154092 research outputs found
Sort by
Leveraging the critical incident technique for uncovering and training the OSCM competences of the future
No full textPurpose: The purpose of this perspective article is twofold. First, it discusses how the critical incident technique (CIT) can serve as a method for identifying competences required in operations and supply chain management (OSCM) in combination with a systematic literature review (SLR) and Delphi approach. Second, it discusses how the CIT can be used for pedagogical purposes and can support the development of the resulting competences in teaching by using critical incidents (CIs) identified during the research process as a form of problem-based learning (PBL). Thus, we illustrate how CIT can drive competence-oriented research and educational advancements to address complex and dilemma-struck challenges in OSCM.Design/methodology/approach: We provide an overview of CIT in the context of research and education and illustrate CIT’s role in identifying competences and developing them through training. Based on previous use of CIT for the identification of sustainable sourcing competences and subsequent training, we highlight CIT’s broader applicability and extend the discussion to other areas in OSCM, such as supply chain resilience and leveraging new technologies.Findings: We propose CIT as an effective tool for recognising and developing competences in OSCM. Both future research implications and pedagogical implications are offered. The strengths and limitations of CIT as a method in both research and educational settings are explored.Research limitations/implications: The illustration of CIT’s application is limited to research on identifying and training sustainable sourcing competences. Further research is recommended to extend CIT’s application to other OSCM areas.Practical implications: For OSCM researchers, educators and practitioners, CIT offers a structured approach to identifying and teaching needed competences, ultimately contributing to more effective training programs in complex supply chain environments.Social implications: For society at large and professional OSCM communities, the ability to adapt to new regulatory and economic realities and address the complex and dilemma-struck challenges in OSCM is highly desirable.Originality/value: We position CIT as a dual-purpose tool for research and education in OSCM. CIT is useful for both identifying competences and training future OSCM leaders, offering a method that can be applied to various complex areas in the field, intended to inspire future research and teaching
Searching for Transactivity in a Collaborative Chat:Analysis of Novelty and Reference with GenAI
No full textTransactivity, or building on the contribution of a learning partner, is an essential part of collaboration. While previous studies often emphasize the context of knowledge co-construction, less attention has been given to the analysis of transactivity independent of task. Recent research opted for perceiving transactivity as having two core elements: novelty and reference. In this study, we have constructed a model operationalizing both elements as scales. Subsequently, we explored the use of ChatGPT for classifying novelty and reference, achieving acceptable inter-rater reliability with human raters. Our dataset consisted of 21 collaborative dialogues of a Computational Thinking assignment in dyads. Results indicated that reference was more continuously present while novelty appeared in peaks. Transactivity, likewise, appeared mostly in isolated peaks. Regarding dyadic collaboration, novelty was also found to be more unevenly distributed than reference. This implied that novelty relied more frequently on one person. Our recommendation to instructional designers is to focus primarily on scaffolding for novelty, preferably tailored to individual participants.</p
A Comparative Study of Machine Learning and Neural Network Models for Phishing Detection
No full textPhishing remains one of the most prevalent cybersecurity threats, particularly within communication systems, as it exploits email platforms to deceive users into disclosing sensitive information. This paper presents a comprehensive comparison of traditional machine learning (ML) models and advanced neural network (NN) architectures for phishing email detection. We evaluate models including Naive Bayes, Logistic Regression, Decision Trees, Random Forests, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) based on their ability to identify phishing attempts from sequential email data. Our study analyzes the trade-offs between model complexity, computational cost, and accuracy, focusing on scalability and generalization to real-world phishing attacks. Results show that while neural networks, particularly LSTM and GRU, can effectively capture complex patterns and dependencies in email content, simpler ML models such as Stochastic Gradient Descent (SGD) achieve competitive accuracy with significantly lower computational overhead. This balance between performance and resource efficiency makes ML models particularly suitable for large-scale, real-time phishing detection systems. The findings of this research offer valuable insights for implementing robust, adaptive, and intelligent phishing detection in secure communication environments
Power-to-methanol:Techno-economic analysis of a regional, decentral case-study
No full textThis study investigates the decentral Power-to-Methanol economics for the situation in The Netherlands. Power-to-X processes are seen as a promising technology for balancing the energy market and enabling fossil-free pathways within the chemical industry. However, within literature there is a lot of variation in the levelized cost of methanol per production. The research presented in this paper consists of three steps, starting with an ASPEN Plus® simulation of different scenarios, followed by calculations of capital investment (CAPEX) and operational & maintenance costs (OPEX), finally resulting in the levelized cost of methanol. Results show that the operational cost greatly exceeds the capital costs, due to the high contributions of either H2 as raw material or sustainable electricity if H2 is produced on site. Levelized cost of sustainable methanol is calculated between 4,059 and 4,510 €/ton MeOH. This is twice as high compared to methanol generated with the same process using grey H2 (2,244 €/ton MeOH) and a factor 6 more expensive compared to bulk MeOH from fossil industry (700 €/ton MeOH). Future trends in costs show that for decentral production, grey and sustainable MeOH can have a similar order of magnitude by 2050.</p
Experimental validation of kinetics and VLE of carbon di-oxide absorption in aqueous MEA at deep removal conditions
No full textThe increasing attention for deep removal (99 %+) of carbon di-oxide (CO2) from flue gases, requires confirmation of absorption kinetics and VLE under the relevant operating conditions. In this work, the CO2 absorption rate and the equilibrium capacity in aqueous solution of 5 M Mono-ethanolamine (MEA) is investigated for the deep-removal regime (2000 ppm to 100 ppm CO2) using a stirred-cell contactor. The absorption flux is measured over a temperature range of 20–45 °C with solvent loadings from 0 to 0.36 mol CO2/mol MEA, while the equilibrium capacity is evaluated between 30–60 °C, at solvent loadings ranging from 0.05 to 0.35 mol CO2/mol MEA. For the accurate measurement of absorption kinetics under these conditions, the gas-phase mass transfer coefficient (kg) was characterized using various solvent systems. The kinetic and VLE data presented in this work covers the experimental conditions not studied before, still very relevant to the deep CO2 removal operating conditions. The absorption kinetics are reported as the absorption flux per unit driving force (referred to as specific absorption flux). The specific absorption flux allows an independent assessment of absorption kinetics (as a function of solvent loading and temperature), unaffected by possible errors introduced by the solubility and diffusivity data. The specific absorption flux is described using an Arrhenius type equation. The application of specific absorption flux for calculating the absorber column height in Aspen Plus is illustrated with a typical Natural gas combined cycle (NGCC) case.</p
Sepsis as a complex syndrome:Are combined biomarkers the future of diagnosis and prognosis? Clinical perspective
No full textSepsis remains a major cause of mortality worldwide, driven by a dysregulated host response to infection that leads to life-threatening organ dysfunction. Despite advances in evidence-based medicine, early diagnosis and risk stratification remain significant challenges due to the complex, multifaceted nature of sepsis and substantial interindividual variability in clinical presentation. Current approaches relying on single biomarkers cannot provide comprehensive insights into disease progression, limiting their clinical utility in guiding timely and effective interventions. Given the limitations of current single biomarkers in capturing the complexity of sepsis, there is an urgent need for improved diagnostic approaches. While the discovery of novel biomarkers remains important, combining existing biomarkers may offer a pragmatic and effective strategy to improve diagnostic accuracy by leveraging the strengths of each to compensate for the limitations of other. In this clinical perspective, we highlight the potential of such combined biomarker strategies to enhance diagnostic accuracy, support identification of the infection source, and improve prognostic assessment across the clinical course and into long-term outcomes. We provide examples of key biomarkers and their synergistic potential, emphasizing the need for advanced analytical methods such as machine learning and multi-omics integration to enhance predictive accuracy. Shifting toward multi-component biomarker panels represents a critical step toward a more precise, personalized approach to sepsis management to reduce sepsis-related morbidity and mortality. We advocate for further research and validation efforts to facilitate the clinical implementation of combined biomarker models, ultimately transforming sepsis care.</p
Adoption roadblocks of blockchain technology to achieve the sustainable development goal in perishable food supply chain
No full textEnsuring traceability in the perishable food supply chain (PFSC) is crucial for safeguarding consumer rights, food quality, and safety. Blockchain technology (BT), with its decentralized and immutable attributes, offers significant potential to enhance this traceability. However, its widespread adoption faces considerable roadblocks due to industry regulations and operational obstacles. This study aims to identify and analyze these roadblocks for BT adoption in the food industry to support strategic decision-making. Through a comprehensive literature review and expert discussions, 14 key roadblocks to blockchain adoption were identified, and a Grey DEMATEL integrated ANP methodology was applied. Findings reveal that the three most significant roadblocks to BT-based traceability adoption are a ‘data security concern: lack of technological maturity and acceptance’ (prominence value 4.219), ‘threat to data privacy’ (4.035), and ‘lack of digital infrastructure’ (3.971). Addressing these top three roadblocks in order of importance is crucial for accelerating BT adoption. This study provides theoretical contributions to methodological technologies and offers practical insights for professionals to overcome these roadblocks, thereby enhancing food security and safety within global PFSCs through robust, end-to-end encrypted traceable systems. For the successful implementation of blockchain technology strong rule and regulation against data security, creating centre or excellence and standardisation is advisable.</p
Sensitivity Analysis of Distributed PV-fed AC Distribution Network Supported with Distributed Energy Storage Systems
No full textWith the severe climatic changes it became essential to reduce greenhouse gas emissions. This can be achieved by substituting fossil fuel-depending energy sources with green renewable energy sources. Despite having lot of merits, the extensive installation of renewable energy at the low voltage grid has led to instability in the grid voltage due to dynamic changes in the generation-consumption relationship. Therefore, dynamic solutions became needed to intervene and restore the grid voltage stability. This paper shows the importance of using distributed energy storage units in the low-voltage grid to restore its voltage stability.</p
Atmospheric rivers catalyze snowmelt and contribute to chains of landslides
No full textAtmospheric rivers (ARs) significantly impact hydrometeorological conditions by transporting large amounts of heat and water vapor, often resulting in extreme weather events and geohazards such as landslides. While the role of ARs in producing extreme rainfall and related landslides is well established, their influence on landslides through temperature-driven snowmelt remains poorly understood. Here, we examine this mechanism using 330 recorded landslides from February to April 2022 across the North Anatolian Mountains (Türkiye). Our results demonstrate that ARs significantly contributed to snowmelt (up to 250 mm per event), stimulated by abrupt temperature increases (up to +6 °C) and rain-on-snow conditions, with rainfall and snowfall reaching up to 100 mm and 40 mm, respectively; all differences were statistically significant (p < 0.01) when comparing AR and non-AR days. These processes shifted landslide activity to higher elevations and steeper slopes over time, with median values rising from 330 m to 549 m and 16° to 21°, respectively. The results highlight the compound effect of ARs on landslide initiation and suggest that warming-driven snowmelt can substantially contribute to slope destabilization. This study provides a framework for understanding AR-related landslide hazards in other midlatitude mountain regions, including the Pacific Rim, the Andes, High Mountain Asia, and the Alps. As climate change is projected to amplify the frequency, intensity, and spatial extent of ARs, the risk of AR-induced geohazards is therefore likely to intensify further in such mountainous regions.</p
EEG based predictions of good outcome after cardiac arrest improve with sevoflurane sedation, as compared with propofol
No full textPurpose: We investigated the prognostic value of the early electroencephalogram (EEG) in comatose patients after cardiac arrest, sedated with sevoflurane, as compared to those sedated with propofol. Methods: This retrospective cohort study included all resuscitated patients aged ≥18 after cardiac arrest (CA) admitted to the intensive care unit of two large Dutch teaching hospitals. In one hospital all CA patients were sedated with sevoflurane only and cooled to 36 °C, while in the other hospital the patients were sedated with propofol and cooled to 33 °C. EEG patterns at 12 and 24 h after CA were analyzed visually and quantitatively and classified as favorable, unfavorable or other EEG patterns. Quantitative parameters including background continuity index, burst-suppression amplitude ratio, and alpha-delta ratio were compared between groups of sedation. Outcome at 6 months was defined as good (Cerebral Performance Category 1 or 2) or poor (Cerebral Performance Category 3, 4 or 5). Results: We included 412 patients of whom 51 sevoflurane-sedated and 361 propofol-sedated. Predicting good outcome at 12 h after CA, we found a higher sensitivity for those sedated with sevoflurane (0.89, 95 %-CI 0.62–1.00), as compared with those sedated with propofol (0.42, 95 %-CI 0.34–0.50), without significant loss of specificity (0.71, 95 %-CI 0.44–0.91 and 0.88, 95 %-CI 0.81–0.92 respectively). No significant differences in sensitivity and specificity were found between sedation with sevoflurane and propofol for predicting good outcome using the EEG at 24 h after CA. For prediction of poor outcome at 12 and 24 h after CA, no significant differences were found between patients sedated with sevoflurane and propofol regarding sensitivity and specificity. Conclusion: In this non-randomised two-centre cohort study, sevoflurane sedation was associated with less frequent discontinuous EEG 12 h after cardiac arrest, enabling earlier and reliable prediction of good outcome. Prediction of poor outcome was reliable with both sevoflurane and propofol. However, potential bias due to differences in temperature management cannot be excluded.</p