Search Results (126)

As organizations continue to embrace digital transformation, the need for robust cybersecurity strategies has never been more critical. This paper explores the Zero Trust Architecture (ZTA) as a contemporary cybersecurity framework that addresses the challenges posed by increasingly interconnected systems. Zero Trust (ZT) operates under the principle of “never trust, always verify,” ensuring that every access request is thoroughly authenticated, regardless of the requester’s location within or outside the network. However, implementing ZT is a challenging task, requiring an adequate roadmap to prioritize the different initiatives in agreement with company culture, exposure and cyber posture. We apply multi-criteria decision analysis (MCDA) to evaluate the relative importance of various components within a ZT framework, using the Incomplete Analytic Hierarchy Process (IAHP). Expert opinions from professionals in cybersecurity and IT governance were gathered through structured questionnaires, leading to a prioritized ranking of the eight key ZT pillars, as defined by the Cybersecurity and Infrastructure Security Agency (CISA), Washington, DC, USA, along with a prioritization of the sub-elements within each pillar. The study provides actionable insights into the implementation of ZTA, helping organizations prioritize security efforts to mitigate risks effectively and build a resilient digital infrastructure. The evaluation results were used to create a prioritized framework, integrated into the ZEUS platform, developed with Teleconsys S.p.A., to enable detailed assessments of a firm’s cyber partner regarding ZT and identify improvement areas. The paper concludes by offering recommendations for future research and practical guidance for organizations transitioning to a ZT model. Full article

Background: Right ventricular (RV) dysfunction is associated with poor clinical outcomes in critically ill sepsis patients, but its pathophysiology and predictors are incompletely characterized. We aimed to investigate the predictors of RV dysfunction and its outcomes in sepsis patients admitted to the intensive care unit (ICU). Methods: This is a single-center retrospective cohort study of adult patients admitted to the ICU for sepsis who had echocardiography within 72 h of diagnosis. Patients with acute coronary syndrome, acute decompensated heart failure, or significant valvular dysfunction were excluded. RV dysfunction was defined as the presence of RV dilation, hypokinesis, or both. Demographics and clinical outcomes were obtained from electronic medical records. Results: A total of 361 patients were included in our study—47 with and 314 without RV dysfunction. The mean age of the population was 66.8 years and 54.6% were females. Compared to those without RV dysfunction, patients with RV dysfunction were more likely to require mechanical ventilation (63.8% vs. 43.9%, p = 0.01) and vasopressor support (61.7% vs. 36.6%, p < 0.01). On multivariate logistic regression analysis, increasing age (OR 1.03, 95% C.I. 1.00–1.06), a history of HIV infection (OR 5.88, 95% C.I. 1.57–22.11) and atrial fibrillation (OR 4.34, 95% C.I. 1.83–10.29), and presence of LV systolic dysfunction (OR 14.40, 95% C.I. 5.63–36.84) were independently associated with RV dysfunction. Patients with RV dysfunction had significantly worse 30-day survival (Log-Rank p = 0.023). On multivariate Cox regression analysis, older age (HR 1.02, 95% C.I. 1.00–1.04) and peak lactate (HR 1.16, 95% C.I. 1.11–1.21) were independent predictors of 30-day mortality. Conclusions: Among other findings, our data suggests a possible association between a history of HIV infection and RV dysfunction in critically ill sepsis patients, and this should be investigated further in future studies. Patients with evidence of RV dysfunction had poorer survival in this population; however this was not an independent predictor of mortality in the multivariate analysis. A larger cohort with a longer follow-up period may provide further insights. Full article

Background and Objectives: The Naples prognostic score (NPS), which incorporates inflammatory and nutritional indicators, is increasingly used as a prognostic score for various malignancies. Nonetheless, few studies have specifically evaluated the NPS as a prognostic factor for cervical cancer. This study aimed to assess the value of NPS at diagnosis as a predictor of cancer progression. Materials and Methods: This study included patients diagnosed with cervical cancer at Korea University Anam Hospital from January 2019 to December 2023. Patients with incomplete data or those who were lost to follow-up were excluded. The NPS was calculated based on laboratory results at the time of diagnosis, categorizing patients into the low-NPS group (NPS 0–1) and high-NPS group (NPS ≥ 2). Survival analysis was performed using the Kaplan–Meier method and log-rank test. Univariate and multivariate Cox proportional hazards models were used to identify independent prognostic factors. Results: Out of 178 patients, 98 and 80 were categorized into the low-NPS and high-NPS groups, respectively. Kaplan–Meier survival analysis showed that the high-NPS group had significantly lower disease-free survival (DFS) (p < 0.001) and overall survival (OS) (p = 0.02) rates than the low-NPS group. Multivariate Cox regression analysis identified the NPS as an independent prognostic factor for DFS (adjusted hazard ratio, 1.98; p = 0.017), but not for OS. Conclusions: This study demonstrated that the NPS measured at diagnosis may serve as a useful independent prognostic factor for cancer progression in patients with cervical cancer. Full article

Petroleum oily sludge (OLS), a hazardous by-product of the petroleum industry, and high-alkali lignite (HAL), an underutilized low-rank coal, pose significant challenges to sustainable waste management and resource efficiency. This study systematically investigated the combustion behavior, reaction pathways, and gaseous-pollutant-release mechanisms across varying blend ratios, utilizing integrated thermogravimetric-mass spectrometry analysis (TG-MS), interaction analysis, and kinetic modeling. The key findings reveal that co-combustion significantly enhances the combustion performance compared to individual fuels. This is evidenced by reduced ignition and burnout temperatures, as well as an improved comprehensive combustion index. Notably, an interaction analysis revealed coexisting synergistic and antagonistic effects, with the synergistic effect peaking at a blending ratio of 50% OLS due to the complementary properties of the fuels. The activation energy was found to be at its minimum value of 32.5 kJ/mol at this ratio, indicating lower reaction barriers. Regarding gas emissions, co-combustion at a 50% OLS blending ratio reduces incomplete combustion products while increasing CO₂, indicating a more complete reaction. Crucially, sulfur-containing pollutants (SO₂, H₂S) are suppressed, whereas nitrogen-containing emissions (NH₃, NO₂) increase but remain controllable. This study provides novel insights into the synergistic mechanisms between OLS and HAL during co-combustion, offering foundational insights for the optimization of OLS-HAL combustion systems toward efficient energy recovery and sustainable industrial waste management. Full article

Background: Gastric signet-ring cell carcinoma (GSRCC) is an aggressive gastric cancer subtype with limited evidence supporting the role of neoadjuvant chemotherapy (NAC). This study evaluated the impact of NAC on overall survival (OS) in a large, population-based cohort. Methods: We analyzed data from the SEER database (2011–2018), identifying patients aged 20–80 years with primary gastric tumors (C16.0–C16.9) and signet-ring cell histology who underwent curative-intent gastrectomy. Patients with metastatic disease, non-curative surgery, clinical Stage I, incomplete staging, or unknown survival were excluded. OS was assessed using Kaplan–Meier analysis and multivariable Cox regression. Subgroup analyses evaluated the interaction of NAC with tumor location and clinical stage. Results: A total of 978 patients met inclusion criteria; 436 (44.6%) received NAC. The 3- and 5-year OS rates were 43.9% and 38.3%, respectively. NAC was not associated with improved OS compared to surgery alone (5-year OS: 39.7% vs. 37.2%; log-rank p = 0.34) and was not an independent prognostic factor in multivariable analysis (p = 0.651). Independent predictors of worse OS included larger tumor size, advanced stage, positive nodal status, and Black race (all p < 0.05). Subgroup analysis indicated a survival benefit from NAC in patients with mid or distal gastric tumors (p < 0.001 for interaction). Conclusions: In this SEER-based analysis, NAC did not improve OS in the overall GSRCC population. However, selected subgroups may derive benefit, supporting a personalized approach to neoadjuvant therapy in GSRCC. Full article

Background/Objectives: Knowledge Graphs (KGs) are often incomplete, which can significantly impact the performance of downstream applications. Manual completion of KGs is time-consuming and costly, emphasizing the importance of developing automated methods for KGC. Link prediction serves as a fundamental task in this domain. The semantic correlation among entity features plays a crucial role in determining the effectiveness of link-prediction models. Notably, the human brain can often infer information using a limited set of salient features. Methods: Inspired by this cognitive principle, this paper proposes a lightweight Bi-level routing attention mechanism specifically designed for link-prediction tasks. This proposed module explores a theoretically grounded and lightweight structural design aimed at enhancing the semantic recognition capability of language models without altering their core parameters. The proposed module enhances the model’s ability to attend to feature regions with high semantic relevance. With only a marginal increase of approximately one million parameters, the mechanism effectively captures the most semantically informative features. Result: It replaces the original feature-extraction module within the KGML framework and is evaluated on the publicly available WN18RR and FB15K-237 dataset. Conclusions: Experimental results demonstrate consistent improvements in standard evaluation metrics, including Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hits@10, thereby confirming the effectiveness of the proposed approach. Full article

Background: Patients with ST-elevation myocardial infarction (STEMI) and multivessel coronary artery disease (MVD) who undergo complete revascularization (CR) have a more favorable prognosis than those who receive incomplete revascularization (IR), as evidenced by recent randomized controlled trials. Despite the absence of a survival benefit associated with CR in these trials, positive outcomes were ascribed to combined endpoints, such as repeat revascularization, myocardial infarction, or ischemia-driven rehospitalization. In light of the significant burden that rehospitalization from STEMI imposes on healthcare systems, we examined the long-term effects of CR on ischemia-driven rehospitalization and cardiovascular (CV) mortality in STEMI patients with MVD. Methods: In our retrospective study, we included patients with STEMI and MVD who underwent successful primary percutaneous coronary intervention (PCI) at the University Medical Centre Ljubljana between 1 January 2009, and 11 April 2011. The combined endpoint was ischemia-driven rehospitalization and CV mortality, with a minimum follow-up period of six years. Results: We included 235 participants who underwent CR (N = 70) or IR (N = 165) at index hospitalization, with a median follow-up time of 7 years (interquartile range 6.0–8.2). The primary endpoint was significantly higher in the IR group than in the CR group (47.3% vs. 32.9%, log-rank p = 0.025), driven by CV mortality (23.6% vs. 12.9%, log-rank p = 0.047), as there was no difference in ischemia-driven rehospitalization rate (log-rank p = 0.206). Ischemia-driven rehospitalization did not influence CV mortality in the CR group (p = 0.49), while it significantly impacted CV mortality in the IR group (p = 0.03). After adjusting for confounders, there were no differences in CV mortality between CR and IR groups (p = 0.622). Predictors of the combined endpoint included age (p = 0.014), diabetes (p = 0.006), chronic kidney disease (CKD) (p = 0.001), cardiogenic shock at presentation (p = 0.003), chronic total occlusion (CTO) (p = 0.046), and ischemia-driven rehospitalization (p = 0.0001). Significant risk factors for the combined endpoint were cardiogenic shock at presentation (p < 0.001), stage 4 kidney failure (p = 0.001), age over 70 years (p = 0.004), female gender (p = 0.008), and residual SYNTAX I score > 5.5 (p = 0.017). Conclusions: Patients with STEMI and MVD who underwent CR had a lower combined endpoint of ischemia-driven rehospitalizations and CV mortality than IR patients, but after adjustments for confounders, the true determinants of the combined endpoint and risk factors for the combined endpoint were independent of the revascularization method. Full article

Modern organizations collect increasing volumes of data to drive decision-making, often stored in centralized repositories such as data lakes, which consist of diverse structured and unstructured datasets. However, these repositories often suffer from issues such as incomplete, inconsistent, and low-quality data, which hinder data-driven insights. Existing methods for data imputation, including statistical techniques and machine learning approaches, often rely heavily on large amounts of labeled data and domain-specific knowledge, making them labor-intensive and limited in handling semantic heterogeneity across data formats. To address these challenges, this study proposes a novel retrieval-augmented generation (RAG) framework for data imputation that effectively combines the strengths of retrieval mechanisms and large language models (LLMs). This approach constructs semantic-based indexes for heterogeneous data, employs a recall and re-ranking strategy to enhance retrieval relevance, and proposes a method to reduce inference cost while ensuring imputation quality. Extensive experiments on real-world datasets demonstrate that the proposed framework significantly outperforms machine learning and deep learning approaches. Full article

In knowledge graph construction, missing facts often lead to incomplete structures, thereby limiting the performance of downstream applications. Although recent knowledge graph completion (KGC) methods based on representation learning have achieved notable progress, they still suffer from two fundamental limitations, namely the lack of structured reasoning capabilities and the inability to assess the confidence of their predictions, which often results in unreliable outputs. We propose the GLR framework, which integrates Graph Chain-of-Thought (Graph-CoT) reasoning, LoRA fine-tuning, and the P(True)-based confidence evaluation mechanism. In the KGC task, this approach effectively enhances the reasoning ability and prediction reliability of large language models (LLMs). Specifically, Graph-CoT introduces local subgraph structures to guide LLMs in performing graph-constrained, step-wise reasoning, improving their ability to model multi-hop relational patterns. Complementing this, LoRA-based fine-tuning enables efficient adaptation of LLMs to the KGC scenario with minimal computational overhead, further enhancing the model’s capability for graph-structured reasoning. Moreover, the P(True) mechanism quantifies the reliability of candidate entities, improving the robustness of ranking and the controllability of outputs, thereby enhancing the credibility and interpretability of model predictions in knowledge reasoning tasks. We conducted systematic experiments on the standard KGC datasets FB15K-237, WN18RR, and UMLS, which demonstrate the effectiveness and robustness of the GLR framework. Notably, GLR achieves a Mean Reciprocal Rank (MRR) of 0.507 on FB15K-237, marking a 6.8% improvement over the best recent instruction-tuned method, DIFT combined with CoLE (MRR = 0.439). GLR also maintains significant performance advantages on WN18RR and UMLS, verifying its effectiveness in enhancing both the structured reasoning capabilities and the prediction reliability of LLMs for KGC tasks. These results indicate that GLR offers a unified and scalable solution to enhance structure-aware reasoning and output reliability of LLMs in KGC. Full article

Questionnaire data plays a key role in social research, especially when evaluating public attitudes using Likert-type scales. Yet, traditional analyses often merge some ordinal categories and exclude responses such as Don’t Know, No Answer, or Refused—risking the loss of valuable information. This study introduces BS-TOSIE (Belief Structure-Based TOPSIS for Survey Item Evaluation), a novel method that preserves and integrates all response types, including ambiguous ones. By combining the Belief Structure framework with the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, BS-TOSIE offers a structured approach to ranking and evaluating individual survey items measured on an ordinal scale, even in the presence of missing or incomplete data. Response distributions are transformed into a belief structure vector, enabling comparison against ideal and anti-ideal benchmarks. We demonstrate this approach using data from the Quality of Life in European Cities survey to assess perceptions of local governance in European cities. This study analyzes changes in citizen satisfaction with local public administration across five key dimensions—timeliness, procedural clarity, fairness of fees, digital accessibility, and perceived corruption—in 83 European cities between 2019 and 2023. The findings reveal persistent regional disparities, with Northern and Western European cities consistently outperforming those in Southern and Eastern Europe, although some cities in Central Europe show signs of improvement. Zurich consistently received high satisfaction scores, while other cities, such as Rome and Palermo, showed lower scores. Unlike traditional methods, our approach preserves the full spectrum of responses, yielding more nuanced and interpretable insights. The results show that BS-TOSIE enhances both the clarity and depth of survey analysis, making a methodological contribution to the evaluation of ordinal data and offering empirical insights into public perceptions of local city administration. Full article

Machining centers are complex systems that consist of multiple subsystems. When maintaining these subsystems, considering opportunistic maintenance can prevent frequent shutdowns during the machining process and reduce costs. This paper proposes an opportunistic maintenance strategy for machining centers. Firstly, the reliability of the machining center subsystem was modeled, which serves as the basis for determining when to repair a subsystem. In this process, an improved average rank method was employed, which considers the time correlation of subsystem failures and can achieve better model-fitting results. In the opportunistic maintenance strategy, imperfect maintenance is considered. Additionally, the strategy includes direct maintenance costs, downtime costs, failure risk costs, and penalty costs for incomplete utilization of subsystems. The opportunistic maintenance threshold helps determine whether other subsystems need to be repaired during this maintenance opportunity. The optimization objective is to minimize the total cost within the specified operating time. By modeling the reliability of subsystems using the failure data collected from five machining centers, the opportunistic maintenance strategy can reduce downtime by 10 times, preventive downtime by 29%, and cost by 7%. The results indicate that for machining centers or other complex systems, the opportunistic maintenance strategy mentioned in this article can lead to good results. Full article

This study examines the applicability of the well-established PageRank algorithm for ranking teams and predicting outcomes in incomplete, single-elimination high school baseball tournaments. Using match data from Taiwan’s CTBC Black Panther Cup National High School Baseball Tournament spanning from 2013 to 2023, this research investigates whether PageRank can produce valid, stable, and predictive rankings under structural constraints and limited data environments. Three empirical evaluations were conducted. First, a comparative analysis between PageRank rankings and official results demonstrated high ordinal consistency, with Kendall’s tau values exceeding 0.70 in most seasons. Second, PageRank rankings were assessed for temporal robustness, demonstrating stable performance across seasons and under varying data inputs. Third, a series of n-step-ahead simulations were implemented to test the predictive validity of PageRank. The results indicate that incorporating historical data substantially improves forecasting accuracy, achieving up to 92.9% when data from up to four previous seasons are included. Overall, the findings support PageRank as a consistent and interpretable ranking method that is well-suited for grassroots sports. Its ability to incorporate indirect competition and opponent strength makes it effective in settings with sparse or unbalanced schedules. This study provides methodological insights and practical implications for ranking and evaluation in school-level sports. Full article

Background: Nursing students’ readiness for clinical practicums is critical to nursing education. Concerns about students’ preparedness for clinical courses have emerged due to increased student-to-educator ratios and limited hands-on practice time. Moreover, feedback from clinical instructors reveals that many student nurses lack the necessary knowledge and skills for patient care, thereby raising questions about their readiness for clinical practicum. Purpose: This study investigates undergraduate nursing students’ readiness for clinical practicum in the UAE by examining their perspectives, the variation in clinical study plans across different contexts, and the evidence gathered from clinical evaluations. Methodology: A case study design was adopted, utilizing semi-structured interviews with 13 nursing students from a UAE nursing college. Additionally, two types of document analysis were conducted. First, 11 nursing curricula from high-ranking universities were analyzed to compare whether students received adequate laboratory courses for their clinical practicum. Second, 217 clinical evaluation reports from third- and fourth-year nursing students across 4 campuses of the UAE nursing college were reviewed. Findings: The study identified two key themes from the interviews: incomplete readiness for clinical practicum and the factors contributing to this incomplete readiness. Document analysis revealed that, unlike many American and Australian institutions, most universities lacked co-requisite laboratory courses. Clinical evaluation reports highlighted that some students, particularly in their fourth year, were inadequately prepared for clinical practice due to deficiencies in both clinical skills and theoretical knowledge. Conclusions: The findings indicate that many nursing students felt only partially prepared for their practicum, negatively impacting their confidence and competency. Moreover, adopting the American and Australian approach of pairing practicum courses with laboratory courses may better prepare students for clinical practicum. Recommendations for future research have been outlined. Full article

Biological studies on symmetry can be expanded to consider red (longer wavelengths) and blue (shorter wavelengths) shifts as antisymmetries (opposite-pattern symmetries), which may arise from similar underlying causes (invariant process symmetries). In this context, classic shift asymmetries of redder plumage in response to higher dietary carotenoids appear conceptually incomplete, as potential blue-shifted counterparts were not considered. A latent symmetric response is highlighted by recent evidence showing that the maximum absorbance bands of various colorful plumage pigments are red-shifted in birds with ultraviolet-sensitive (UVS) color vision but blue-shifted in those with violet-sensitive (VS) color vision. Blue-shifted responses to increased dietary carotenoid contents may also be underestimated, as relevant studies have focused on species-rich but uniformly UVS Passerida passerines. This study explored the relationship between pattern–process symmetries and diets of VS Piciformes–Coraciiformes by gauging the responses of their plumage reflectance to a modified diet index (Diet_c), where the overall rank carotenoid contents of food items were weight-averaged by three levels of importance in a species’ diet. In the case of both sexes, the main long-wavelength reflectance band for the three carotenoid-based pigment classes defined the same graded series of blue shifts in response to higher Diet_c. Yellow showed a strong absolute (negative slope) blue shift, orange showed a weaker absolute blue shift, and red exhibited only a blue shift (flat, non-significant slope) relative to absolute red shifts (positive slope). The secondary shorter-wavelength reflectance band was also unresponsive to Diet_c in the VS Piciformes–Coraciiformes (relative blue shift) compared with earlier evidence for it decreasing (absolute red shift) at higher Diet_c in UVS species. Results for the intervening minimum reflectance (maximum absorbance) band were intermediate between those for the other reflectance bands. No pigment class monopolized lower or higher Diet_c, but red was less variable overall. Phylogenetic independence, sexually similar responses, and specimen preservation reinforced characterizations. A review of avian perceptual studies suggested that VS models discriminate yellows and oranges extremely well, consistent with the importance of the corresponding carotenoids as Diet_c indicators. Both UVS and VS species appear to produce putatively more costly and possibly beneficial carotenoid metabolites and/or concentrations in response to higher Diet_c, supporting underlying invariant processes in relation to carotenoid limitations and honest signaling despite opposite plumage shifts and their different chemical bases. In symmetry parlance, pigment classes (red) or wavebands (short) that lack responses to Diet_c suggest broken pattern and process symmetry. The biology of VS Piciformes–Coraciiformes may favor such exceptions owing to selection for visual resemblance and tuning specializations, although universal constraints on physical and chemical properties of (particularly red) carotenoids may favor certain functional tendencies. Thus, symmetry principles organize carotenoid diversity into a simplified and predictive framework linked to color vision. Full article

The rapid expansion of online grocery shopping has intensified the need for strategically located distribution hubs that ensure efficient and sustainable operations. Traditional location models emphasize economic and logistical factors but often neglect energy efficiency and environmental sustainability. This paper proposes a hybrid decision-making model that integrates the analytic hierarchy process (AHP) and the spherical fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS) to address the complexities of delivery hub location selection. The AHP is used to determine the relative importance of key decision-making criteria, including cost, accessibility, infrastructure, competition, and sustainability, while SFTOPSIS ranks the candidate locations based on their proximity to the ideal solution. Spherical fuzzy sets allow for a more nuanced treatment of uncertainty, improving decision-making accuracy in dynamic environments. The results demonstrate that this hybrid approach effectively manages incomplete and uncertain data, delivering a robust ranking of candidate locations. By incorporating sustainability as a key factor, this study provides a structured and adaptive framework for businesses to optimize logistics operations in the post-pandemic landscape. The proposed methodology not only enhances decision-making in location selection but contributes to the development of more resilient and sustainable supply chain strategies. Full article