Hasil untuk "Management information systems"

Elaine B. Barker

This Recommendation provides cryptographic key-management guidance. It consists of three parts. Part 1 provides general guidance and best practices for the management of cryptographic keying material, including definitions of the security services that may be provided when using cryptography and the algorithms and key types that may be employed, specifications of the protection that each type of key and other cryptographic information requires and methods for providing this protection, discussions about the functions involved in key management, and discussions about a variety of key-management issues to be addressed when using cryptography. Part 2 provides guidance on policy and security planning requirements for U.S. government agencies. Part 3 provides guidance when using the cryptographic features of current systems.

U. Benz, P. Hofmann, G. Willhauck et al.

D. Straub, R. Welke

J. Ward, P. Griffiths

B. Weber, M. Reichert, S. Rinderle-Ma

A. Sidorova, Nicholas E. Evangelopoulos, J. Valacich et al.

Ana Rita Trindade, Pedro Matias, Vander Lacerda et al.

Pitaya (<i>Selenicereus</i> spp.) cultivation has expanded in the Iberian Peninsula in recent years, driven mainly by increasing demand from the European market and by the crop’s good adaptability to Mediterranean conditions. However, the successful consolidation of this crop requires the adoption of cultural practices adapted to regional edaphoclimatic conditions and production systems. The present review aims to synthesise and critically analyse the scientific literature on pitaya cultural practices, integrating information from major producing regions worldwide and from Mediterranean environments, where data remain limited. Key topics include propagation methods for success in early development, training systems and pruning, soil management within the framework of sustainable orchard management practices and the crop’s versatility in integrating diverse agroecosystems. In addition, bibliometric analysis identified water requirements and irrigation strategies as key aspects for which region-specific guidelines are still required. This study emphasises the utilisation of floral induction techniques and the significance of supplementary manual pollination for ensuring higher productivity and superior fruit quality. Overall, this review provides a consolidated reference to support the development of sustainable and regionally adapted pitaya production systems in the Iberian Peninsula.

Krishnan Batri, Lakshmi S, Mahesh T R et al.

Hyperspectral anomaly detection faces fundamental challenges in balancing spatial context, statistical rigor, and interpretability without ground truth supervision. This article presents spatially informed theoretical model (STIM), a novel unsupervised framework that addresses these challenges through a principled two-stage reference computation architecture. STIM systematically aggregates local spectral statistics into globally informed spatial references, enabling the derivation of three complementary features: energy (photometric deviation), entropy (local spectral coherence), and divergence (global statistical rarity). We establish theoretical foundations including noise robustness, Lipschitz continuity, and information-theoretic optimality with convergence guarantees. Comprehensive validation on five Airborne Visible/Infrared Imaging Spectrometer&#x2014;Next Generation benchmark datasets demonstrates STIM&#x0027;s substantial superiority over traditional statistical and deep learning methods, achieving 14.6&#x00D7; to 585&#x00D7; improvements in mean anomaly scores with a reliability index of 0.933. Feature dynamics analysis confirms multimodal orthogonality and consistent interpretability across diverse hyperspectral environments. STIM enables robust, interpretable, and generalizable anomaly detection for operational hyperspectral imaging without requiring labeled supervision or scene-specific calibration, advancing the state-of-the-art in unsupervised hyperspectral analysis.

D. Q. Chen, M. Mocker, David S. Preston et al.

Collins M. Mudogo, Angeline Mulwa, Dorothy Kyalo

Background: Although the field of digital health is rapidly growing, there is scanty information on the impact of these interventions on the overall performance of health projects. Objectives: We assessed the influence of utilisation of four types of digital health interventions (DHIs) and application of monitoring and evaluation (ME) practices on performance of human immunodeficiency virus (HIV) projects. Method: This was a cross-sectional survey across eight public health facilities providing care to HIV patients and where all the four types of DHIs were being implemented in Kisumu County, Kenya. A total of 191 service providers who were at their stations of work on the day of data collection were recruited into the study. Aspects of utilisation of the DHIs, application of ME practices and performance of the HIV projects were measured using standardised 12 statements on a 5-point Likert scale. Results: Using a multi-linear regression model, we established that the four DHIs could potentially explain 22% (R2 = 0.22; p-value 0.001 at 95% confidence interval) of variation in performance of HIV projects. Application of best ME practices could further explain the variation of the relationship between utilisation of DHIs and performance of HIV or AIDS projects up to 33.2% (R2 = 0.332; p-value 0.001 at 95% confidence interval). Conclusion: Optimal utilisation of DHIs improves performance of HIV projects. Contribution: This study provides evidence on the importance of utilising digital health in managing health projects. Further, it augments the central role of monitoring and evaluation in project performance.

Tessa Oakley, Juliao Vaz, Fausto da Silva et al.

Abstract Background Effective diagnostic capacity is crucial for clinical decision-making, with up to 70% of decisions in high-resource settings based on laboratory test results. However, in low- and middle-income countries (LMIC) access to diagnostic services is often limited due to the absence of Laboratory Information Management Systems (LIMS). LIMS streamline laboratory operations by automating sample handling, analysis, and reporting, leading to improved quality and faster results. Despite these benefits, sustainably implementing LIMS in LMIC is challenging due to high costs, inadequate infrastructure, and limited technical expertise. Methods This study evaluated the implementation of a customised microbiology LIMS at the National Health Laboratory (NHL) in Timor-Leste. The LIMS was deployed in November 2020, with an accompanying online results portal introduced in early 2021. The implementation was assessed via a checklist based on key challenges and requirements for LIMS in LMIC, alongside a post-implementation survey of scientists and clinicians. Results The assessment revealed significant improvements in laboratory processes, including enhanced sample throughput, data management, and result reporting. The LIMS reduced transcription errors and standardised reporting of antimicrobial susceptibility testing (AST), improving data quality and accessibility. However, challenges such as unreliable internet connectivity and the need for ongoing funding and technical support persist. The user satisfaction survey, with responses from 19 laboratory scientists and 15 clinicians, revealed positive feedback on workflow improvements and result accessibility, although concerns about internet speed, sustainability, and the need for further training were noted. Conclusion This study highlights the importance of careful planning, customisation, and stakeholder engagement in LIMS implementation in LMIC. The success in Timor-Leste demonstrates the potential for improved laboratory quality and patient outcomes, but also underscores the need for ongoing investment in infrastructure, technical expertise, and sustainability planning.

Luxia Yang, Yilin Hou, Hongrui Zhang et al.

Vehicle detection algorithms, which are essential to intelligent traffic management and control systems, have attracted growing attention. However, most high-precision vehicle detection algorithms suffer from high computational effort and slow detection speeds, resulting in the challenging task of deploying these algorithms on mobile devices. In this paper, we propose a lightweight direction-aware network (LDAN) based on the YOLOv8 for vehicle detection on mobile devices. First, a lightweight C2f-GSP module is proposed to optimize the backbone network, which enhances the interaction of local features and fully extracts vehicle information. Then, a triple efficient coordinate attention mechanism (TECA) is designed. The mechanism can fully perceive the details and salient information of input features in multiple directions, thus improving the ability of the model to capture critical features. Moreover, to further reduce model parameters and computational requirements, a lightweight shared convolutional detection head (SCL-Head) is devised using a parameter-sharing mechanism. Finally, experimental results on the KITTI dataset show that the proposed method not only reduces resource consumption but also improves the accuracy of vehicle detection, which provides a novel technical path to realize real-time and accurate vehicle detection on mobile devices.

Mohd Abdul Rahim Khan, Nasser Ali AlJarallah, Ashit Kumar Dutta et al.

Epilepsy is a critical neurological disorder that requires accurate and privacy-preserving diagnostic solutions to enable early detection and effective management. However, existing approaches face key challenges, including reliance on centralized data, poor generalizability across modalities, suboptimal feature extraction, and vulnerability to noise. To address these issues, we propose MultiEpilepsyNet, a novel multimodal seizure detection framework that integrates federated learning with hybrid deep learning models. At its core, the system introduces SeizureFed-Net, a federated architecture that enables collaborative learning from EEG and MRI data while safeguarding patient privacy. For detection, we design SeizureShieldNet, a hybrid model that fuses the temporal learning capabilities of BBIDNet (Boosted BiLSTM Intrusion Detector Network) with the adaptive decision-making of FD-TMS (Fuzzy-DQN Threat Mitigation System) under uncertainty. To further enhance model efficiency, a Jackal-Wolf Hybrid Optimizer (JWHO)—a novel combination of Golden Jackal Optimization and Grey Wolf Optimizer—is employed for optimal feature subset selection. On the imaging side, MRI preprocessing is improved through EpiSkullNet+ +, a modified 3D UNet+ + architecture tailored for precise brain segmentation. Extensive experiments on two benchmark datasets demonstrate the superiority of our approach, achieving 99.36 % accuracy on the CHB-MIT EEG dataset and 99.38 % accuracy on an epilepsy MRI dataset. Beyond accuracy, MultiEpilepsyNet demonstrates improved robustness to missing modalities, reduced training overhead via federated aggregation, and enhanced privacy preservation compared to centralized deep learning models, thereby addressing critical barriers in practical clinical deployment. These outcomes highlight the effectiveness, scalability, and real-world clinical potential of the proposed framework for epilepsy diagnosis and management.

Wei Meng

The decline of single authorship in peer-reviewed journals within the current collaboration-oriented knowledge production framework has prompted deeper reflection on the shifting power structures in academic systems. This paper aims to explore the underlying institutional logic and evaluation mechanisms contributing to the marginalization of single-author research in the management field. It further investigates how the discourse of collaborative advantage conceals structural power redistribution and ideological disembedding. Through an analysis of authorship data from top-tier journals, a critical reading of institutional incentive texts, and an empirical review of authorial configurations, the study building on the work of Harzing, Wuchty, and Lariviere constructs a three-dimensional causal chain: collaboration incentives, responsibility dilution, and originality weakening. Findings suggest that single authorship is not explicitly excluded but is gradually sidelined from central publication channels by funding policies, review practices, and performance metrics. Independent thought is thus structurally marginalized within institutionalized collaboration. The paper advocates for a paradigm shift from instrumental rationality to value-based rationality and calls for the restoration of legitimacy and public value for independent research through reforms in evaluation frameworks, journal governance, and research ethics, aiming to safeguard academic diversity and intellectual autonomy.

Thomas D. Wilson, Elena Maceviciute

We report on a preliminary investigation into the current scope of research in information management, adopting a conceptual approach derived from previous work by Hjørland in information science and by Palvia in information systems. We created a data-set of 107 articles resulting from a search in Web of Science, using the search strategy of the term information management in the titles of articles, and then restricting the analysis to those journals we identified as having an information science orientation, rather than an information systems orientation. The analysis reveals the International Journal of Information Management as the most significant journal in the field, but also draws attention to the rise of interest in the field through contributions to two Brazilian journals and one Spanish journal. The thematic analysis revealed that the dominant research themes from the information science perspective were empirical user studies, studies of the structural and institutional approach, and information system usage and adoption. Further work will be undertaken to explore the relevance of the approach in the analysis of other document sets from areas such as health care, construction and engineering.

S. Grabski, S. Leech, Pamela J. Schmidt

G. Stoneburner, Alice Goguen, Alexis Feringa

Mostafa Pahlevanzadeh, Nadjla Hariri, Dariush Matlabi et al.

Introduction The purpose of the current research is to design a knowledge management system performance evaluation model in the software industry using a neural network. Based on the collected data, a quantitative study was conducted to confirm the findings obtained from the qualitative stage. For exploratory study and extraction of categories related to evaluation factors, the meta-combination method (Sandelowski and Barroso model) was used. The research method in the quantitative part is descriptive survey. The statistical population of the research was made up of all software developers and software experts in universities and companies. Findings: 7 main categories including individual factors, organizational factors, technology and infrastructure, functional factors, knowledge management tools, economic factors, knowledge management tools, and 29 sub-categories were identified. The innovation of the research is building a model using neural network algorithms that have the ability to predict the performance evaluation index of the knowledge management system and the impact of each of the indicators using a neural network in the field of software. Conclusion: The results obtained from the questionnaire have been used for the input of the network model, the results showed that components such as technology infrastructure factors and functional factors have a greater impact on the evaluation of knowledge management performance in software development.Literature ReviewIn a research, they evaluated the performance of the knowledge management system in Iranian software companies. The results showed that the knowledge management system consists of 4 processes of identifying and creating, recording and maintaining, sharing and applying and internalizing knowledge. In a research, they designed a fuzzy inference system to evaluate the performance of the knowledge management system in the software development industry. The use of neural networks in evaluating the key factors of the knowledge management system in Iranian companies based in Alborz province was investigated. A research modeled an organizational knowledge management system based on artificial intelligence. Fuzzy theory was used to create knowledge extraction mechanism and reference model library from project model to dedicated reference model.MethodologyThe method used in this research is a mixed research method of exploratory type with a qualitative approach and meta-composite and Delphi methods. In the first stage, the meta-composite method was used to identify the main and sub-categories of the indicators, and then the validation and presentation of the final indicators were done with the fuzzy Delphi method. The current research method is practical in terms of purpose. The sample size was selected by simple random sampling method with Cochran's formula of 186 people. In the meta-combination method of the research, library sources and documents including articles, reliable and referable internet sources, as well as domestic and foreign scientific reports were used. For exploratory study and extraction of categories related to evaluation factors, the meta-combination method (Sandelowski and Barroso model) was used. Factors and dimensions of knowledge management system evaluation for which indicators are considered were provided to 20 members and experts. The implementation of the Delphi panel was carried out in two periods. Fuzzy Delphi method was used to screen and identify the final indicators and to answer the first and second questions of the research regarding the agreement of the experts of the research community regarding the obtained components, which includes software experts and knowledge management experts. 7 main categories including individual factors, organizational factors, technology and infrastructure, functional factors, knowledge management tools, economic factors, and 29 sub-categories were identified.  In order to collect quantitative data, a researcher-made questionnaire (40 items) was used, the items of which were taken from the results of the meta-composite analysis in the first stage. In this research, in order to check the reliability of the research questionnaire, Cronbach's alpha coefficient was estimated at 0.89 for infrastructure factors and 0.88 for functional factors, respectively.Results In this research, the performance of the knowledge management system was evaluated with a neural network approach. Examining the results showed that the following components affect the evaluation of knowledge management performance in the software development industry. 1. Individual factors 2. Economic factors 3. Organizational factors 4. Knowledge management processes 5. Functional factors 6. Technological infrastructure factors 7. Knowledge management toolsDiscussionSolutions to improve the performance of knowledge management in the software development industry were presented: • Adjust the strategies in such a way that the creation of new knowledge, the application of new knowledge, its dissemination and sharing, and the storage and documentation of knowledge are explicitly considered. • Identifying influential people in the process of implementation and establishment of knowledge management, to improve the effective factors in the effective establishment of knowledge management more than in the past. • Developing procedures for documenting the experiences of experts in the software development industry on a continuous basis. • Managers and practitioners of the software industry should also consider parameters such as available budget, organizational culture, infrastructure, etc. • To provide the relevant managers and practitioners with a criterion for reviewing future policies and investments and help them make more appropriate decisions.ConclusionIn this research, 29 primary indicators have been identified based on the research literature, which include: • Organizational culture for sharing and using knowledge • Organizational Structure • The physical environment • Organization strategy • Support of senior managers such as motivation and commitment • Supporting innovations and digital technologies • Specialized knowledge of software development • General knowledge in software development • Involvement of developers • Education • Being up-to-date in the fields of specialized software • Knowledge and awareness of the knowledge management system • Correct understanding of system design requirements • Portals and portals of knowledge such as the Internet and email and social networks • MIS, Expert, DSS systems • Data warehouse - knowledge warehouse • Search and recovery tools and dashboard • Data security • The degree of integration of organizational systems • Quality of knowledge • Document management • Data management and workflow • Process Management • Creation and acquisition of knowledge, transfer and sharing of knowledge • Acquisition and use of knowledge • Operating cost of the software • Cost of software support.

Iñigo Cuiñas, Isabel Expósito, Darius Andriukaitis et al.

Frequency-Selective Surfaces (FSSs) are structures that act as frequency-dependent electromagnetic filters, enabling innovative designs for energy-efficient building envelopes. This paper explores their potential for energy harvesting and integration into construction materials, offering insights into design strategies, performance analysis, and potential applications of FSS sin future architectural projects. A range of FSS designs are presented and systematically classified based on their performance and adaptability for building integration. This includes their use as part of traditional construction elements or as independent components of building walls. Critical issues such as the limitations, challenges, and durability of FSSs in real-world applications are also examined to provide a comprehensive view of their practical feasibility. Additionally, incorporating the electromagnetic properties of these materials into Building Information Modelling (BIM) systems is recommended. Doing so will enable architects and engineers to better utilize the novel opportunities that FSSs offer, fostering more innovative, energy-efficient building envelopes. Overall, this paper provides valuable insights into how FSSs can transform the future of sustainable architecture and energy management in buildings.

Xudong Qi, Junfeng Yao, Ping Wang et al.

Abstract Accurate traffic flow forecasting is a critical component in intelligent transportation systems. However, most of the existing traffic flow prediction algorithms only consider the prediction under normal conditions, but not the influence of weather attributes on the prediction results. This study applies a hybrid deep learning model based on multi feature fusion to predict traffic flow considering weather conditions. A comparison with other representative models validates that the proposed spatial‐temporal fusion graph convolutional network (STFGCN) can achieve better performance.