Based on probability schemes describing the structure of the coordination sphere of a symmetrically independent structural unit in a crystal, a quantitative criterion for an anti-liquid in presented as a function of coordination number. The obtained result can be applied to problems of classifying crystal structures, including subperiodic ones, in chemical and mineralogical crystallography.
Vietnam ranks among the top countries in terms of both internet traffic and online toxicity. As a result, implementing embedding models for recommendation and content control duties in applications is crucial. However, a lack of large-scale test datasets, both in volume and task diversity, makes it tricky for scientists to effectively evaluate AI models before deploying them in real-world, large-scale projects. To solve this important problem, we introduce a Vietnamese benchmark, VN-MTEB for embedding models, which we created by translating a large number of English samples from the Massive Text Embedding Benchmark using our new automated framework. We leverage the strengths of large language models (LLMs) and cutting-edge embedding models to conduct translation and filtering processes to retain high-quality samples, guaranteeing a natural flow of language and semantic fidelity while preserving named entity recognition (NER) and code snippets. Our comprehensive benchmark consists of 41 datasets from six tasks specifically designed for Vietnamese text embeddings. In our analysis, we find that bigger and more complex models using Rotary Positional Embedding outperform those using Absolute Positional Embedding in embedding tasks. Datasets are available at HuggingFace: https://huggingface.co/collections/GreenNode/vn-mteb-68871433f0f7573b8e1a6686
Background Effective doctor-patient communication is crucial for patient health outcomes, emotional well-being, and overall satisfaction. In undergraduate medical education, such skills are often taught through simulated patient (SP) interactions. With the acceleration of digital learning and teaching (DLT) during the COVID-19 pandemic, new flexible opportunities have emerged. However, questions remain about the quality of the communication experience, particularly the perceived social presence in online settings. Methods A randomized between-subjects trial was conducted with 140 fourth-year medical students attending a mandatory communication course at the University of Bern, Switzerland. All participants completed identical preparatory learning materials before being randomly assigned to either online or face-to-face communication training with SPs. Both groups engaged in motivational-interviewing scenarios. Students’ and SPs perceptions of social presence were measured using the newly developed SoPr-scale. Communication performance was assessed by trained raters using the Behaviour Change Counselling Index (BECCI) for MI and the Berlin Global Rating (BGR) scale for general communication quality. SPs and students also rated their acceptance of the training modality. Results Both students and SPs in face-to-face condition reported significantly higher social presence compared with the online condition. Communication performance, however, did not differ significantly between groups. Notably, 99% of students and all SPs preferred face-to-face interactions. Conclusions Although online training produced communication performance equivalent to face-to-face training, perceived social presence and preference strongly favored in-person delivery. These findings underscore the persistent challenge of fostering interpersonal connection in DLT environments despite technological progress. While online communication training can achieve comparable performance outcomes, enhancing social presence remains essential for (e.g.) sustaining engagement and satisfaction in medical DLT environments. Practice implications Blended learning designs combining online preparation with onsite practice, and targeted efforts to enhance social presence, are recommended to optimize engagement and learning effectiveness.
Vaginal deliveries are frequently associated with perineal trauma, including severe tearing in some cases. Understanding of pelvic floor muscle damage and perineal tearing during childbirth is of great clinical relevance. However, the knowledge of these complex phenomena is incomplete. The objective of the present study is to explore the multifactorial view of pelvic floor muscle damage and perineal tearing during childbirth. Using nonlinear finite element modeling coupled to statistical surrogate modeling, we modeled fetal descent with imposed displacement and used active maternal for muscle contraction to estimate the pelvic floor muscle damage and perineal tearing indicators under different influencing factors such as fetal head deformability and biometry, as well as constitutive behaviors. The obtained results show that fetal head deformability reduces stress and strain concentrations in the pelvic floor muscles (PFM) and perineal region, while increasing fetal head size leads to heightened internal tissue responses. Linear regression analysis demonstrated strong model performance (R² = 0.782–0.981) and statistically predictive relationships between fetal biometric parameters, soft tissue constitutive behaviors, and associated mechanical responses. By integrating advanced finite element modeling with statistical modeling and regression, this work provides new quantitative insights into the biomechanical factors, highlighting tissue deformation patterns and indicating potential risk of tissue damage in highly strained areas due to localized mechanical stress. This approach offers a predictive and non-invasive strategy for assessing maternal tissue vulnerability during childbirth.
This study develops and validates a capability-driven framework linking Industry 4.0 (I4.0), Circular Economy (CE) practices, and Sustainable Performance (SP) in manufacturing sector of an emerging economy. Drawing on the Resource-Based View (RBV) and Dynamic Capabilities View (DCV), the study theorizes I4.0 as a meta-capability that operationalizes CE principles through digital transformation and resource reconfiguration. It also introduces Operational Performance (SP-O) as a fourth and capability-based dimension of the traditional Triple Bottom Line, thereby transforming sustainability from a static outcome into an evolving organizational capability. Data collected from 328 manufacturing firms were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that I4.0 and CE are mutually reinforcing and jointly enhance SP through the mediating role of SP-O. Furthermore, Ethical Leadership (EL) strengthens the I4.0–SP link but weakens the CE–SP link, while Green Knowledge Sharing (GKS) shows no significant moderating effect. These findings advance sustainability theory by proposing a Dynamic Capability of Sustainability (DCS) perspective, in which technological, operational, and behavioral mechanisms interact to create adaptive and context-sensitive sustainability pathways. The study contributes to theory by redefining how sustainability is conceptualized, assessed, and executed, and to practice by offering a holistic roadmap for aligning digital transformation, circular strategies, and leadership in pursuit of sustainable competitiveness.
