Hasil untuk "Management information systems"

A. Gunasekaran, E. Ngai

Rishav Sen, Yunuo Zhang, Fangqi Liu et al.

Vehicle-to-building (V2B) systems integrate physical infrastructures, such as smart buildings and electric vehicles (EVs) connected to chargers at the building, with digital control mechanisms to manage energy use. By utilizing EVs as flexible energy reservoirs, buildings can dynamically charge and discharge them to optimize energy use and cut costs under time-variable pricing and demand charge policies. This setup leads to the V2B optimization problem, where buildings coordinate EV charging and discharging to minimize total electricity costs while meeting users' charging requirements. However, the V2B optimization problem is challenging because of: (1) fluctuating electricity pricing, which includes both energy charges ($/kWh) and demand charges ($/kW); (2) long planning horizons (typically over 30 days); (3) heterogeneous chargers with varying charging rates, controllability, and directionality (i.e., unidirectional or bidirectional); and (4) user-specific battery levels at departure to ensure user requirements are met. In contrast to existing approaches that often model this setting as a single-shot combinatorial optimization problem, we highlight critical limitations in prior work and instead model the V2B optimization problem as a Markov decision process (MDP), i.e., a stochastic control process. Solving the resulting MDP is challenging due to the large state and action spaces. To address the challenges of the large state space, we leverage online search, and we counter the action space by using domain-specific heuristics to prune unpromising actions. We validate our approach in collaboration with Nissan Advanced Technology Center - Silicon Valley. Using data from their EV testbed, we show that the proposed framework significantly outperforms state-of-the-art methods.

Dinara A. Osmonalieva , Chinara R. Kulueva , Ibrokhimbek D. Nasirkhodjaev et al.

We dwelt on the directions of product quality management in the conditions of climate-smart agriculture with the application of corporate information systems based on machine learning. The revealed aspects are used with different results within different countries and territories, which have individual climate characteristics and approaches to agricultural production. Specifics of agriculture could be unified with innovative climate-smart approaches to certain processes. We showed that such synthesis allows creating the most optimal solutions to reduce the climate footprint and raise productivity. The considered countries have a potential for further creation of intellectual digital solutions to improve quality management in climate-smart agriculture. This would help achieve results to ensure national and global food security. The goal of this paper was to reveal the key features of using the means of machine learning in corporate information systems to ensure product quality management in climate-smart agriculture. The scientific novelty of this research consisted in the improvement of theoretical and practical substantiation of the forms of interaction between parties that are interested in an increase in the efficiency of climate-smart agriculture with the use of machine learning tools. The main research methods were a systemic approach, comparison of advantages and disadvantages, statistical analysis, and ranking method.

Hongrui Shi, Shunbao Li, Zhipeng Yuan et al.

Effective pest management is complex due to the need for accurate, context-specific decisions. Recent advancements in large language models (LLMs) open new possibilities for addressing these challenges by providing sophisticated, adaptive knowledge acquisition and reasoning. However, existing LLM-based pest management approaches often rely on a single-agent paradigm, which can limit their capacity to incorporate diverse external information, engage in systematic validation, and address complex, threshold-driven decisions. To overcome these limitations, we introduce PestMA, an LLM-based multi-agent system (MAS) designed to generate reliable and evidence-based pest management advice. Building on an editorial paradigm, PestMA features three specialized agents, an Editor for synthesizing pest management recommendations, a Retriever for gathering relevant external data, and a Validator for ensuring correctness. Evaluations on real-world pest scenarios demonstrate that PestMA achieves an initial accuracy of 86.8% for pest management decisions, which increases to 92.6% after validation. These results underscore the value of collaborative agent-based workflows in refining and validating decisions, highlighting the potential of LLM-based multi-agent systems to automate and enhance pest management processes.

Dong Zhou, Bida Pei, Xueqin Li et al.

Abstract Within the global architecture, engineering, and construction industry, the use of Building Information Modeling (BIM) technology has significantly expanded. However, given the unique characteristics of road infrastructure, the application of BIM technology is still being explored. This article focuses on the Yuanchen Expressway, exploring innovative applications of BIM technology in comprehensive construction management. The project employs advanced technologies, including BIM, Geographic Information Systems (GIS), and the Internet of Things (IoT), to precisely identify critical nodes and breakthroughs. Supported by a detailed BIM model and a multi-level, diversified digital management platform, the project effectively addresses construction challenges in multiple tunnels, bridges, and complex interchanges, achieving intelligent construction innovation throughout the Yuanchen Expressway with BIM technology. By guiding construction through BIM models, utilizing a BIM+GIS-based management cloud platform system, and employing VR safety briefings, the project effectively reduces the difficulty of communication and coordination in project management, shortens the project measurement cycle, improves on-site work efficiency, and ensures comprehensive control and safety management. This article provides an exemplary case for the application of full-line construction management using BIM technology in the highway sector both in China and globally, offering new perspectives and strategies for highway construction management.

Ezeldin Sherif, Waleed Helmy, Galal Hassan Galal-Edeen

Agile Software Development (ASD) is a type of iterated software development that strives to maximize productivity, effectiveness, and quick delivery through the minimization of documents and needless procedures within constrained timeframes. Agile software development has a number of advantages. There are still some difficulties. For instance, during the development lifecycle, non-functional requirements (NFRs) are disregarded and not given first-class artifacts. This results in several issues, including customer dissatisfaction and a great deal of rework, which impacts time and cost. In this paper, a proposed framework for handling non-functional requirements in Agile is explained. The framework supports the several primary activities of requirements engineering including requirements elicitation, analysis, documentation, and validation. In addition, the framework handles non-functional recommendations. Results of the suggested solution validation showed that it could address the problems with non-functional requirements in Agile.

Ayu Aprilyana Kusuma Dewi, I Putu Eka Indrawan

The tourism sector has the advantage that it is currently undergoing continuous expansion and diversification compared to the manufacturing sector. Current tourism development in various regions offers innovative services through the application of information and communication technology. Currently, the application of mini dictionary applications is more widely applied in city tourism areas that have complete basic infrastructure, adequate availability of information and communication technology and a comprehensive service system. In the tourism sector in Indonesia has considerable potential and attractiveness in the natural and cultural tourism sector, in the development of tourism the central government through The Ministry of Tourism and Creative Economy/Tourism and Creative Economy Agency in each province has the aim of increasing the number of visits and services. This research will examine each tourist attraction, which in its development has developed as an urban area with the availability of supporting tourism facilities / infrastructure that has been fulfilled. The purpose of this study is to identify the level of readiness of tourist attractions in implementing mini dictionary applications in terms of the availability and quality of basic infrastructure services and ICT and tourist support facilities. This research uses the waterfall process method used to describe the research approach to the analysis process. The results of the analysis show that the level of readiness of the application of mini dictionaries at tourist attractions is stated to be against ready. The readiness of the application of the mini dictionary in terms of the availability and quality of basic infrastructure services and ICT and tourist support, based on the results of a review of all components in its application that can be reviewed from infrastructure, facilities and service systems shows that almost all tourist attractions have shown readiness in basic infrastructure and for ICT infrastructure and tourist support facilities are still in a somewhat ready position. This is due to the inadequate quantity and also ICT that has not been applied in tourism management.

Daniel Sousa-Dias, Daniel Amyot, Ashkan Rahimi-Kian et al.

Advances in energy generation and distribution technology have created the need for new power management paradigms. Transactive energy markets are integrated software and hardware systems that enable optimized energy management and direct trading between prosumers. This literature review covers unresolved security and privacy vulnerabilities in the proposed implementations of such markets. We first performed a coarse search for such implementations. We then combed the resulting literature for references to privacy concerns, security vulnerabilities, and attacks that their system was either vulnerable to or sought to address. We did so with a particular focus on threats that were not mitigated by the use of blockchain technology, a commonly employed solution. Based on evidence from 28 peer-reviewed papers, we synthesized 14 categories of concerns and their proposed solutions. We found that there are some concerns that have been widely addressed, such as protecting trading history when using a public blockchain. Conversely, there were serious threats that are not sufficiently being considered. While a lack of real-world deployment has limited information about which attacks are most likely or feasible, there are clear areas of priority that we recommend to address going forward, including market attacks, false data injection attacks, single points of failure, energy usage data leakage, and privacy.

Mohammad Tafiqur Rahman, Tim A. Majchrzak, Maung K. Sein

Abstract Responding to a disaster encompasses a myriad of humanitarian actions; the ultimate and crucial is immediate relief distribution. Making effective decisions in chaotic disaster environment is always complex and challenging. Decisions made here are heavily influenced by the decisions made in several related problem areas such as facility locations, relief supply chain, transportation, scheduling, and inventory management. While each of these problem areas has its own set of decision factors, several of these factors are also common in multiple problem areas. These common decision factors offer both an opportunity and a challenge. The challenge is to balance the relative importance of a factor that is common between one or more problem areas—one factor that is considered vital in one area may have a lower priority in another area. The opportunity here is to develop a common framework that can help all problem areas to work together to achieve the main objective of effectively distributing essential relief goods among affected people. While the literature has studied individual problem areas and their decision factors, an integrated view showing the linkages between multiple problem areas is missing. In this paper, we propose such an integrative framework. Based on a systematic review of the literature, we first identified problem areas that are linked to relief distribution and then identified the linkages between these areas. We synthesized the findings into a conceptual framework and validated it through a panel of experienced field experts who work in relief distribution. We framed our refined framework as an information ecosystem of humanitarian actions where relief distribution resides at the core. Such a conceptualization will not only enrich the in-depth understanding of humanitarian domain, but also offer insights for developing computer-based decision support systems for relief distribution.

João Varajão

Han Yan, Qinwei Ran, Ronghai Hu et al.

Extensive grassland degradation under climate change and intensified human activities has threatened ecological security and caused a variety of environmental problems. However, it is still challenging to predict the grassland degradation status on a large scale because it is a multi-factorial phenomenon with complex changes in ecosystem structure and function, which is hard to be fully characterized through mechanism models. The emergence of machine learning algorithms provides a potential to model complex systems and mine information from multi-source data without elucidating underlying mechanisms. Here, we utilized random forest and neural network algorithms to predict the grassland degradation represented by the net primary productivity (NPP) changing rate based on multi-source data including geographic, meteorological, plant traits, land use type and microbial variables in the Chinese Northern grassland. Particularly, the microbial roles in determining the degradation status were concerned. Results show that a high prediction precision was achieved by random forest model, rather than by neural network model, with a mean relative error of 16.9% and a mean square error of 9.273e-05. Besides identified longitude, arid index and current NPP state, specific soil microbial groups, mainly Solirubrobacter, were screened as credible biomarkers. Regarding model fitting, geographic, meteorological and plant variables explained 61.8% of the total variance, which was enhanced up to 72.8% by the rest microbial markers. These findings provide a theoretical basis to establish a pre-warning system for grassland management and policy-making.

Diya Namira Purba, Fhira Nhita, Isman Kurniawan

Schizophrenia is a chronic mental illness that leads the patient to hallucinations and delusions with a prevalence of 0.4% worldwide. The importance early detection of Schizophrenia is tracking the pre-syndrome of Schizophrenia during the active phase, and could reduce psychosis symptomatic. However, the method sometimes cannot detect the symptoms accurately. As an alternative, machine learning can be implemented on microarray data for early detection. This study aimed to implement three ensemble methods, i.e., Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to identify Schizophrenia. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that the model 6, which is developed by the XGBoost method, performs better than other models with the value of accuracy and F1-score are 0.87 and 0.87, respectively.

Marija Marković, Bri-Mathias Hodge

This paper introduces a new model for highly accurate distribution voltage solutions, coined as a parameterized linear power flow model. The proffered model is grounded on a physical model of linear power flow equations, and uses learning-aided parameterization to increase the fidelity of voltage solutions over a wide range of operating points. To this end, the closed-form analytic solution of the parameterization approach is obtained via a Gaussian Process using a deliberately small input sample and without the need for recomputation. The resulting "self-adjusting" parameter is system-specific and controls how accurate the proposed power flow equations are according to loading conditions. Under a certain value of the resulting parameter, the proposed model can fully recover the linearized formulation of a specialized branch flow model for radial distribution systems, the so-called simplified DistFlow model. Numerical examples are provided to illustrate the effectiveness of the proposed model as well as the improvement in solution accuracy for voltage magnitudes over the simplified DistFlow model and several other linear power flow models, at multiple loading levels. Simulations were carried out on six small- and medium-sized test systems.

Rogério Alves Soares, Marcirio Silveira Chaves, Cristiane Drebes Pedron

Despite the positive influence of risk management in Information Technology (IT) project results, many project managers are not managing risks or are managing them partially. To enhance risk management, collaborative project management has gained attention in recent years with the introduction of Web 2.0 tools. Project managers have used such tools to facilitate open communication and distribution of activities. This research introduces a prescriptive framework (W4RM – Wiki for Risk Management) based on a wiki to support collaborative risk management in IT projects. An exploratory focus group was set up and a series of interviews with practitioners was conducted to explore how a wiki can support risk management in IT projects. Findings show that project managers are facing difficulties managing risks and are the only ones responsible for identifying, registering and monitoring risks. By implementing a collaborative tool, managers can disseminate a collaboration culture and participate in risk management processes. This sense of collaboration may be used to keep the community identifying new risks, relating these risks to one or more projects, and facilitating continuous risk management. Practitioners can also adopt W4RM as a tool to support communication regarding risks status to be established for internal team stakeholders.

Bruce Bode, Aaron King, David Russell-Jones et al.

Primary care providers (PCPs) play an important role in providing medical care for patients with type 2 diabetes. Advancements in diabetes technologies can assist PCPs in providing personalised care that addresses each patient’s individual needs. Diabetes technologies fall into two major categories: devices for glycaemic self-monitoring and insulin delivery systems. Monitoring technologies encompass self-measured blood glucose (SMBG), where blood glucose is intermittently measured by a finger prick blood sample, and continuous glucose monitoring (CGM) devices, which use an interstitial sensor and are capable of giving real-time information. Studies show people using real-time CGM have better glucose control compared to SMBG. CGM allows for new parameters including time in range (the time spent within the desired target glucose range), which is an increasingly relevant real-time metric of glycaemic control. Insulin pens have increased the ease of administration of insulin and connected pens that can calculate and capture data on dosing are becoming available. There are a number of websites, software programs, and applications that can help PCPs and patients to integrate diabetes technology into their diabetes management schedules. In this article, we summarise these technologies and provide practical information to inform PCPs about utility in their clinical practice. The guiding principle is that use of technology should be individualised based on a patient’s needs, desires, and availability of devices. Diabetes technology can help patients improve their clinical outcomes and achieve the quality of life they desire by decreasing disease burden.KEY MESSAGESIt is important to understand the role that diabetes technologies can play in primary care to help deliver high-quality care, taking into account patient and community resources. Diabetes technologies fall into two major categories: devices for glycaemic self-monitoring and insulin delivery systems. Modern self-measured blood glucose devices are simple to use and can help guide decision making for self-management plans to improve clinical outcomes, but cannot provide “live” data and may under- or overestimate blood glucose; patients’ monitoring technique and compliance should be reviewed regularly. Importantly, before a patient is provided with monitoring technology, they must receive suitably structured education in its use and interpretation.Continuous glucose monitoring (CGM) is now standard of care for people with type 1 diabetes and people with type 2 diabetes on meal-time (prandial) insulin. Real-time CGM can tell both the patient and the healthcare provider when glucose is in the normal range, and when they are experiencing hyper- or hypoglycaemia. Using CGM data, changes in lifestyle, eating habits, and medications, including insulin, can help the patient to stay in a normal glycaemic range (70–180 mg/dL). Real-time CGM allows for creation of an ambulatory glucose profile and monitoring of time in range (the time spent within target blood glucose of 70–180 mg/dL), which ideally should be at least 70%; avoiding time above range (>180 mg/dL) is associated with reduced diabetes complications and avoiding time below range (<70 mg/dL) will prevent hypoglycaemia. Insulin pens are simpler to use than syringes, and connected pens capture information on insulin dose and injection timing.There are a number of websites, software programs and applications that can help primary care providers and patients to integrate diabetes technology into their diabetes management schedules. The guiding principle is that use of technology should be individualised based on a patient’s needs, desires, skill level, and availability of devices.

Xuecheng Liu, Luoyi Fu, Xinbing Wang et al.

The von Neumann graph entropy is a measure of graph complexity based on the Laplacian spectrum. It has recently found applications in various learning tasks driven by networked data. However, it is computational demanding and hard to interpret using simple structural patterns. Due to the close relation between Lapalcian spectrum and degree sequence, we conjecture that the structural information, defined as the Shannon entropy of the normalized degree sequence, might be a good approximation of the von Neumann graph entropy that is both scalable and interpretable. In this work, we thereby study the difference between the structural information and von Neumann graph entropy named as {\em entropy gap}. Based on the knowledge that the degree sequence is majorized by the Laplacian spectrum, we for the first time prove the entropy gap is between $0$ and $\log_2 e$ in any undirected unweighted graphs. Consequently we certify that the structural information is a good approximation of the von Neumann graph entropy that achieves provable accuracy, scalability, and interpretability simultaneously. This approximation is further applied to two entropy-related tasks: network design and graph similarity measure, where novel graph similarity measure and fast algorithms are proposed. Our experimental results on graphs of various scales and types show that the very small entropy gap readily applies to a wide range of graphs and weighted graphs. As an approximation of the von Neumann graph entropy, the structural information is the only one that achieves both high efficiency and high accuracy among the prominent methods. It is at least two orders of magnitude faster than SLaQ with comparable accuracy. Our structural information based methods also exhibit superior performance in two entropy-related tasks.

Thierry Roncalli

This article is part of a comprehensive research project on liquidity risk in asset management, which can be divided into three dimensions. The first dimension covers the modeling of the liability liquidity risk (or funding liquidity), the second dimension is dedicated to the modeling of the asset liquidity risk (or market liquidity), whereas the third dimension considers the management of the asset-liability liquidity risk (or asset-liability matching). The purpose of this research is to propose a methodological and practical framework in order to perform liquidity stress testing programs, which comply with regulatory guidelines (ESMA, 2019, 2020) and are useful for fund managers. In this third and last research paper focused on managing the asset-liability liquidity risk, we explore the ALM tools that can be put in place to control the liquidity gap. These ALM tools can be split into three categories: measurement tools, management tools and monitoring tools. In terms of measurement tools, we focus on the computation of the redemption coverage ratio (RCR), which is the central instrument of liquidity stress testing programs. We also study the redemption liquidation policy and the different implementation methodologies, and we show how reverse stress testing can be developed. In terms of liquidity management tools, we study the calibration of liquidity buffers, the pros and cons of special arrangements (redemption suspensions, gates, side pockets and in-kind redemptions) and the effectiveness of swing pricing. In terms of liquidity monitoring tools, we compare the macro- and micro-approaches of liquidity monitoring in order to identify the transmission channels of liquidity risk.

R. Pulwarty, M. Sivakumar

Abstract Drought is among the most damaging, and least understood, of all “natural” hazards. Although some droughts last a single season and affect only small areas, the instrumental and paleoclimate records show that droughts have sometimes continued for decades and have impacted millions of square kilometers in North America, West Africa, and East Asia. To cross the spectrum of potential drivers and impacts, drought information systems have multiple sub-systems which include an integrated risk assessment, communication and decision support system of which early warning is a central component and output. An early warning system is much more than a forecast – it is a linked risk information (including people׳s perception of risk) and communication system that actively engages communities involved in preparedness. There are numerous drought systems warning systems being implemented at different scales of governance. We draw on the lessons of over 21 drought early warning systems around the world, in both developing and developed countries and at regional, national and community levels. The successes illustrate that effective early warning depends upon a multi-sectoral and interdisciplinary collaboration among all concerned actors at each stage in the warning process from monitoring to response and evaluation. However, the links between the community-based approach and the national and global EWSs are relatively weak. Using the rich experience of information systems across the globe, this paper identifies pathways for knowledge management and action at the relevant scales for decision-making in response to a changing climate.

Adrian Rohr, Peter Priesmeier, Katerina Tzavella et al.

Emergency management services, such as firefighting, rescue teams and ambulances, are all heavily reliant on road networks. However, even for highly industrialised countries such as Germany, and even for large cities, spatial planning tools are lacking for road network interruptions of emergency services. Moreover, dependencies of emergency management expand not only on roads but on many other systemic interrelations, such as blockages of bridges. The first challenge this paper addresses is the development of a novel assessment that captures systemic interrelations of critical services and their dependencies explicitly designed to the needs of the emergency services. This aligns with a second challenge: capturing system nodes and areas around road networks and their geographical interrelation. System nodes, road links and city areas are integrated into a spatial grid of tessellated hexagons (also referred to as tiles) with geographical information systems. The hexagonal grid is designed to provide a simple map visualisation for emergency planners and fire brigades. Travel time planning is then optimised for accessing city areas in need by weighing impaired areas of past events based on operational incidents. The model is developed and tested with official incident data for the city of Cologne, Germany, and will help emergency managers to better device planning of resources based on this novel identification method of critical areas.

László Gogolák, Igor Fürstner

Striving for excellence during the assembling process through incorporating the expectations of Industry 4.0 requires complex information management on issues of overall system status, especially the physical characteristics and position of the parts being assembled, as well as the assembling units and tools. This research introduces both an overall customized assembling system supervision model, which is based on a modified four-layer control system hierarchy that suits the specific requirements of such systems and the developed wireless sensor network technology for assembling process management with a particular focus on localization. The developed model highlights the localization problems of the system as well as other aspects required for overall system status determination. The localization of assembled parts is based on the fingerprint localization method by using the received signal strength indicator. The proposed localization algorithms are based either on artificial neural networks or on the weighted k-nearest neighbor method. The developed model has been tested both in laboratory conditions and in a simulated industrial environment. The research results offer a general solution to the problem of assembling system supervision, regardless of size and shape, with emphasis on the localization problem solution.