicle/infrastructure) sensor data as feedback whereas the other utilizes social data from human users' mobile devices as feedback. The Social Data-based Filter is aided by a fake data isolator and a social signal processor that translates the social information into usable feedback signals. Mathematical convergence properties are analyzed for the filters using Lyapunov's stability theory. Lastly, we validate our proposed scheme by presenting simulation results.

Katherine Davis

Power systems are large scale cyber-physical critical infrastructure that form the basis of modern society. The reliability and resilience of the grid is dependent on the correct functioning of related subsystems, including computing, communications, and control. The integration is widespread and has a profound impact on the operation, reliability, and efficiency of the grid. Technologies comprising these infrastructure can expose new sources of threats. Mapping these threats to their grid resilience impacts to stop them early requires a timely and detailed view of the entire cyber-physical system. Grid resilience must therefore be seen and addressed as a cyber-physical systems problem. This short position paper presents several key preliminaries, supported with evidence from experience, to enable cyber-physical situational awareness and intrusion response through a cyber-physical energy management system.

Shashank Yadav

Policymakers face a broader challenge of how to view AI capabilities today and where does society stand in terms of those capabilities. This paper surveys AI capabilities and tackles this very issue, exploring it in context of political security in digitally networked societies. We extend the ideas of Information Management to better understand contemporary AI systems as part of a larger and more complex information system. Comprehensively reviewing AI capabilities and contemporary man-machine interactions, we undertake conceptual development to suggest that better information management could allow states to more optimally offset the risks of AI enabled influence and better utilise the emerging capabilities which these systems have to offer to policymakers and political institutions across the world. Hopefully this long essay will actuate further debates and discussions over these ideas, and prove to be a useful contribution towards governing the future of AI.