Hasil untuk "Management information systems"

Asaph Azaria, A. Ekblaw, Thiago Vieira et al.

Dana Van Aken, Andrew Pavlo, Geoffrey J. Gordon et al.

R. Elmasri, S. Navathe

Dave Ludwick, J. Doucette

K. McCloghrie, M. Rose

R. Haux

Abena Ofosu, Thai Thai, Birhanu Birhanu

According to the United Nations (n.d.), climate change is the long-term shift in temperatures and weather patterns due to natural changes, such as the sun’s activity and significant volcanic eruptions, or human activities, such as burning fossil fuels like coal, oil, and gas. The effects of and challenges caused by climate change on farmers’ ability to manage mixed farming systems in sub-Saharan Africa are well documented in the literature. How­ever, the synergies among mixed farming systems’ components and farmers’ innovation demands and responses to climate change impacts remain frag­mented. Using a case of mixed crop-livestock-tree (MCLT) systems in northern Ghana, this paper examined farmers’ responses, their innovation needs, and how these innovations can be catalyzed to enable more farmers to adopt similar climate change adaptations. Our findings show that climate change impacts mixed farming systems in several domains, with these impacts being more visible in some domains. Significant productivity declines are observed in crops, livestock, and the whole mixed farming system. Productivity declines lead to decreased incomes, food availability, and house­hold food security. Female farmers’ access to pro­duction factors, resource management, and market participation is reduced. Farmers make technical, managerial, and business changes in response to climate change impacts. Such changes are domi­nated by technical changes, including using high-yielding, disease-resistant, and early-maturing crop varieties, crop and animal pest and disease manage­ment, agricultural water and land management, and wind and bush fire control. Interconnections between the MCLT system components include cross-component investments, additional income generation, animal feeding and healthcare improve­ment, nutrition exchanges, and family nutrition improvement. These interconnections generate income and cash flow and support food and nutri­tion security, enabling farmers’ adaptation. Cli­mate-resilient innovation bundles to enable farmers’ adaptation include good agricultural prac­tices, circular farming techniques, irrigation pack­ages, information services, and value-chain link­ages. Scaling climate-resilient innovations in northern Ghana and other sub-Saharan African contexts require multiple pathways, including inno­vation platforms, innovation bundling, multi-actor partnerships, inclusive finance, and multistake­holder dialogues to support farmers’ adaptation to climate change.

Xitian Tang, Wenzhen Gu, Yao Hu et al.

Abstract With the rapid development of global medical informatization, information systems have become a critical part of the nursing profession The training of dental assistants lacks specialized information management tools, resulting in increasing difficulty in time-consuming training management. To evaluate a new electronic dental assistants self-design training program and examine its effects on informatization management. A self-design training management system was developed. The core module of the system, the clinical training management module, consisted of six different parts: resource management, course management, examination assessment, student management, teacher management, and evaluation management. 137 dental assistants were enrolled to complete the training and assessed through the system. With the introduction of the new system, the coverage of the required courses arranged by the nursing department increased from 80.29 to 100% (P < 0.01). The average time for the training supervisors to complete the evaluation of dental assistants’ monthly examination significantly decreased from (94.80 ± 28.95) to (8.30 ± 3.30) minutes (P < 0.05). The pass rate of the N2-level promotion test increased from 68.18 to 100% (P < 0.01). The four-hand operation response time of outpatient dental assistants was significantly decreased from (5.42 ± 0.83) to (3.37 ± 0.76) seconds (P < 0.05); The satisfaction score of 94.2% of doctors on dental assistants passed 44 points, 95.6% of dental assistants and 100% training supervisors rated the training management system on satisfaction score of more than 36 points. This training management system significantly enhances the training experience for dental assistants, not only by boosting participation rates but also by streamlining training management time and elevating the overall quality of training along with staff satisfaction. This study highlights the potential for electronic management systems to enhance the quality and efficiency of dental assistant training globally.

M. S. Pavithran, B. Sreeram, Adwait V. Pillai et al.

Weather forecasting is an essential aspect of climate-sensitive industries like agriculture, water resources management, and disaster risk management. Short-range forecasts enable prompt decision-making, whereas medium-range and long-range predictions are vital for strategic decision-making and policy formulation. Traditional forecasting models tend to fail to detect the intricate, non-linear, and scale-dependent processes inherent in meteorological records. Although classical models are capable of providing some level of predictability, they tend to lack the ability to describe temporal dynamics and nonlinear relationships of meteorological information. Deep learning is increasingly becoming an influential alternative because it has the capability for modeling sequence dependencies and spatiotemporal patterns. This research deals with the issue of enhancing multi-scale weather forecasts using sophisticated neural architectures. Particularly, it compares and examines LSTM, LSTM-CNN, and LSTM-Transformer models for forecasting temperature, humidity, and rainfall at different time resolutions. Weather data for Chennai between 2000 and 2025 were retrieved through the Open-Meteo API. The data were resampled into daily, weekly, and monthly scales, normalized, and fed into a walk-forward validation process. Each model was tuned with Keras Tuner and evaluated using different metrics. Findings indicate that LSTM-CNN has the best performance for short-term forecasting because it can learn local patterns, and LSTM-Transformer is best suited for long-range forecasting with global attention mechanisms. Rainfall, because it is bursty, still proves to be the hardest parameter to accurately model. The research finds that architecture choice should be dependent on the forecast horizon, and that hybrid models are promising candidates for improving accuracy and scalability. These results support the creation of intelligent, geographically specific climate forecasting systems for climate-resilient decision-making. These insights can directly support agricultural scheduling and water resource planning, offering region-specific decision support for climate resilience.

RunQing Wu, KaiHui Huang, HanYi Zhang et al.

Continuously acquiring new knowledge from a dynamic environment is a fundamental capability for animals, facilitating their survival and ability to address various challenges. This capability is referred to as continual learning, which focuses on the ability to learn a sequence of tasks without the detriment of previous knowledge. A prevalent strategy to tackle continual learning involves selecting and storing numerous essential data samples from prior tasks within a fixed-size memory buffer. However, the majority of current memory-based techniques typically utilize a single memory buffer, which poses challenges in concurrently managing newly acquired and previously learned samples. Drawing inspiration from the Complementary Learning Systems (CLS) theory, which defines rapid and gradual learning mechanisms for processing information, we propose an innovative dual memory system called the Information-Theoretic Dual Memory System (ITDMS). This system comprises a fast memory buffer designed to retain temporary and novel samples, alongside a slow memory buffer dedicated to preserving critical and informative samples. The fast memory buffer is optimized employing an efficient reservoir sampling process. Furthermore, we introduce a novel information-theoretic memory optimization strategy that selectively identifies and retains diverse and informative data samples for the slow memory buffer. Additionally, we propose a novel balanced sample selection procedure that automatically identifies and eliminates redundant memorized samples, thus freeing up memory capacity for new data acquisitions, which can deal with a growing array of tasks. Our methodology is rigorously assessed through a series of continual learning experiments, with empirical results underscoring the effectiveness of the proposed system.

D. Robey

Myeonghyun Kim, Kisung Park, Youngho Park

As the usage of electric vehicles (EVs) expands, various energy management technologies, including battery energy storage systems, are being developed to efficiently charge EVs using various energy sources. In recent years, many blockchain-based energy trading schemes have been proposed for secure energy trading. However, existing schemes cannot fully solve privacy issues and security problems during energy trading. In this paper, we propose a reliable and privacy-preserving vehicular energy trading scheme utilizing decentralized identifier technology. In the proposed scheme, identity information and trading result information are not revealed publicly; this is due to the use of decentralized identifiers and verifiable credential technologies. Additionally, only parties who have successfully conducted energy trading can manage complete transaction information. We also demonstrate our method’s security and ensure privacy preservation by performing informal and formal security analyses. Furthermore, we analyze the performance and security features of the proposed scheme and related works and show that the proposed scheme has competitive performance.

Huiyuan Zhang, Yinglin Wang, Ruigang Diao et al.

Abstract Background The sharp increase in fungal infections, insufficient diagnostic and treatment capabilities for fungal infections, poor prognosis of patients with fungal infections as well as the increasing drug resistance of fungi are serious clinical problems. It is necessary to explore the implementation and evaluation methods of antifungal stewardship (AFS) to promote the standardized use of antifungal drugs. Methods The AFS programme was implemented at a tertiary first-class hospital in China using a plan-do-check-act (PDCA) quality management tool. A baseline investigation was carried out to determine the utilization of antifungal drugs in pilot hospitals, analyse the existing problems and causes, and propose corresponding solutions. The AFS programme was proposed and implemented beginning in 2021, and included various aspects, such as team building, establishment of regulations, information construction, prescription review and professional training. The management effectiveness was recorded from multiple perspectives, such as the consumption of antifungal drugs, the microbial inspection rate of clinical specimens, and the proportion of rational prescriptions. The PDCA management concept was used for continuous improvement to achieve closed-loop management. Results In the first year after the implementation of the AFS programme, the consumption cost, use intensity and utilization rate of antifungal drugs decreased significantly (P < 0.01). The proportion of rational antifungal drug prescriptions markedly increased, with the proportion of prescriptions with indications increasing from 86.4% in 2019 to 97.0% in 2022, and the proportion of prescriptions with appropriate usage and dosage increased from 51.9 to 87.1%. In addition, after the implementation of the AFS programme, physicians’ awareness of the need to complete microbial examinations improved, and the number of fungal cultures and serological examinations increased substantially. Statistics from drug susceptibility tests revealed a decrease in the resistance rate of Candida to fluconazole. Conclusion This study indicated that the combination of AFS and the PDCA cycle could effectively reduce antifungal consumption and promote the rational use of antifungal drugs, providing a reference for other health care systems to reduce the overuse of antifungal drugs and delay the progression of fungal resistance.

João Varajão

Pikkin Lau, Lingfeng Wang, Wei Wei et al.

In this paper, a novel cyber-insurance model design is proposed based on system risk evaluation with smart technology applications. The cyber insurance policy for power systems is tailored via cyber risk modeling, reliability impact analysis, and insurance premium calculation. A stochastic Epidemic Network Model is developed to evaluate the cyber risk by propagating cyberattacks among graphical vulnerabilities. Smart technologies deployed in risk modeling include smart monitoring and job thread assignment. Smart monitoring boosts the substation availability against cyberattacks with preventive and corrective measures. The job thread assignment solution reduces the execution failures by distributing the control and monitoring tasks to multiple threads. Reliability assessment is deployed to estimate load losses convertible to monetary losses. These monetary losses would be shared through a mutual insurance plan. To ensure a fair distribution of indemnity, a new Shapley mutual insurance principle is devised. Effectiveness of the proposed Shapley mutual insurance design is validated via case studies. The Shapley premium is compared with existent premium designs. It is shown that the Shapley premium has high indemnity levels closer to those of Tail Conditional Expectation premium. Meanwhile, the Shapley premium is nearly as affordable as the coalitional premium and keeps a relatively low insolvency probability.

Maximilian Stölzle, Cosimo Della Santina

Even though a variety of methods have been proposed in the literature, efficient and effective latent-space control (i.e., control in a learned low-dimensional space) of physical systems remains an open challenge. We argue that a promising avenue is to leverage powerful and well-understood closed-form strategies from control theory literature in combination with learned dynamics, such as potential-energy shaping. We identify three fundamental shortcomings in existing latent-space models that have so far prevented this powerful combination: (i) they lack the mathematical structure of a physical system, (ii) they do not inherently conserve the stability properties of the real systems, (iii) these methods do not have an invertible mapping between input and latent-space forcing. This work proposes a novel Coupled Oscillator Network (CON) model that simultaneously tackles all these issues. More specifically, (i) we show analytically that CON is a Lagrangian system - i.e., it possesses well-defined potential and kinetic energy terms. Then, (ii) we provide formal proof of global Input-to-State stability using Lyapunov arguments. Moving to the experimental side, we demonstrate that CON reaches SoA performance when learning complex nonlinear dynamics of mechanical systems directly from images. An additional methodological innovation contributing to achieving this third goal is an approximated closed-form solution for efficient integration of network dynamics, which eases efficient training. We tackle (iii) by approximating the forcing-to-input mapping with a decoder that is trained to reconstruct the input based on the encoded latent space force. Finally, we show how these properties enable latent-space control. We use an integral-saturated PID with potential force compensation and demonstrate high-quality performance on a soft robot using raw pixels as the only feedback information.

Maice Costa, M. Codreanu, A. Ephremides

Yuliia Kovalenko, Tetyana Bilovus, Valentyna Unynets-Khodakivska et al.

The purpose of this study is to study the indicators for evaluating the effectiveness of the implementation of investment processes on the securities market, taking into account the scientific foundations of information management systems and analysis of indicators of financial efficiency of the investment function of the securities market in Ukraine.The relevance of this study is due to the growing importance of management information systems in all sectors of the Ukrainian economy, in particular, the provision of solutions to the problems of activating investment processes in the securities market of Ukraine by analyzing and reassessing the effectiveness of investment processes at this level, taking into account the scientific basis of management information systems. A set of indicators that best reflect the implementation of the investment function of the Ukrainian securities market is proposed. A matrix of characteristics of investment processes in the securities market is proposed. It is argued why domestic and foreign investors prefer local securities market indices when making investment decisions. Through the implementation of correlation-regression models, it has been proven that, on average, 87% of changes in investments in securities are due to changes in the number of licensed entities, which on the Chedoch scale indicates a close relationship between the indicators. The results obtained using statistical inference methods indicate a high impact of both external macroeconomic factors that inhibit the development of the securities market and internal, which in turn is reflected in the indicators of assessing the effectiveness of investment processes in the securities market.

Joachim Schöpfel, Otmane Azeroual

The paper offers a comprehensive examination of the ethical considerations surrounding the management of research information within an organization. It includes a summary of survey findings and an overview of ongoing research efforts. Special attention is given to ethical standards pertaining to data quality, data models, and data formats, which are essential factors in organizing research knowledge. As open science continues to gain prominence, research ethics - such as integrity, openness, and transparency - become increasingly crucial. Adhering to these open science principles will play a pivotal role in determining future research funding. Hence, it is crucial to understand how current research information systems (CRIS), a particular type of knowledge organization systems, deal with matters concerning scientific misconduct and integrate ethical guidelines for both individuals and institutions into their data structures.

Fatkhurokhman Fauzi, Dewi Ratnasari Wijaya, Tiani Wahyu Utami

Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, crude oil will still be a contributor to the gross domestic product in 2021. The excessive consumption of fuel oil (BBM) in Indonesia has resulted in a scarcity of crude oil, especially diesel. Forecasting the price of Brent crude oil is an important effort to anticipate fluctuations in the price of fuel oil. The cascade-forward neural network (CFNN) method is proposed to forecast fuel prices because of its superiority in fluctuating data types. The data used in this research is the price of Brent crude oil in the period January 2008 to December 2022. The CFNN method will be evaluated using the mean absolute percentage error (MAPE) to choose the best architectural model. The best Architectural Model is used to predict the next 12 months. After 10 architectural model trials, 2-6-1 became the best model with a MAPE data training value of 6.3473% and MAPE data testing of 9.4689%. Forecasting the results for Brent crude oil for the next 12 months tends to experience a downward trend until December 2023.