Hasil untuk "Management information systems"

Li Yeshen, Dong Peng, Zhu He et al.

With the rapid development of the digital economy, cybersecurity risks have become increasingly severe. According to relevant reports, ransomware has emerged as one of the most destructive threats in cyberspace. Alarmingly, cybercriminals are continuously leveraging advanced artificial intelligence (AI) technologies to develop next-generation ransomware, making these attacks more intelligent, covert, and damaging. Consequently, it is imperative to comprehensively examine the new impact of AI on cybersecurity, deeply reveal the operating principles of AI-assisted ransomware, and build effective defense strategies. At present, there is a lack of systematic and comprehensive literature analyzing the operating principles and impacts of AI-assisted ransomware. To address this gap, firstly, ransomware was categorized. Subsequently, the attack process of ransomware was analyzed. And then, combined with the latest research progress, the operating principles of AI-assisted ransomware were elaborated in depth. Finally, response measures to operating principles ransomware were systematically summarized from five key perspectives: prevention, prediction, detection, identification and mitigation. Additionally, the development trends and potential future research directions of AI-assisted ransomware were analyzed, aiming to provide valuable insights and guidance for practitioners in the field of cybersecurity.

P. Blanc, A. Lassin, P. Piantone et al.

George Petridis, Apostolia Karabatea, Constantinos Bakogiannis et al.

ABSTRACT Healthcare systems across Europe and globally are increasingly challenged by the need to deliver high‐quality, coordinated care for complex patient populations, such as those living with chronic heart failure (CHF). Many national healthcare policies consider the adoption and implementation of patient‐centred and interoperable information communication technologies‐enabled solutions offered in a single digital platform as a key facilitator towards the transition to integrated and coordinated care. Aiming to support CHF patients and to assist their management, in this paper, we present CareCardia, a modular digital solution designed to support the comprehensive management of CHF. CareCardia offers an interoperable ecosystem that connects healthcare professionals, informal caregivers and patients along a unified CHF care pathway spanning across diagnosis, acute care and jointly managed long‐term care. Specifically, CareCardia integrates state‐of‐the‐art, clinical evidence‐based technologies such as a clinical decision support system and an exergaming platform that will follow patients through the CHF journey. This paper outlines the system architecture and core functionalities of CareCardia prototype. We also present early findings from the initial exploration of the tool, discussing its anticipated impact on CHF and its potential to foster patient empowerment across the continuum of care.

Kavita Radhakrishnan, Cheongin Rachel Im, Jada L. Brooks et al.

Abstract Background Hypertension is a major risk factor for cardiovascular (CV) health in Native Americans (NAs), contributing to disparities in mortality, hospitalizations, and complications that include stroke and kidney diseases. However, despite the benefits of lifestyle modifications for CV health, systemic and cultural barriers hinder their adoption. To promote self-care behaviors, interventions must be culturally tailored and sustainable. Digital games (DGs) offer a promising, community-based approach to enhance self-care for hypertension (HTN) in NAs, aligning with traditional NA practices in which games foster skill-building and engagement. This study focuses on the Lumbee NA community, which faces significant HTN-related disparities. Using community-based participatory research, we are developing a culturally tailored, native-sensor-controlled digital game (N-SCDG) to support HTN self-care behaviors. Methods This is a prospective, randomized (1:1) controlled clinical trial with two groups, to evaluate the impact of a culturally tailored N-SCDG on engagement in HTN self-care behaviors and related health outcomes among Lumbee adults at 3 and 6 months. Adults aged ≥ 18 years from the Lumbee tribal community in Robeson County and diagnosed with HTN will be randomized into an N-SCDG intervention group or a sensor-only control group. Both groups will receive a Fitbit activity tracker to monitor physical activity (PA). The N-SCDG group will engage in the game, which incorporates evidence-based HTN education, while the control group will receive the same HTN education in written format. The primary outcome is the mean daily step count, recorded by the activity tracker at 3 and 6 months. Secondary outcomes include systolic blood pressure (SBP), diastolic blood pressure (DBP), BP control, HTN knowledge, self-efficacy, motivation for self-care, quality of life (QoL), and cardiac hospitalization rates. Discussion This evaluation of an N-SCDG to enhance HTN self-care in Lumbee adults will integrate culturally relevant design with evidence-based education and thus address a gap in use of digital health tools for NAs. The findings will provide vital data on the impact of digital health interventions to improve HTN outcomes and advance health equity in underserved NA communities. Trial registration ClinicalTrials.gov NCT05671406. Registered on January 9, 2024.

Shiyuan Zhang, Yanni Ju, Weishan Kong et al.

Accurate traffic flow prediction plays a vital role in intelligent transportation systems, helping traffic management departments maintain stable traffic order, reduce traffic congestion, and improve road safety. Existing prediction methods focus on dynamic modeling of the spatiotemporal dependencies of traffic flow, capturing the periodicity and spatial heterogeneity in traffic data. However, they still suffer from a lack of focus on the important local information in long-term predictions, leading to overly smooth results that fail to effectively capture sudden changes in traffic patterns. To address these limitations, we propose the sLSTM-Attention-Based Multi-Head Dynamic Graph Convolutional Network (sAMDGCN) model. Specifically, we extend sLSTM and introduce temporal trend-aware multi-head attention to jointly capture the complex temporal dependencies. We propose a multi-head dynamic graph convolutional network to capture a wider range of dynamic spatial dependencies. To validate the effectiveness of sAMDGCN, we perform extensive experiments on four real-world traffic flow datasets. Experimental results show that our proposed sAMDGCN model outperforms the advanced baseline methods in long-term traffic flow prediction tasks, demonstrating its superior performance in capturing complex and dynamic traffic patterns.

Darlene Barker, Mukesh Kumar Reddy Tippireddy, Ali Farhan et al.

The deployment of emotion-recognition technologies expands across healthcare education and gaming sectors to improve human–computer interaction. These systems examine facial expressions together with vocal tone and physiological signals, which include pupil size and electroencephalogram (EEG), to detect emotional states and deliver customized responses. The technology provides benefits through accessibility, responsiveness, and adaptability but generates multiple complex ethical issues. The combination of emotional profiling with biased algorithmic interpretations of culturally diverse expressions and affective data collection without meaningful consent presents major ethical concerns. The increased presence of these systems in classrooms, therapy sessions, and personal devices makes the potential for misuse or misinterpretation more critical. The paper integrates findings from literature review and initial emotion-recognition studies to create a conceptual framework that prioritizes data dignity, algorithmic accountability, and user agency and presents a conceptual framework that addresses these risks and includes safeguards for participants’ emotional well-being. The framework introduces structural safeguards which include data minimization, adaptive consent mechanisms, and transparent model logic as a more complete solution than privacy or fairness approaches. The authors present functional recommendations that guide developers to create ethically robust systems that match user principles and regulatory requirements. The development of real-time feedback loops for user awareness should be combined with clear disclosures about data use and participatory design practices. The successful oversight of these systems requires interdisciplinary work between researchers, policymakers, designers, and ethicists. The paper provides practical ethical recommendations for developing affective computing systems that advance the field while maintaining responsible deployment and governance in academic research and industry settings. The findings hold particular importance for high-stakes applications including healthcare, education, and workplace monitoring systems that use emotion-recognition technology.

Silvia Baeva, Ivelina Hinova, Plamen Stanchev

The inverter is the key element that converts the intermittent DC power of the PV array into a quality AC flow to the grid and simultaneously performs functions such as power factor control, reactive services, and grid code compliance. Therefore, the detailed operating profile of the inverter, how the power, dynamics, power quality, and efficiency evolve over time, is critical for both the scientific understanding of the system and the daily operation (O&M). Monitoring only aggregated energy indicators or single KPIs (e.g., PR) is often insufficient: it does not distinguish weather-related variations from technical limitations (clipping, curtailment), does not show dynamic loads (ramp rate), and does not provide confidence in the quality of the injected energy (PF, P–Q behavior). These deficiencies motivate research that simultaneously covers the physical side of the conversion, the operational dynamics, and the climatic reference of the resource. The analysis covers the window of 25 January–15 April 2025 (winter→spring). Due to the pronounced seasonality of the solar resource and temperature regime, all quantitative results and conclusions regarding efficiency, dynamics, clipping, and degradation are valid only for this window; generalizations to other seasons require additional data. In the next stage, we will add ≥12 months of data and perform a comparable seasonal analysis. Full specifications of the measuring equipment (DC/AC current/voltage, clock synchronization, separate high-frequency <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>Q</mi></mrow></semantics></math></inline-formula>-logger) and quantitative uncertainty estimates, including distribution to key indicators (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>η</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>R</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><mi>H</mi><mi>D</mi></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>I</mi></mrow><mrow><mi>D</mi><mi>C</mi></mrow></msub></mrow></semantics></math></inline-formula>), are presented. The PVGIS per-kWp climate reference is anchored to the nameplate DC peak and cross-checked against percentile scaling; <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>a</mi><mo>±</mo><mi>ε</mi></mrow></semantics></math></inline-formula> scale error shifts <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>R</mi></mrow></semantics></math></inline-formula> by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ε</mi></mrow></semantics></math></inline-formula> and changes <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>Δ</mo><mi>E</mi></mrow></semantics></math></inline-formula> proportionally only on hours with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mover accent="true"><mrow><mi>P</mi></mrow><mo>^</mo></mover><mo>></mo><mi>P</mi></mrow></semantics></math></inline-formula>. The capacity for the climate reference (PVGIS per-kWp) is calibrated to the tabulated DC peak power <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>C</mi></mrow><mrow><mi>c</mi><mi>e</mi><mi>r</mi><mi>t</mi></mrow></msub></mrow></semantics></math></inline-formula> and is cross-validated using a percentile scale (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>Q</mi></mrow><mrow><mn>0.99</mn></mrow></msub></mrow></semantics></math></inline-formula>).

Popović Ivana, Radosavljević Katica, Pătărlăgeanu Simona Roxana et al.

One of the biggest problems faced by the world business is the constant struggle to stay competitive in the market. The acquired knowledge and satisfied numerous technical and organizational conditions have resulted in the development of a whole range of modern management techniques whose meaningful application should enable the acquisition and presentation of relevant management models. Namely, in the light of these new circumstances, in addition to strategic and operational management, companies introduce controlling into the basic activities of corporate management. The aim of this article is to understand the importance of the controlling introduction, its role in the business process, its place in the organizational structure of the company, and the potential benefits that controlling brings. This study addresses a research gap in the literature by examining the implementation of financial controlling practices in the energy sector of a non-EU transition economy. While extensive research has been conducted in the Western European contexts, empirical studies focusing on Southeast European countries, particularly Serbia, remain limited. The article contributes to filling this gap by providing survey-based insights into managerial perceptions, controlling functions, and risk management tools in a transitional regulatory and institutional environment. The added value of this study lies in its empirical examination of financial controlling within a transition economy context, offering sector-specific insights from Serbia’s energy industry. Unlike existing studies focusing on developed markets, this article highlights managerial attitudes, applied tools, and risk-related practices in an underexplored regulatory environment. The aim of this work is to determine the influence of controlling in Serbia based on the results of practical research. Testing of statistical hypotheses proved the importance of controlling in the company. The value of this work is that it was observed that traditional management approaches, based on information obtained from conventional systems, are not sufficient and that they need to be upgraded with modern controlling techniques.

Zineb Maasaoui, Mheni Merzouki, Abdella Battou et al.

This paper presents an advanced framework for real-time monitoring and analysis of network traffic and endpoint security in large-scale enterprises by addressing the increasing complexity and frequency of cyber-attacks. Our Network Security Traffic Analysis Platform employs a comprehensive technology stack including the Elastic Stack, ZEEK, Osquery, Kafka, and GeoLocation data. By integrating supervised machine learning models trained on the UNSW-NB15 dataset, we evaluate Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM), with the Random Forest classifier achieving a notable accuracy of 99.32%. Leveraging Artificial Intelligence and Natural Language Processing, we apply the BERT model with a Byte-level Byte-pair tokenizer to enhance network-based attack detection in IoT systems. Experiments on UNSW-NB15, TON-IoT, and Edge-IIoT datasets demonstrate our platform’s superiority over traditional methods in multi-class classification tasks, achieving near-perfect accuracy on the Edge-IIoT dataset. Furthermore, Network Security Traffic Analysis Platform’s ability to produce actionable insights through charts, tables, histograms, and other visualizations underscores its capability in static analysis of traffic data. This dual approach of real-time and static analysis provides a robust foundation for developing scalable, efficient, and automated security solutions, essential for managing the evolving threats in modern networks.

M. Yancey, K. Close

Kumar Nishant Ranjan Sinha, Vijay Kumar, Nirbhay Kumar et al.

Boiling is used for the thermal management of high-energy-density devices and systems. However, sudden thermal runaway at boiling crisis often results in catastrophic failures. Machine learning is a promising tool for in-situ monitoring of boiling-based systems for preemptive control of boiling crisis. A carefully acquired and well-labeled dataset is a primary requirement for utilizing any data-driven learning framework to extract valuable descriptors. Here, we present a comprehensive dataset of boiling acoustics presented in our recent work [1]. We collect the audio files through meticulously controlled near-saturated pool boiling experiments under steady-state conditions. To this end, we connect a high-sensitivity hydrophone to a pre-amplifier and a data acquisition unit for accurate and reliable acquisition of acoustic signals. We organize the audio files into four categories as per the respective boiling regimes: background or natural convection (BKG, 2−5W/cm2), nucleate boiling (NB, 8−140W/cm2), excluding those at higher heat flux values preceding the onset of boiling crisis or the critical heat flux (Pre-CHF, ≈145W/cm2), and transition boiling (TB, uncontrolled). Each audio file label provides explicit information about the heat flux value and the experimental conditions. This dataset, consisting of 2056 files for BKG, 13367 files for NB, 399 files for Pre-CHF, and 460 files for TB, serves as the foundation for training and evaluating a deep learning strategy to predict boiling regimes. The dataset also includes acoustic emission data from transient pool boiling experiments conducted with varying heating strategies, heater surface, and boiling fluid modifications, creating a valuable dataset for developing robust data-driven models to predict boiling regimes. We also provide the associated MATLAB® codes used to process and classify these audio files.

Grace Bolfrey-Arku , Joyce Haleegoah , Stephen Arthur

Vegetables and cereals, besides health benefits, are of significant socio-economic importance in Ghana, because, the whole production process provides employment for both rural and urban dwellers. Unfortunately, the high prevalence of pests (weeds inclusive) and disease complexes, associated with them, inflict significant economic damage on field and storage, if not properly managed. This review purposed to document challenges from pesticide use and suggest perspective recommendations for mitigation. Information was sourced from published journal articles, technical and annual reports (Research Extension Farmer Linkage Committee (RELC), Environmental Protection Agency (EPA, Ghana), and Institutions), the authors’ observations and personal communication with farmers, agricultural extension agents and other experts. The review analysis indicated over 80% of farmers use pesticides, particularly on high-value cash vegetable and cereal crops; and also to alleviate human labour constraints. Challenges such as pesticide resistance, increasing incidence of existing pests and diseases, or the manifestation of new pests and diseases due to climate change or continuous cropping among others were evident, prompting shifts to increased pesticide use for management and also for desired profit. Highlights on concerns for the insatiable quest for pests and disease control by chemical means, consequently increased reports on dangers of continuous pesticide use on human and public health, the environment and the economy. This research revealed that a minimal understanding of the use and application of pesticides contributed to the non-intended effects on health and the environment. Hopefully, the identified gaps and recommendations if properly addressed by policy would significantly enhance quality production systems for global trade and protect local consumers. State the contribution of this study to scholarship.

Piero Chiarelli, Simone Chiarelli

The simulation analogy presented in this work enhances the accessibility of abstract quantum theories, specifically the stochastic hydrodynamic model (SQHM), by relating them to our daily experiences. The SQHM incorporates the influence of fluctuating gravitational background, a form of dark energy, into quantum equations. This model successfully addresses key aspects of objective-collapse theories, including resolving the ‘tails’ problem through the definition of quantum potential length of interaction in addition to the De Broglie length, beyond which coherent Schrödinger quantum behavior and wavefunction tails cannot be maintained. The SQHM emphasizes that an external environment is unnecessary, asserting that the quantum stochastic behavior leading to wavefunction collapse can be an inherent property of physics in a spacetime with fluctuating metrics. Embedded in relativistic quantum mechanics, the theory establishes a coherent link between the uncertainty principle and the constancy of light speed, aligning seamlessly with finite information transmission speed. Within quantum mechanics submitted to fluctuations, the SQHM derives the indeterminacy relation between energy and time, offering insights into measurement processes impossible within a finite time interval in a truly quantum global system. Experimental validation is found in confirming the Lindemann constant for solid lattice melting points and the ⁴He transition from fluid to superfluid states. The SQHM’s self-consistency lies in its ability to describe the dynamics of wavefunction decay (collapse) and the measure process. Additionally, the theory resolves the pre-existing reality problem by showing that large-scale systems naturally decay into decoherent states stable in time. Continuing, the paper demonstrates that the physical dynamics of SQHM can be analogized to a computer simulation employing optimization procedures for realization. This perspective elucidates the concept of time in contemporary reality and enriches our comprehension of free will. The overall framework introduces an irreversible process impacting the manifestation of macroscopic reality at the present time, asserting that the multiverse exists solely in future states, with the past comprising the formed universe after the current moment. Locally uncorrelated projective decays of wavefunction, at the present time, function as a reduction of the multiverse to a single universe. Macroscopic reality, characterized by a foam-like consistency where microscopic domains with quantum properties coexist, offers insights into how our consciousness perceives dynamic reality. It also sheds light on the spontaneous emergence of gravity in discrete quantum spacetime evolution, and the achievement of the classical general relativity limit in quantum loop gravity and causal dynamical triangulation. The simulation analogy highlights a strategy focused on minimizing information processing, facilitating the universal simulation in solving its predetermined problem. From within, reality becomes the manifestation of specific physical laws emerging from the inherent structure of the simulation devised to address its particular issue. In this context, the reality simulation appears to employ an optimization strategy, minimizing information loss and data management in line with the simulation’s intended purpose.

S. Schaltegger, R. Burritt

Fariba Khounraz, Mahmood Khodadoost, Saeid Gholamzadeh et al.

Abstract Background The rapid prevalence of coronavirus disease 2019 (COVID‐19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID‐19 patients using data mining techniques. Methods In this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other. Results Based on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models. Conclusion Data mining methods have the potential to be used for predicting outcomes of COVID‐19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID‐19 patients.

Alexander Benlian, William J. Kettinger, A. Sunyaev et al.

ALEXANDER BENLIAN (benlian@ise.tu-darmstadt.de; corresponding author) is a Professor of Management Information Systems (MIS) at Technische Universität Darmstadt, Germany, where he serves as Dean of the Department of Business, Economics, and Law. His former academic position was Ludwig-Maximilians-University of Munich, where he received a Ph.D. He has also served as a senior consultant with McKinsey & Company. Dr. Benlian’s research interests include the transformative value of cloud computing, online platforms, digital transformation, and digital business models, with over 150 academic publications in these areas. His work has appeared in Journal of Management Information Systems, Journal of the AIS, Journal of Strategic Information Systems, MIS Quarterly Executive, and others. He is Associate Editor of the European Journal of Information Systems and International Journal of Electronic Commerce and serves the Editorial Review Board of the Journal of Service Research.

Raúl Trujillo-Cabezas, J. Verdegay

The regional information program specializes in spatial development, economics, land and housing economics, sustainable planning, information management and marketing in the agriculture/food business. research and education in spatial economics, regional and development finance, spatial development and planning, urban and rural information, spatial analysis, Management Information Systems (MIS) of agricultural and food industries, e-Business, Information management and marketing in the food business. Students in the regional information program can study understand, analyze, and propose theories for urban and regional structures in terms of the regional and spatial economy and information systems. Students also learn to use diverse statistical and econometric tools such as Geographic Information Systems (GIS) and Management Information Systems (MIS) for analyzing regional/agricultural information and food business.

J. Rockart, Lauren S. Flannery

J. V. Bemmel, M. Musen

Soheil Habibian, Benjamin B. Wheatley, Suehye Bae et al.

Abstract Although robots are increasingly found in a wide range of applications, their use in proximity to humans is still fraught with challenges, primarily due to safety concerns. Roboticists have been seeking to address this situation in recent years through the use of soft robots. Unfortunately, identifying appropriate models for the complete analysis and investigation of soft robots for design and control purposes can be problematic. This paper seeks to address this challenge by proposing two complementary modeling techniques for a particular type of soft robotic actuator known as a Fiber-Reinforced Elastomeric Enclosure (FREE). We propose that researchers can leverage multiple models to fill gaps in the understanding of the behavior of soft robots. We present and evaluate both a dynamic, lumped-parameter model and a finite element model to extend understanding of the practicability of FREEs in soft robotic applications. The results of experimental simulations using a lumped-parameter model show that at low pressures FREE winding angle and radius change no more than $$2\%$$ 2 % . This observation provided confidence that a linearized, dynamic, lumped-mass model could be successfully used for FREE controller development. Results with the lumped-parameter model demonstrate that it predicts the actual rotational motion of a FREE with at most $$4\%$$ 4 % error when a closed-loop controller is embedded in the system. Additionally, finite element analysis was used to study FREE design parameters as well as the workspace achieved with a module comprised of multiple FREEs. Our finite element results indicate that variations in the material properties of the elastic enclosure of a FREE are more significant than variations in fiber properties (primarily because the fibers are essentially inextensible in comparison to the elastic enclosure). Our finite element analysis confirms the results obtained by previous researchers for the impact of variations in winding angle on FREE rotation, and we extend these results to include an analysis of the effect of winding angle on FREE force and moment generation. Finally, finite element results show that a $$30^{\circ }$$ 30 ∘ difference in winding angle dramatically alters the shape of the workspace generated by four FREEs assembled into a module. Concludingly, comments are made about the relative advantages and limitations of lumped-parameter and finite element models of FREEs and FREE modules in providing useful insights into their behavior.