Hasil untuk "Management information systems"

R. Hillestad, J. Bigelow, A. Bower et al.

P. Olsson, C. Folke, F. Berkes

Mohamed Abdel-Basset, Gunasekaran Manogaran, M. Mohamed

Abstract The traditional supply chains faces several challenges such as uncertainty, cost, complexity and vulnerable problems. To overcome these problems the supply chains must be more smarter. For establishing a large-scale of smart infrastructure to merge data, information, products, physical objects and all processes of supply chain, we applies the internet of things (IOT) in supply chain management (SCM) through building a smart and secure system of SCM. We have prepared a website for suppliers and managers. We tracked the flow of products at each stage in supply chain management through the Radio Frequency Identification (RFID) technology. Each product attached with RFID tag and scanned through RFID reader and ESP8266 at each phase of supply chain management. After scanning the tag we stores tag id in the database. All information about products will be entered by suppliers and then uploaded to managers. In our system the supplier and manager gets perfect information of the entire life cycle of goods, and this will achieve transparency of supply chain management. For assessing security criteria of proposed system of supply chain management, we also proposed a framework which integrates neutrosophic Decision Making Trial and Evaluation Laboratory (N-DEMATEL) technique with analytic hierarchy process (AHP). The neutrosophic Decision Making Trial and Evaluation Laboratory (N-DEMATEL) technique is utilized to infer cause and effect interrelationships among criteria of smart supply chain security requirements. Depending on obtained information from (N-DEMATEL) the neutrosophic AHP is utilized to calculate weight of criteria and sub-criteria. Then the integrated framework will help researchers and practitioners to design secure system of supply chains. We presented DEMATEL and AHP in neutrosophic environment to deal effectively with vague, uncertain and incomplete information. So the proposed system of supply chain management will be able to overcome all challenges of traditional SCM and provide secure environment of SCM processes.

Simon Bennett

Abstract Purpose: This paper draws on a sociological analysis of the 2024 Azerbaijan Airlines flight J2-8243 incident to critique the Conflict Zone Information Bulletin (CZIB) normative-bureaucratic safety protocol. Sociological theories referenced include: passive and active learning; latent and active error; systems-thinking; organisational accident and the Swiss cheese model of accident trajectory. The Azerbaijan Airlines Flight J2-8243 incident followed earlier shoot-downs. For example: Iran Air Flight 655; Malaysia Airlines Flight MH17; Ukraine International Airlines Flight PS752. Earlier shoot-downs confirmed the importance of risk-free routing. Design/methodology: The paper draws on authoritative secondary data, for example, the 2025 Preliminary Report published by Kazakhstan’s Commission for Aviation Investigation, to mount a critique of the Conflict Zone Information Bulletin (CZIB) normative-bureaucratic safety protocol. The paper: Describes the factors that contributed to the shoot-down; Tests the efficacy of normative-bureaucratic defences against shoot-downs, such as the sharing of information via International Civil Aviation Organisation (ICAO) Annex 13-compliant investigations and the issuing of CZIBs; Asks whether accident investigators pay sufficient attention to the social, economic and political context of a near-miss, incident, accident or shoot-down when establishing causation; Assesses the workload implications of considering the social, economic and political context of a near-miss, incident, accident or shoot-down. Findings: It is concluded that Azerbaijan Airlines failed to act on the relevant CZIB for two reasons. First, Azerbaijan’s government expected the airline to maintain an air bridge with its influential neighbour. While a private concern, Azerbaijan Airlines is the country’s de facto flag carrier, and is expected to act as such by the government. Secondly, the authoritarian character of Azerbaijan’s government discouraged the airline from questioning its government. It is concluded that the European Union’s CZIB normative-bureaucratic safety protocol is compromised by realpolitik without and within the aviation industry. Normative-bureaucratic safety protocols such as CZIBs may create a false sense of security, given this reality. Originality/value: To the best of the author’s knowledge, at the time this paper was written the 2024 Azerbaijan Airlines flight J2-8243 accident trajectory had not been subjected to a holistic, sociological analysis. The paper’s value lies in that fact that it examines the immediate and proximate causes of the flight J2-8243 incident through a powerful sociological lens that draws on the work of risk-management luminaries such as Professor James Reason, whose Swiss Cheese model of system failure is used by many aviation accident investigators.

Shumaila Javaid, Nasir Saeed

Electromagnetic (EM) communication is approaching fundamental physical and thermodynamic limits, where further performance gains through spectrum expansion and waveform optimization alone are increasingly unsustainable. The purpose of this paper is to explore how wireless communication may evolve beyond the EM paradigm by reframing information transfer as controlled manipulation of physical, biological, and cognitive states rather than radiative signal propagation. The main contribution of this work is a state-centric conceptual framework for post-6G communication. The paper identifies and categorizes ten foundational paradigms, including quantum-state transfer, atomic and lattice-level signaling, biological communication, cognitive telepresence, and spacetime-based coordination, defining potential non-EM and hybrid communication mechanisms. In addition, a research roadmap is outlined to place these paradigms within plausible future network generations beyond 6G. The key findings of this study are conceptual. The analysis shows that diverse communication mechanisms across physical, biological, and cognitive domains can be unified using common principles such as state transduction, coherence preservation, entropy management, and energy-aware conversion. These findings indicate that future communication systems may evolve from spectrum-bound infrastructures into adaptive and self-organizing networks that integrate information transfer with sensing, computation, and actuation. This work establishes a conceptual reference framework for future theoretical and interdisciplinary research on communication beyond conventional EM-based systems.

DR. Jenkins, C. Auckland, C. Chadwick et al.

Abstract Introduction Carbapenemase-producing Enterobacterales (CPE) are an important public health threat, with costly operational and economic consequences for NHS Integrated Care Systems and NHS Trusts. UK Health Security Agency guidelines recommend that Trusts use locally developed risk assessments to accurately identify high-risk individuals for screening, and implement the most appropriate method of testing, but this presents many challenges. Methods A convenience sample of cross-specialty experts from across England met to discuss the barriers and practical solutions to implementing UK Health Security Agency framework into operational and clinical workflows. The group derived responses to six key questions that are frequently asked about screening for CPE. Key findings Four patient groups were identified for CPE screening: high-risk unplanned admissions, high-risk elective admissions, patients in high-risk units, and known positive contacts. Rapid molecular testing is a preferred screening method for some of these settings, offering faster turnaround times and more accurate results than culture-based testing. It is important to stimulate action now, as several lessons can be learnt from screening during the COVID-19 pandemic, as well as from CPE outbreaks. Conclusion Further decisive and instructive information is needed to establish CPE screening protocols based on local epidemiology and risk factors. Local management should continually evaluate local epidemiology, analysing data and undertaking frequent prevalence studies to understand risks, and prepare resources– such as upscaled screening– to prevent increasing prevalence, clusters or outbreaks. Rapid molecular-based methods will be a crucial part of these considerations, as they can reduce unnecessary isolation and opportunity costs.

Hu Ankui, Wu Mengkun, Zhong Bo et al.

Abstract The construction of underground cavern groups represents a particularly challenging task in current subsurface engineering due to a multitude of variable and often unknown factors, including diverse geological conditions. This study introduces a four-dimensional spatiotemporal model and formulates a dynamic safety information model for these underground systems. Developed using C# and Python, the model integrates the finite element analysis software ABAQUS and Microsoft SQL Server database. The framework allows for real-time visual management of monitoring data, dynamic coupling of construction phases with safety metrics, and continual updates correlating with construction progress. The theoretical findings offer valuable insights for enhancing the safety and efficiency of underground cavern group construction while also supplying methods for real-time safety feedback and control throughout the construction process.

Gamidullaeva Leyla, Vornovskaia Anastasiia

Relevance and goal. The relevance of this work is due to the need of finding effective approaches to determining the long-term structure of the regional economy for an alternative strategy for making management decisions in order to ensure balanced development of the internal territory. The research analyzes the capabilities of big data technology and demonstrates promising analytical tools for more effective use of the “smart specialization” approach in order to determine industry priorities for the structural transformation of regional economies. Materials and methods. The research is based on general scientific (induction, deduction, comparison, system-structural, etc.) and special research methods – big data analysis of the social network VKontakte, comparative analysis, analysis of the regulatory framework. This study was carried out using materials from two regions of the Russian Federation: Kaliningrad oblast and Penza oblast. Resources such as portal “RosNavyk”, social network VK, analytical platform PolyAnalyst were used. The data sources were the Spatial Development Strategy of the Russian Federation until 2025 and HeadHunter.ru, a website providing job search and recruitment services. Results. The authors obtained the following specific results: firstly, promising sectors of the regions were identified, taking into account the main parameters of the labor market; secondly, the authors conducted a comparative analysis of the results obtained with the data from the Spatial Development Strategy of the Russian Federation; thirdly, a relationship between promising regional specializations and the attitude of local residents towards popular professions in the region was identified based on social media data. Conclusions. The use of end-to-end big data technology to identify promising specializations in the region opens up new opportunities in this area and allows to operationalize the concept of “smart specialization” as a promising tool for implementing spatial development policies. The information about the attitude of local residents of the regions towards certain professions is of high value from the point of view of further connecting industry priorities identified as a result of the analysis of regional contexts, as well as the research and innovation potential that they possess, with the views and expectations of participants in regional economic systems. The practical use of this approach will allow to make effective management decisions and pursue a balanced industry policy that takes into account current patterns emerging in the labor market and the attitude of the region's population towards certain professions. Stakeholders of this information may be universities, employers, professional communities and associations, regional authorities, as well as relevant ministries and departments.

Aitizaz Ali, Bander Ali Saleh Al-rimy, Abdulwahab Ali Almazroi et al.

In the era of interconnected and intelligent cyber-physical systems, preserving privacy has become a paramount concern. This paper aims a groundbreaking proof-of-concept (PoC) design that leverages consortium blockchain technology to address privacy challenges in cyber-physical systems (CPSs). The proposed design introduces a novel approach to safeguarding sensitive information and ensuring data integrity while maintaining a high level of trust among stakeholders. By harnessing the power of consortium blockchain, the design establishes a decentralized and tamper-resistant framework for privacy preservation. However, ensuring the security and privacy of sensitive information within CPSs poses significant challenges. This paper proposes a cutting-edge privacy approach that leverages consortium blockchain technology to secure secrets in CPSs. Consortium blockchain, with its permissioned nature, provides a trusted framework for governing the network and validating transactions. By employing consortium blockchain, secrets in CPSs can be securely stored, shared, and accessed by authorized entities only, mitigating the risks of unauthorized access and data breaches. The proposed approach offers enhanced security, privacy preservation, increased trust and accountability, as well as interoperability and scalability. This paper aims to address the limitations of traditional security mechanisms in CPSs and harness the potential of consortium blockchain to revolutionize the management of secrets, contributing to the advancement of CPS security and privacy. The effectiveness of the design is demonstrated through extensive simulations and performance evaluations. The results indicate that the proposed approach offers significant advancements in privacy protection, paving the way for secure and trustworthy cyber-physical systems in various domains.

Kiran Jabeen, Muhammad Attique Khan, Jamel Balili et al.

One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters’ initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets—CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework.

Bekelu Negash, Alan Katz, Christine J. Neilson et al.

Introduction Using data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). There are established procedures for de-identifying structured data, but de-identifying clinical notes, electronic health records, and other records that include free text data is more complex. Several different ways to achieve this are documented in the literature. This scoping review identifies categories of de-identification methods that can be used for free text data. Methods We adopted an established scoping review methodology to examine review articles published up to May 9, 2022, in Ovid MEDLINE; Ovid Embase; Scopus; the ACM Digital Library; IEEE Explore; and Compendex. Our research question was: What methods are used to de-identify free text data? Two independent reviewers conducted title and abstract screening and full-text article screening using the online review management tool Covidence. Results The initial literature search retrieved 3,312 articles, most of which focused primarily on structured data. Eighteen publications describing methods of de-identification of free text data met the inclusion criteria for our review. The majority of the included articles focused on removing categories of personal health information identified by the Health Insurance Portability and Accountability Act (HIPAA). The de-identification methods they described combined rule-based methods or machine learning with other strategies such as deep learning. Conclusion Our review identifies and categorises de-identification methods for free text data as rule-based methods, machine learning, deep learning and a combination of these and other approaches. Most of the articles we found in our search refer to de-identification methods that target some or all categories of PHII. Our review also highlights how de-identification systems for free text data have evolved over time and points to hybrid approaches as the most promising approach for the future.

K. S. Arikumar, Sahaya Beni Prathiba, Mamoun Alazab et al.

Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person’s movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.

Bertrand MOCQUET

The advent of digital technologies in academic organizations is not new. Several movements have taken place in the last thirty years or so, with the beginning of computerization of university management dating back to the 1990s. This article proposes to make a feedback between 2016 and 2020 on the modalities of construction of the information systems of 180 French universities and institutions, made by the Agency for the Mutualisation of Universities and Institutions (Amue), by mutualisation and collective construction, commonly called co-construction. We will show that the current IS construction is based on Engeström's theory of activity and that the community of practice plays an important role in the success of this construction.

Yian Xu

This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173801 The application of building extraction involves a wide range of fields, including urban planning, land use analysis and change detection. It is difficult to determine whether each pixel is a building or not because of the large difference within the building category. Therefore, automatic building extraction from aerial images is still a challenging research topic. Although deep convolutional networks have many advantages, the networks used for image-level classification cannot be directly used for pixel-level building extraction tasks. This is caused by successive steps larger than one in the pooling or convolution layer. These operations will reduce the spatial resolution of feature maps. Therefore, the spatial resolution of the output feature map is no longer consistent with that of the input, which cannot meet the task requirements of pixel- level building extraction. In this paper, we propose a encoder-decoder structure based on conditional random field for building extraction in remote sensing images. The problem of boundary information lost by unitary potential energy in traditional conditional random field is solved through multi-scale building information. It also preserves the local structure information. The network consists of two parts: encoder sub-network and decoder sub-network. The encoder sub-network compresses the spatial resolution of the input image to complete the feature extraction. The decoder sub-network improves the spatial resolution from features and completes building extraction. Experimental results show that the proposed framework is superior to other comparison methods in terms of the accuracy on open data sets, and can extract building information in complex scenes well.

Ni Kadek Sutriasih, I Made Dwi Putra Asana, Ni Putu Suci Meinarni

PT Arpan Bali Utama merupakan perusahaan manufaktur yang bergerak dalam bidang produksi minuman wine. Adapun beberapa permasalahan yang ada yaitu disaat proses pencatatan transaksi pembelian dan penerimaan barang, serta permasalahan lainnya yaitu, pihak manager tidak memiliki laporan perbandingan pemesanan dan penerimaan sehingga tidak diketahui bagaimana pelayanan dari pihak supplier. Solusi dari masalah tersebut adalah dengan membangun sistem pengadaan barang. Langkah-langkah yang dilakukan dalam tahap pengembangan sistem ini antara lain dengan metode: analisis perancangan, implementasi dan pengujian sistem dengan black box testing dan pengujian User Acceptance Test (UAT). Hasil dari penelitian bahwa sistem informasi pengadaan barang dapat membantu perusahaan dalam pencatatan dan pelaporan pengadaan barang. Terdapat fitur data barang, data supplier, data purchase order, data penerimaan, data pembayaran serta data penjualan yang berfungsi dalam pencatatan pembelian dan penjualan barang. Selain itu juga terdapat fitur penjurnalan yang berfungsi untuk pencatatan biaya-biaya pada perusahaan. Manager perusahaan juga dapat mencetak laporan pembelian, penjualan, perbandingan, penerimaan, laporan jurnal, buku besar, laba rugi serta laporan neraca saldo. Hasil pengujian menggunakan metode black box testing dan pengujian User Acceptance Test (UAT). Metode black box testing menunjukkan bahwa semua fitur yang terdapat dalam sistem berjalan dengan baik sesuai fungsinya. Sedangkan hasil pengujian User Acceptance Test terhadap sistem pendukung keputusan ini memperoleh persentase 86.5%.

G. Dickson, R. Leitheiser, James C. Wetherbe et al.

M. Carey, L. Haas, P. Schwarz et al.

Panagiotis Kanatas, Ilias S. Travlos, Ioannis Gazoulis et al.

Decision support systems (DSS) have the potential to support farmers to make the right decisions in weed management. DSSs can select the appropriate herbicides for a given field and suggest the minimum dose rates for an herbicide application that can result in optimum weed control. Given that the adoption of DSSs may lead to decreased herbicide inputs in crop production, their potential for creating eco-friendly and profitable weed management strategies is obvious and desirable for the re-designing of farming systems on a more sustainable basis. Nevertheless, it is difficult to stimulate farmers to use DSSs as it has been noticed that farmers have different expectations of decision-making tools depending on their farming styles and usual practices. The function of DSSs requires accurate assessments of weeds within a field as input data; however, capturing the data can be problematic. The development of future DSSs should target to enhance weed management tactics which are less reliant on herbicides. DSSs should also provide information regarding weed seedbank dynamics in the soil in order to suggest management options not only within a single period but also in a rotational view. More aspects ought to be taken into account and further research is needed in order to optimize the practical use of DSSs for supporting farmers regarding weed management issues in various crops and under various soil and climatic conditions.

Lazar D. Gitelman, Vladimir V. Dobrodey, Mikhail V. Kozhevnikov

Nowadays, it is relevant to consider changes in the structure of the fuel and energy balance of industrial regions and the availability of imported fuel and energy resources, especially in the areas that lack energy sources. The ongoing structural shifts in energy consumption systems and the growing uncertainty in energy markets encourage the development of tools for improving the sustainable development of regional energy systems. To refine the theoretical and methodological basis of the study, we defined its conceptual framework, described the difference s betwee n sustainabl e functionin g an d developmen t o f th e energy sector and determined the factors of its regional differentiation and manifestations of the energy crisis. Further, we identified the shortcomings of the existing methods for forecasting the demand for electricity. We paid special attention to quality factors of strategic planning in the region, in particular, the used statistics and documents. Based on the analysis of integrated resource planning (IRP) methodology, our experience in forecasting fuel and energy balances, assessment of sectoral indicators of energy efficiency and energy demand in the region, we proposed a model for predictive and analytical justification of regional programmes for energy development. Such a model significantly increases the information reliability of these programmes’ implementation. Considering organisational tools to support sustainable development, we developed a regional energy management scheme and a mechanism stimulating local energy companies to improve energy efficiency in the consumption sector, enhance regional competition and attract investments in the renewal of fixed assets. The study has practical significance due to recommendations and tools for adjusting regional energy policy based on the coordination of the predicted parameters for various participants in the energy supply process.

C. Brock Kirwan, C. Brock Kirwan, Daniel K. Bjornn et al.

Computer users are often the last line of defense in computer security. However, with repeated exposures to system messages and computer security warnings, neural and behavioral responses show evidence of habituation. Habituation has been demonstrated at a neural level as repetition suppression where responses are attenuated with subsequent repetitions. In the brain, repetition suppression to visual stimuli has been demonstrated in multiple cortical areas, including the occipital lobe and medial temporal lobe. Prior research into the repetition suppression effect has generally focused on a single repetition and has not examined the pattern of signal suppression with repeated exposures. We used complex, everyday stimuli, in the form of images of computer programs or security warning messages, to examine the repetition suppression effect across repeated exposures. The use of computer warnings as stimuli also allowed us to examine the activation of learned fearful stimuli. We observed widespread linear decreases in activation with repeated exposures, suggesting that repetition suppression continues after the first repetition. Further, we found greater activation for warning messages compared to neutral images in the anterior insula, pre-supplemental motor area, and inferior frontal gyrus, suggesting differential processing of security warning messages. However, the repetition suppression effect was similar in these regions for both warning messages and neutral images. Additionally, we observed an increase of activation in the default mode network with repeated exposures, suggestive of increased mind wandering with continuing habituation.