Hasil untuk "Management information systems"

Richa Tanya Jeff Dennis A Colleen Evan Devon J Rodney St Agarwala Barrett Beck Benson Bollin Bolton Bourexi, R. Agarwala, Tanya Barrett et al.

Abstract The National Center for Biotechnology Information (NCBI) provides a large suite of online resources for biological information and data, including the GenBank® nucleic acid sequence database and the PubMed database of citations and abstracts for published life science journals. The Entrez system provides search and retrieval operations for most of these data from 39 distinct databases. The E-utilities serve as the programming interface for the Entrez system. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. New resources released in the past year include PubMed Data Management, RefSeq Functional Elements, genome data download, variation services API, Magic-BLAST, QuickBLASTp, and Identical Protein Groups. Resources that were updated in the past year include the genome data viewer, a human genome resources page, Gene, virus variation, OSIRIS, and PubChem. All of these resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

Jay Lee, Fangji Wu, Wenyu Zhao et al.

K. Laudon, Jane P. Laudon, A. Gilfillan et al.

A. Hevner, S. Chatterjee

P. Longley, M. Goodchild, D. Maguire et al.

J. Jasperson, Pamela E. Carter, R. Zmud

Kieran Conboy

Awareness and use of agile methods has grown rapidly among the information systems development (ISD) community in recent years. Like most previous methods, the development and promotion of these methods have been almost entirely driven by practitioners and consultants, with little participation from the research community during the early stages of evolution. While these methods are now the focus of more and more research efforts, most studies are still based on XP, Scrum, and other industry-driven foundations, with little or no conceptual studies of ISD agility in existence. As a result, this study proposes that there are a number of significant conceptual shortcomings with agile methods and the associated literature in its current state, including a lack of clarity, theoretical glue, parsimony, limited applicability, and naivety regarding the evolution of the concept of agility in fields outside systems development. Furthermore, this has significant implications for practitioners, rendering agile method comparison and many other activities very difficult, especially in instances such as distributed development and large teams that are not conducive to many of the commercial agile methods. This study develops a definition and formative taxonomy of agility in an ISD context, based on a structured literature review of agility across a number of disciplines, including manufacturing and management where the concept originated, matured, and has been applied and tested thoroughly over time. The application of the texonomy in practice is then demonstrated through a series of thought trials conducted in a large multinational organization. The intention is that the definition and taxonomy can then be used as a starting point to study ISD method agility regardless of whether the method is XP or Scrum, agile or traditional, complete or fragmented, out-of-the-box or in-house, used as is or tailored to suit the project context.

S. Seidel, J. Recker, J. Brocke

Shubham Vatsal, Harsh Dubey, Aditi Singh

Large Language Model (LLM)-based agents that plan, use tools and act has begun to shape healthcare and medicine. Reported studies demonstrate competence on various tasks ranging from EHR analysis and differential diagnosis to treatment planning and research workflows. Yet the literature largely consists of overviews which are either broad surveys or narrow dives into a single capability (e.g., memory, planning, reasoning), leaving healthcare work without a common frame. We address this by reviewing 49 studies using a seven-dimensional taxonomy: Cognitive Capabilities, Knowledge Management, Interaction Patterns, Adaptation &#x0026; Learning, Safety &#x0026; Ethics, Framework Typology and Core Tasks &#x0026; Subtasks with 29 operational sub-dimensions. Using explicit inclusion and exclusion criteria and a labeling rubric (<italic>Fully Implemented &#x2713;</italic>, <italic>Partially Implemented</italic> <inline-formula> <tex-math notation="LaTeX">$\Delta $ </tex-math></inline-formula>, <italic>Not Implemented &#x2717;</italic>), we map each study to the taxonomy and report quantitative summaries of capability prevalence and co-occurrence patterns. Our empirical analysis surfaces clear asymmetries. For instance, the External Knowledge Integration sub-dimension under Knowledge Management is commonly realized (~76% &#x2713;) whereas Event-Triggered Activation sub-dimenison under Interaction Patterns is largely absent (~92% &#x2717;) and Drift Detection &#x0026; Mitigation sub-dimension under Adaptation &#x0026; Learning is rare (~98% &#x2717;). Architecturally, Multi-Agent Design sub-dimension under Framework Typology is the dominant pattern (~82% &#x2713;) while orchestration layers remain mostly partial. Across Core Tasks &#x0026; Subtasks, information centric capabilities lead e.g., Medical Question Answering &#x0026; Decision Support and Benchmarking &#x0026; Simulation, while action and discovery oriented areas such as Treatment Planning &#x0026; Prescription still show substantial gaps (~59% &#x2717;). Together, these findings provide an empirical baseline indicating that current agents excel at retrieval-grounded advising but require stronger adaptation and compliance platforms to move from early-stage systems to dependable systems.

Da Sheng, L. Ding, B. Zhong et al.

Abstract The information generated from a nonconformance can be used to determine the party responsible for ensuring that quality standards are assured. However, in the construction industry, the absence of a uniform and transparent system for managing quality information undermines the assurance process and may lead to disputes among stakeholders. In addressing this issue, we develop a blockchain-based framework for managing quality information, referred to as “Product Organization Process (POP) qualityChain”. A Hyperledger-Fabric-based architecture and a series of blockchain solutions (e.g., a POP-model-based quality information structure, a consensus mechanism, smart contracts for processing quality information, authorization sequences, and execution processes) are covered. Finally, we build a prototype system and use a case study to validate our framework. Results show that the proposed framework can decentralize the management of quality information, thereby achieving consistent and secure quality information management. Future work could seek more evidence from practice to further validate our framework.

K. Paustian, Sarah M. Collier, J. Baldock et al.

Abstract The importance of building/maintaining soil carbon, for soil health and CO2 mitigation, is of increasing interest to a wide audience, including policymakers, NGOs and land managers. Integral to any approaches to promote carbon sequestering practices in managed soils are reliable, accurate and cost-effective means to quantify soil C stock changes and forecast soil C responses to different management, climate and edaphic conditions. While technology to accurately measure soil C concentrations and stocks has been in use for decades, many challenges to routine, cost-effective soil C quantification remain, including large spatial variability, low signal-to-noise and often high cost and standardization issues for direct measurement with destructive sampling. Models, empirical and process-based, may provide a cost-effective and practical means for soil C quantification to support C sequestration policies. Examples are described of how soil science and soil C quantification methods are being used to support domestic climate change policies to promote soil C sequestration on agricultural lands (cropland and grazing land) at national and provincial levels in Australia and Canada. Finally, a quantification system is outlined – consisting of well-integrated data-model frameworks, supported by expanded measurement and monitoring networks, remote sensing and crowd-sourcing of management activity data – that could comprise the core of a new global soil information system.

Turhan Karakaya, Yavuz Selim Balcıoğlu, Abdullah Kürşat Merter

Water scarcity has become a material financial risk, yet extant research treats corporate water use as a purely environmental issue. The present study employs stakeholder theory as a theoretical framework and as a heuristic device to test the hypothesis that strong social-performance scores insulate high-water-using firms from valuation penalties. Using data from 11,000 companies worldwide between 2015 and 2023, which includes Refinitiv ESG ratings, CDP water data, and verified financial information, we compare profitability, growth, and market value based on water usage and social scores, while also considering factors like company size, industry, region, year, and other ESG aspects. Our findings demonstrate that this study is the first to quantify the buffering degree of social performance against water-related financial penalties across a comprehensive multi-industry sample. It has been demonstrated that high-water firms in the top social quartile exhibit profit margins that are 21.7% higher and market capitalizations that are 30% larger than their peers with weak social ratings. Interaction models show that the water–value penalty falls by 60% for socially responsible firms, especially in the utilities and food processing sectors. Decomposition analysis reveals that community engagement provides the strongest protective effect (42.3%), followed by product responsibility (35.1%), while human rights initiatives show the weakest buffering capacity (18.9%). The results obtained demonstrate the cross-pillar complementarities of ESG and provide a foundation for the development of integrated disclosure policy.

Arnolnt Spyros, Ilias Koritsas, Angelos Papoutsis et al.

Cyber Threat Intelligence (CTI) is an important asset for organisations to facilitate the safeguarding of their systems against new and emerging cyber threats. CTI continuously provides up-to-date information which enables the design and implementation of better security measures and mitigation strategies. Organisations gather data from different sources either internal or external to the organisation, which are analysed, resulting in CTI. Nevertheless, the gathered data usually contain a large amount of content that is irrelevant to CTI or even to cybersecurity. Furthermore, most approaches concerning CTI management (e.g., gathering, analysis) involve simply gathering and storing the information without any enrichment such as classification or correlation. However, in order to obtain optimal results, organisations should be able to utilise all capabilities of CTI. Therefore, in this work, we propose ThreatWise AI, a novel framework that enables the gathering, analysis, enrichment, storage, and sharing of CTI in an efficient and secure manner. In particular, we have developed a novel pipeline in ThreatWise AI which incorporates different advanced tools, with distinct capabilities that interact with each other to provide a complete set of functionalities for the administration of the overall CTI lifecycle. The developed tools integrate various Python scripts and provide gathering and analysis functionalities of CTI. Furthermore, the proposed framework leverages the MISP platform for storing, enriching and sharing while also integrating Artificial Intelligence (AI) and Machine Learning (ML) algorithms for advanced data enrichment.

YAO Chang, HAO Yanni, PENG Shenghui, NIU Zhiang, XIE Yong, ZHAO Shizhen

In recent years,with the widespread adoption and rapid development of technologies such as the Internet,big data and artificial intelligence,scientific research management is gradually shifting from traditional models to new paradigms driven by data,informatization and intelligence.This transformation has made traditional information system architectures increasingly inadequate to cope with the demands of massive data exchange,data sharing and data security.This paper takes the information system architecture of the National Natural Science Foundation of China(hereinafter referred to as “the NSFC”) as an example to explore its development and evolution directions.Faced with the advancement of science fund reforms in the new era,the existing information systems of the NSFC are facing numerous challenges in terms of network and data security,enhancement of intelligent service levels,and optimization of system development and management efficiency.This paper first introduces the current application status of two traditional information system architectures in the NSFC,and then conducts an in-depth analysis of the challenges faced by the existing information systems from four aspects:the growth of business volume and the number of bu-siness systems,the replacement of information technology with domestic innovation,intelligent services,and data management.Finally,it provides thoughts on the subsequent development of the information system architecture from two dimensions:data ma-nagement and microservices.

Lana V Ivanitskaya, Dimitrios Zikos, Elina Erzikova

BackgroundFueled by innovations in technology and health interventions to promote, restore, and maintain health and safeguard well-being, the field of eHealth has yielded significant scholarly output over the past 25 years. ObjectiveThis study aims to offer a big picture of research developments and multidisciplinary contributions to eHealth that shaped this field up to 2024. To that end, we analyze evidence from 3 corpora: 10,022 OpenAlex documents with eHealth in the title, the 5000 most relevant eHealth articles according to the Web of Science (WoS) algorithm, and all available (n=1885) WoS eHealth reviews. MethodsUsing VOSviewer, we built co-occurrence networks for WoS keywords and OpenAlex concepts. We examined clusters, categorized terminology, and added custom overlays about eHealth technologies, stakeholders, and objectives. A cocitation map of sources referenced in WoS reviews helped identify scientific fields supporting eHealth. After synthesizing eHealth terminology, we proceeded to build a conceptual model of eHealth scholarship grounded in bibliometric evidence. ResultsSeveral research directions emerged from bibliometric networks: eHealth studies on self-management and interventions, especially in mental health; telemedicine, telehealth, and technology acceptance; privacy, security, and design concerns; health information consumers’ literacy; health promotion and prevention; mHealth and digital health; and HIV prevention. Conducted at the individual, health system, community, and society levels, eHealth studies focused on health and wellness across the human lifespan. Keywords such as internet (mean publication year 2017), telemedicine (2018), telehealth (2018), mHealth (2019), mobile health (2020), and digital health (2021) were strongly linked to literature indexed with eHealth (2019). Different types of eHealth apps were supported by research on infrastructures: networks, data exchange, computing technologies, information systems, and platforms. Researchers’ concerns for eHealth data security and privacy, including advanced access control and encryption methods, featured prominently in the maps, along with terminology related to health analytics. Review authors cited a wide range of medical sources and journals specific to eHealth technologies, as well as journals in psychology, psychiatry, public health, policy, education, health communication, and other fields. The Journal of Medical Internet Research stood out as the most cited source. The concept map showed a prominent role of political science and law, economics, nursing, business, and knowledge management. Our empirically derived conceptual model of eHealth scholarship incorporated commonly researched stakeholder groups, eHealth application types, supporting infrastructure, health analytics concepts, and outcomes. ConclusionsDrawing upon contributions from many disciplines, the field of eHealth has evolved from early studies of internet-enabled communications, telemedicine, and telehealth to research on mobile health and emerging digital health technologies serving diverse stakeholders. Digital health has become a popular alternative term to eHealth. We offered practical implications and recommendations on future research directions, as well as guidance on study design and publication.

Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi et al.

This study investigates and predicts the likelihood of operational risk occurrence in the banking industry using machine learning algorithms. The primary objective is to analyze operational risk data and evaluate the performance of various machine learning models to develop effective tools for enhancing risk management and minimizing financial losses in banks and financial institutions. Operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), and k-Nearest Neighbors (KNN). Model performance was assessed using evaluation metrics such as accuracy, precision, recall, F1-score, and the Area Under the Curve (AUC) to determine the most effective model for risk prediction. The findings indicate that the RF and SVM algorithms outperform other models in predicting operational risk across all scenarios. Furthermore, the results demonstrate the strong predictive capability of machine learning algorithms in assessing operational risk, highlighting their potential as valuable decision-making tools for risk management in the banking sector.Keywords: Risk Prediction, Operational Risk, Risk Management, Machine Learning IntroductionOperational risk is defined as the risk arising from external factors or failures in internal controls or information systems, which may lead to both anticipated and unexpected losses (Crouchy et al., 1998). Lopez (2002) characterizes it as any unquantifiable risk that a bank may encounter. According to the Basel II Agreement, operational risk refers to the probability of loss resulting from deficiencies, breakdowns, or inefficiencies in human resources, processes, technologies, infrastructure, or internal and external events (Pena et al., 2018).To estimate the capital required to cover operational risk, the Basel framework introduces three approaches: the Basic Indicator Approach (BIA), the Standardized Approach (SA), and the Advanced Measurement Approach (AMA) (Mora Valencia, 2010; Mora Valencia et al., 2017). The BIA and SA estimate capital requirements based on annual gross income, with the key distinction being that the SA categorizes a bank’s activities into eight business lines. Under the BIA, an alpha coefficient (α) of 15% is applied, whereas in the SA, each business line has a specific beta coefficient (β) ranging between 12% and 18%. The AMA employs both quantitative and qualitative methods for operational risk modeling, leveraging databases to collect statistical data and utilizing the loss distribution approach (LDA) to model frequency and severity distributions. Capital coverage is then determined based on the cumulative distribution of these variables. Since the LDA is data-driven, the Basel framework (BCBS, 2004) emphasizes the necessity of a robust database for collecting operational risk data. Four key databases are required: internal loss event data, external loss event data, scenario-based analysis data, and a database of business environment and internal control factors.Compared to other banking risks, such as credit and market risks, measuring, monitoring, and managing operational risk is considerably more complex. This risk has gained increasing attention in recent years, as large operational losses have led to the liquidation of financial institutions (Abdymomunov et al., 2020; Afonso et al., 2019). Crisanto and Perino (2017) identify cyber threats and cyber fraud as critical factors influencing operational risk capital estimation. These risks have intensified with the growth of electronic banking services and include illegal access, system disruptions, and the misuse or theft of digital assets for financial gain (BCBS, 2016; Drew & Farrell, 2018). To quantify potential losses in electronic banking transactions, Bouveret (2018) proposed a Bayesian Network (BN) model to estimate operational risk capital requirements in financial institutions.Machine learning has emerged as one of the most promising yet challenging approaches in modern finance (Tsai & Wu, 2008). These methods have transformed the financial industry, with deep learning (DL) being extensively studied and applied due to its adaptability and predictive capabilities (Ivanov, 2019). Pena et al. (2021) employed a fuzzy convolutional deep learning model to estimate the maximum operational risk value at a 99.9% confidence level. Similarly, Zhou et al. (2020) utilized semi-supervised machine learning algorithms to classify operational risks based on financial news, analyzing 5,843 documents from financial articles and newspapers in the Asia-Pacific region between February and March 2019. Their model demonstrated the capability to predict various types of risks in the banking industry. In another study, Akbari and Yazdanian (2023) applied machine learning algorithms to determine optimal thresholds for operational loss severity data, classifying the data and estimating the capital required to cover operational risk by integrating severity and frequency distribution functions with Monte Carlo simulation. Method and DataIn this study, operational risk data were collected, pre-processed, and then used for predictions with machine learning models, including RF, DT, SVM, LR, NB, and KNN. The models' performance was assessed using evaluation metrics such as accuracy, precision, recall, F1-score, and AUC to identify the most effective model for predicting the likelihood of risk occurrence. FindingsThe results indicate that the RF and SVM algorithms exhibit strong performance in predicting operational risk across all scenarios. Specifically, the RF algorithm achieved an accuracy of 0.9690, while the SVM algorithm attained an accuracy of 0.9587 in State 1, making them the most effective models in this setting. Both algorithms demonstrated comparable performance across other modes. Conclusion and DiscussionThis study analyzes and predicts operational risk occurrence in the banking industry using machine learning algorithms. The findings indicate that various algorithms, particularly RF and SVM, demonstrate strong predictive performance. These results have the potential to transform operational risk management in banks, leading to significant reductions in associated costs and losses.A key insight from this study is that leveraging large and diverse datasets can substantially enhance prediction accuracy. Machine learning models can process complex datasets, identify hidden patterns, and facilitate early risk detection, enabling banks to implement preventive measures before risks materialize. Moreover, integrating machine learning into risk management enhances decision-making by providing precise, data-driven predictions, allowing for more effective strategies and efficient resource allocation.Future research could incorporate additional data, such as historical records, economic indicators, and internal process information, to further improve prediction accuracy. With advancements in technology, more sophisticated techniques—such as reinforcement learning methods (e.g., DQN, Q-Learning, DDPG, and Meta-Learning)—could enhance the accuracy and efficiency of operational risk prediction models.

S. Dwivedi, Ruhul Amin, Satyanarayana Vollala

Abstract The concept of Supply Chain Management (SCM) is very imperative while moving sensitive products from one entity to the next entity until it reaches to the end-users to avoid damage(s) in the product. In the traditional supply chain management system, several serious problems such as tampering of products, delay, and fraud, etc. exist. It also lacks proper authentication among the participants, data management as well as the integrity of the data. The blockchain mechanism is capable of solving the above-mentioned issues due to its important features such as decentralization, transparency, trust-less environment, anonymity, and immutability. This paper describes how the blockchain mechanism combines with the traditional pharmaceutical supply chain system and to achieve a better SCM system, we present a blockchain-based scheme for information sharing securely in the pharmaceutical supply chain system with smart contracts and consensus mechanism. The proposed scheme also provides a mechanism to distribute required cryptographic keys to all the participants securely using the smart contract technique. Further, transaction and block validation protocols have been designed in our protocol. The security analysis ensures that our protocol is robust and also achieves reasonable performance in terms of computation and communication overheads.

B. Zhong, Haitao Wu, L. Ding et al.

CHEN Jiabin, WANG Chaowei, PANG Mingliang et al.

The integrated sensing and communication(ISAC) system based on orthogonal time frequency space(OTFS) as the transmission waveform is recognized for its higher efficiency of resources, making it one of the key technologies for addressing the shortage of spectrum resources. As the number of sensing targets increases, the difference in signal power received by the base station from the superposition of multiple sensing echo signals becomes less significant. Traditional multi-target channel sensing and target detection algorithms result in error transmission and accumulation, thereby degrading the performance of the system's channel sensing and target detection. A maximum likelihood estimator based multi-objective channel parameter sensing and target detection algorithm was proposed to improve the estimation accuracy of the sensed channel and target parameters. Specifically, the parallel interference cancellation(PIC) algorithm was adopted to the received superimposed signals. The signals were reconstructed using the results obtained from the previous iteration and were subtracted from the received signals. The signal-to-interference-plus-noise ratio of the echo signals in the estimation of the sensed channel and target parameters was improved, so the performance of the maximum likelihood estimator was improved. Simulation results show that the proposed algorithm outperforms the traditional ones in terms of channel estimation accuracy. Additionally, the convergence of the proposed algorithm is also validated to be overhead saving.

Zeeshan Ali, Khumara Ashraf, Naila Siddique et al.

This manuscript aims to find the most key steps and considerations for selecting industry 4.0 technology. For this, we evaluate the technique of circular bipolar complex fuzzy (CBCF) information. Further, we describe the algebraic operational laws and Aczel-Alsina operational laws based on CBCF values. Moreover, we present the technique of the CBCF Aczel-Alsina weighted averaging (CBCFAAWA), CBCF Aczel-Alsina ordered weighted averaging (CBCFAAOWA), CBCF Aczel-Alsina hybrid averaging (CBCFAAHA), CBCF Aczel-Alsina weighted geometric (CBCFAAWG), CBCF Aczel-Alsina ordered weighted geometric (CBCFAAOWG), and CBCF Aczel-Alsina hybrid geometric (CBCFAAHG) operators. Some suitable properties for the above operators are discussed in detail. Additionally, the relation between industry 4.0 and circular bipolar complex fuzzy set theory lies in their complementary roles in coping with the ambiguities and vagueness inherent in modern manufacturing processes, such as decision-making techniques, control systems, predictive maintenance, supply chain management, and conflict analysis. Therefore, to evaluate the best one among the above four, we illustrate the technique of multi-attribute decision-making (MADM) procedure based on initiated operators to show the supremacy and validity of the proposed theory. Finally, we compare our ranking theory with some prevailing ranking results to enhance the stability and worth of the initiated operators.