Search Results (11,346)

Diversity, equity, and inclusion, most commonly known as DEI, serves as a framework for practices that promote the fair treatment and full participation of all members of a community. Culturally responsive teaching and critical pedagogy are commonly associated with DEI as part of the larger strategy to validate and inspire learners while improving their self-efficacy and using education to challenge oppressive systems. While DEI is becoming increasingly better known in higher education, Historically Black Colleges or Universities (HBCUs) are heralded in the literature as a model for this work. Nevertheless, there is relatively limited empirical research exploring facets of DEI and culturally responsive teaching on HBCU and other minority-serving institutions’ campuses. This paper examines the campus of an HBCU located in the Mid-Atlantic United States, with an institutional commitment to diversity, equity, and inclusion, via a comprehensive DEI climate study that included separate surveys of students and faculty/staff. More specifically, the study explored whether all community members consider the campus inclusive, whether all community members experience a culture of belonging, whether adequate resources and supports exist for all campus members to succeed, whether faculty exhibit culturally responsive teaching practices, and whether the perceptions of faculty and staff differ from those of students. The purpose of the study was to help address the gap in the DEI literature exploring the practices of minority-serving institutions. According to the results, participants found the University to be an inclusive place, expressing strong satisfaction with the campus climate and experience. Further, when the presence of culturally responsive teaching practices was explored, strong evidence was indicated. Possible areas for improvement include greater supports and resources for LGBTQIA+, Indigenous, and disabled community members. Full article

Is there an approach that goes beyond the debate between models that argue that emotional intelligence is a skill and those that maintain this construct is a personality trait? This study aims to validate a theoretical model of competent ‘emocreativity’ and to study the psychometric properties of the instrument that measures the emotional competencies included in this model. The participants were 177 teaching professionals and 370 students enrolled in university teacher training programs, who completed two instruments: the Self-Assessment Questionnaire of Emotional Competencies (CACE) and the TMMS24 using a Google form. The results indicate that the CACE has robust psychometric properties, given that the exploratory factor analysis (EFA) is confirmed by the confirmatory factor analysis, and the model’s components show internal consistency. In addition, its convergent validity has been compared with another Emotional Intelligence questionnaire (TMMS24). The conclusion is that the CACE responds to the factorial structure of the initial theoretical model, organized into four factors: emotional awareness, emotional change, emotional bonding, and emotional creativity, thus offering empirical validation of an instrument that can complement traditional approaches to the study of emotional intelligence. Full article

Background/Objectives: Nutritional status affects postoperative outcomes, but the effect of standardized preoperative nutritional preparation on morbidity in malnourished patients undergoing pancreatoduodenectomy (PD) remains unclear. This study evaluated preoperative nutritional parameters following the standardization of nutritional screening and intervention in patients undergoing PD. The influence of nutritional parameters on postoperative morbidity was also assessed. Methods: This prospective cohort study was conducted from 2019 to 2021 at the Department of Surgery, University Hospital, Olomouc. A total of 133 patients were categorized nutritionally as “high risk” (weight loss or reduced appetite with restricted intake) or “low risk” (no weight or appetite loss). High-risk patients received enteral supplementation of 600 kcal/day. A multivariate logistic regression model was used to evaluate the association between major postoperative complications and risk factors, including sex, age, ASA score, BMI, weight and appetite loss, malignancy, duct diameter, pancreatic texture, serum albumin, prealbumin, MUST, and NRS2002 scores. Results: Eighty patients (60.2%) were “high risk,” and 53 (39.8%) were “low risk.” Major morbidity and 90-day mortality occurred in 24 (18.0%) and 4 (3.0%) patients, respectively. No significant differences were observed between high- and low-risk groups in CD morbidity grade, 90-day mortality, POPF, PPH, DGE, or hospital stay. Major morbidity was associated with prealbumin < 0.2 g/L, duct diameter ≤ 3 mm, soft texture, and male sex, with respective odds ratios of 3.307, 3.288, 4.814, and 2.374. Conclusions: High-risk patients receiving preoperative nutrition had comparable rates of major complications and POPF as low-risk patients. Low serum prealbumin predicts major postoperative complications after PD. Full article

A Universal Numerical Integrator (UNI) is a computational framework that combines a classical numerical integration method, such as Euler, Runge–Kutta, or Adams–Bashforth, with a universal approximator of functions, such as a feed-forward neural network (including MLP, SVM, RBF, among others) or a fuzzy inference system. The Euler-Type Universal Numerical Integrator (E–TUNI) is a particular case of UNI based on the first-order Euler integrator and is designed to model non-linear dynamic systems observed in real-world scenarios accurately. The UNI framework can be organized into three primary methodologies: the NARMAX model (Non-linear AutoRegressive Moving Average with eXogenous input), the mean derivatives approach (which characterizes E–TUNI), and the instantaneous derivatives approach. The E–TUNI methodology relies exclusively on mean derivative functions, distinguishing it from techniques that employ instantaneous derivatives. Although it is based on a first-order scheme, the E–TUNI achieves an accuracy level comparable to that of higher-order integrators. This performance is made possible by the incorporation of a neural network acting as a universal approximator, which significantly reduces the approximation error. This article provides a comprehensive overview of the E–TUNI methodology, focusing on its application to the modeling of non-linear autonomous dynamic systems and its use in predictive control. Several computational experiments are presented to illustrate and validate the effectiveness of the proposed method. Full article

Local variations in mechanical properties are commonly observed in engineering structures, driven by complex manufacturing histories and harsh service environments. The evaluation of mechanical properties accurately constitutes a fundamental requirement for structural integrity assessment. The Instrumented Indentation Technique (IIT) can play a critical role in the in-site testing of local properties. However, it could be often a challenge to correlate indentation characteristics with uniaxial stress–strain relationships. In this study, we investigated quantitatively the connection between the indentation responses of commonly used metals and their plastic properties using the finite element inversion method. Materials typically exhibit plastic deformation mechanisms characterized by either linear or power-law hardening behaviors. Consequently, conventional prediction methods based on a single constitutive model may no longer be universally applicable. Hence, this study developed methods for acquiring mechanical properties suitable for either the power-law and linear hardening model, or combined, respectively, based on analyses of microstructures of materials exhibiting different hardening behaviors. We proposed a novel integrated IIT incorporating microstructures and material-specific constitutive models. Moreover, the inter-dependency between microstructural evolution and hardening behaviors was systematically investigated. The proposed method was validated on representative engineering steels, including austenitic stainless steel, structural steel, and low-alloy steel. The predicted deviations in yield strength and strain hardening exponent are broadly within 10%, with the maximum error at 12%. This study is expected to provide a fundamental framework for the advancement of IIT and structural integrity assessment. Full article

Background/Objectives: Effective clinical handover is a critical component of nursing care, particularly in mental health settings, where the transfer of clinical and behavioral information is essential for both patients’ and health personnel’s safety. Gamification has emerged as a promising strategy to enhance clinical education, yet few interventions have focused specifically on mental health care contexts. This study aimed to evaluate the effectiveness of a serious game designed to teach the SBAR (Situation, Background, Assessment, Recommendation) handover framework to undergraduate nursing students through a psychiatric care unit scenario. Methods: A quasi-experimental pre–post design was employed with a convenience sample of 48 nursing students from a Northern Italian university. Participants completed a test assessing their ability to organize clinical information according to the SBAR model before and after the game intervention. Students’ experience was assessed using the Player Experience Inventory. Results: A statistically significant improvement in SBAR application was observed post-intervention. The majority of students reported a positive experience across PXI domains such as Meaning, Challenge, Progress Feedback, and Enjoyment. Comparisons with a previously validated video-based nursing serious game showed a consistent overall pattern in response trends. Conclusions: The SG was an effective and engaging educational tool for improving structured handover skills in nursing students. Gamification may represent a valuable complement to traditional instruction in nursing education, especially in high-communication clinical areas such as mental health. Further research is needed to assess long-term retention and to explore more immersive formats. Full article

As wind turbines are affected by the harsh marine environment, inspection is crucial for the continuous operation of offshore wind farms. Nowadays, the main method of inspection is manual inspection, which has significant limitations in terms of safety, economy, and labor. With the advancement of technology, unmanned inspection systems have attracted more attention from researchers and the industry. This study proposes a novel framework to enable Unmanned Aerial Vehicles (UAVs) to improve their adaptability in autonomous inspection tasks on offshore wind farms, which includes multi-UAVs, inspection task nodes, and multiple charging stations. The main contributions of this paper are as follows: we propose an improved PSO algorithm to improve the location of charging stations; based on the multi-depot traveling salesman problem, we establish a multi-station UAV cooperative task allocation model with energy constraints, with the inspection time consumption of UAVs as the optimization objective; we also propose the Dynamic elite Double population Genetic Algorithm (DDGA) to aid in the cooperative task allocation of UAVs. The simulation results show that, compared with other algorithms, the proposed framework has higher universality and superiority. This paper provides a specific method for the application of unmanned inspection systems in the inspection of wind turbines in offshore wind farms. Full article

This entry presents a comprehensive overview of the computational study of Old English that surveys the evolution from early digital corpora to recent artificial intelligence applications. Six interconnected domains are examined: textual resources (including the Helsinki Corpus, the Dictionary of Old English Corpus, and the York-Toronto-Helsinki Parsed Corpus), lexicographical resources (analysing approaches from Bosworth–Toller to the Dictionary of Old English), corpus lemmatisation (covering both prose and poetic texts), treebanks (particularly Universal Dependencies frameworks), and artificial intelligence applications. The paper shows that computational methodologies have transformed Old English studies because they facilitate large-scale analyses of morphology, syntax, and semantics previously impossible through traditional philological methods. Recent innovations are highlighted, including the development of lexical databases like Nerthusv5, dependency parsing methods, and the application of transformer models and NLP libraries to historical language processing. In spite of these remarkable advances, problems persist, including limited corpus size, orthographic inconsistency, and methodological difficulties in applying modern computational techniques to historical languages. The conclusion is reached that the future of computational Old English studies lies in the integration of AI capabilities with traditional philological expertise, an approach that enhances traditional scholarship and opens new avenues for understanding Anglo-Saxon language and culture. Full article

In this paper, we propose a deterministic epidemic model for malware propagation in wireless mobile environments, taking into account the presence of hotspots. Conventional epidemic models for wireless mobile environments typically assume that mobile hosts randomly move in a given area. However, in real-world scenarios, hotspots such as restaurants and universities often attract large gatherings of people. Therefore, the assumption of purely random movement is not suitable for modeling these scenarios. To realistically represent the movement patterns of mobile hosts, we incorporate the influence of hotspots into our model. Specifically, we formulate a system of Ordinary Differential Equations (ODEs) that captures the infection dynamics in scenarios where one or more hotspots affect host mobility. Our model introduces non-uniform movement probabilities based on cosine similarity, allowing us to reflect the tendency of mobile hosts to cluster around specific locations. By applying these movement probabilities to the ODEs, our epidemic model provides a more accurate representation of malware spreading behavior in hotspot-affected environments. We investigate how malware propagates through mobile hosts and compare the dynamics between the conventional and our proposed models. Numerical experiments demonstrate that our epidemic model effectively captures malware spreading behavior in mobile scenarios with hotspots. Full article

Background: Autonomic nervous system (ANS) and vascular factors are associated with glaucoma. However, the association between systemic comorbidity burden and ANS and hemodynamic function in patients with glaucoma remains unclear. This study aimed to examine the association between heart rate variability (HRV) and acceleration plethysmography (APG) parameters and the age-adjusted Charlson Comorbidity Index (ACCI) in patients with glaucoma. Methods: A total of 260 subjects (260 eyes), including 186 with primary open-angle glaucoma (PG) and 74 with exfoliation glaucoma (EG), were enrolled at Shimane University Hospital from June 2023 to July 2024. HRV and APG were assessed using a sphygmograph (TAS9 Pulse Analyzer Plus View). HRV parameters included time-domain measures (SDNN, RMSSD, CVRR) and frequency-domain measures (TP, VLF, LF, HF, LF/HF). APG parameters included the a, b, c, d, and e components of the accelerated pulse wave, and the following vascular types: Type A, Type B, and Type C. The association between ACCI and HRV and APG parameters was evaluated using Spearman’s rank correlation and multivariate regression adjusted for sex, body mass index, pulse rate, systolic and diastolic blood pressure, intraocular pressure, medication score, mean deviation, and glaucoma type. Results: By univariate analysis, against ACCI, significant inverse correlations were observed for several parameters: LnLF (R = −0.17, p = 0.0062); LnLF/LnHF (R = −0.24, p = 0.00012); b peak (R = −0.14, p = 0.031); d peak (R = −0.17, p = 0.0072); and e peak (R = −0.15, p = 0.015). Regarding HRV parameters, multivariate linear regression models showed that ACCI was significantly positively associated with RMSSD (coefficient: 2.861; 95% CI: 0.447 to 5.274) and significantly negatively associated with the frequency-domain parameters LnLF (coefficient: −0.127; 95% CI: −0.245 to −0.009) and LnLF/LnHF (coefficient: −0.038; 95% CI: −0.062 to −0.014). In APG parameters, the c peak was significant associated with ACCI (coefficient: −12.6; 95% CI: −22.5 to −2.69). ACCI was significantly associated with Type B (coefficient: 0.305; 95% CI: 0.057 to 0.552). Conclusions: Greater systemic comorbidity burden may be related to impaired ANS regulation and increased vascular stiffness in glaucoma patients. Full article

The hybrid work environment significantly undermines the social well-being of employees in the workplace. Existing research predominantly addresses academics’ well-being challenges without offering practical strategies to counter these issues. This study identifies strategies that higher education institutions must adopt to enhance the social well-being of their academics in hybrid work settings. It employs Demerouti’s Job Demands-Resources (JD-R) model and Baumeister and Leary’s theory of the need to belong as its theoretical framework. Using a cross-sectional qualitative approach, semi-structured interviews were guided by an interview schedule to gather data. The sample comprised 23 academics from three campuses of North-West University, and thematic analysis was utilized to analyse the data. The study revealed that growth strategies, such as training, development, and mentoring, are crucial for fostering a sense of belonging, strengthening work relationships, and helping academics connect in a hybrid work environment. Support strategies like providing peer support, management support, physical resources, effective communication, and improvements in job quality enhance academics’ social well-being in this setting. Relationship strategies, which entail organizing social events and promoting a positive organizational culture, are key to encouraging social well-being in the hybrid work environment. Additionally, reward strategies, such as recognition and direct compensation, are essential for reinforcing a sense of belonging, improving work relationships, and enhancing social connections in a hybrid work environment. Intentional, coach-oriented, sensible, and inclusive leadership is vital. The findings offer valuable insights for higher education institutions to adopt a more comprehensive approach to managing the well-being of academic employees. This highlights the need to focus not only on mental and psychological health but also on social well-being. Full article

Accurate estimation of the State-of-Health (SOH) of lithium-ion batteries is crucial for ensuring the safety and longevity of electric vehicles and energy storage systems. However, conventional LSTM models often fail to capture the nonlinear degradation dynamics and long-term dependencies of battery aging. This study proposes a Fractional-Derivative Enhanced LSTM (F-LSTM), which incorporates fractional parameters α and Δt into the cell state update to model multi-scale memory effects. Experiments conducted on the CALCE LiCoO₂ dataset and the Tongji University NCA dataset demonstrate that, compared with the standard LSTM, the proposed F-LSTM reduces RMSE and MAE by more than 40% while maintaining robust performance across different chemistries, temperatures, and dynamic conditions. These results highlight the potential of integrating fractional calculus with deep learning to achieve accurate SOH prediction and support intelligent battery management. Full article

Cascading link failures continue to imperil power grids, transport networks, and cyber-physical systems, yet the relationship between a network’s robustness at the moment of attack and its subsequent resiliency remains poorly understood. We introduce a dynamic framework in which connectivity-based cascades and distributed self-healing act concurrently within each time-step. Failure is triggered when a node’s active-neighbor ratio falls below a threshold $\phi$; healing activates once the global fraction of inactive nodes exceeds trigger $T$ and is limited by budget $B$. Two real data sets—a 332-node U.S. airport graph and a 1133-node university e-mail graph—serve as testbeds. For each graph we sweep the parameter quartet ($\phi ,\hspace{0.17em}B,\hspace{0.17em}T,attackmode$) and record (i) immediate robustness $R$, (ii) 90% recovery time T₉₀, and (iii) cumulative average damage. Results show that targeted hub removal is up to three times more damaging than random failure, but that prompt healing with $B\ge 0.12$ can halve T₉₀. Scatter-plot analysis reveals a non-monotonic correlation: high-$R$ states recover quickly only when $B$ and $T$ are favorable, whereas low-$R$ states can rebound rapidly under ample budgets. A multiplicative fit $T_{90}\propto B^{-\beta }g\left(T\right)h\left(R\right)$ (with $\beta \approx 1$) captures these interactions. The findings demonstrate that structural hardening alone cannot guarantee fast recovery; resource-aware, early-triggered self-healing is the decisive factor. The proposed model and data-driven insights provide a quantitative basis for designing infrastructure that is both robust to failure and resilient in restoration. Full article

Background/Objectives: Cardiac sarcoidosis (CS) is a granulomatous inflammatory cardiomyopathy with heterogeneous presentations, from palpitations to heart failure and sudden cardiac arrest. Despite advances in imaging and immunosuppressive (IS) therapy, relapse patterns and long-term outcomes remain poorly defined. This study aimed to characterize relapse and identify predictors of relapse and major adverse cardiac events (MACE) in a real-world CS cohort. Methods: This retrospective single-center study included 25 adults diagnosed with CS at Geneva University Hospitals between 2016 and 2024, classified per the 2024 American Heart Association diagnostic criteria. Relapse was defined as clinical, arrhythmic, or imaging deterioration requiring treatment escalation. MACE included cardiovascular hospitalization, device therapy, left ventricular assist device, heart transplant, or death. Statistical methods included Kaplan–Meier analysis with log-rank tests and multivariable Cox regression adjusted for age and sex. Results: Relapse occurred in 13 patients (56%), frequently subclinical (61.5%) and detected incidentally on routine PET-CT during IS tapering. In the multivariate model, predictors of relapse included right ventricular FDG uptake (aHR 13.1; 95% CI 1.3–133.7; p = 0.03) and second-line immunosuppression duration ≤24 months (aHR 20.1; 95% CI 1.1–363.8; p = 0.04). Relapse-free patients were more often maintained on dual or triple IS therapy (71.4% vs. 15.4%; p = 0.02) and low-dose prednisone (<10 mg/day) (57.1% vs. 7.7%; p = 0.03). Conclusions: Relapse is common in CS, often subclinical, and associated with PET-CT findings and premature IS tapering. Maintenance therapy may reduce risk. Multimodal imaging remains critical for disease monitoring, though tracers with higher specificity are needed. Further research should refine relapse definitions and support personalized treatment strategies. Full article

Morphogenetic factors (MTFs) are specialized plant genes and transcription factors that play pivotal roles in embryogenesis and organogenesis. This review focuses on their functions in plant development regulation and their applications in plant biotechnology and modern breeding. Common challenges in transformation and regeneration were discussed, along with successful case studies demonstrating improved regeneration capacity and transgene stability in rice (Oryza sativa), soybean (Glycine max), rapeseed (Brassica napus), tomato (Solanum lycopersicum) and other less common crops and plant model organisms. These improvements were achieved through the utilization of key developmental MTFs such as WUCHEL, BABY BOOM, GRF-GIF, etc. The principles of designing genetic constructs with MTFs are explored, including promoter selection and regulatory elements, as well as their synergistic effects with phytohormones like auxins and cytokinins for optimizing in vitro morphogenesis. Current limitations in MTF expression and strategies to overcome them are analyzed. The article highlights recent advances, including MTFs potential for developing stress-resistant, high-yielding cultivars. Key discussion points include the discovery of novel morphogens, their application to recalcitrant species, and prospects for expanding the range of easily transformable and regenerable crops. Future directions involve developing universal transformation protocols and integrating morphogens with precision genome editing technologies, offering new opportunities for agriculture and global food security. Full article