Search Results (6,170)

Campus climate research has long been a focus of higher education scholarship; however, studies show that inequalities and a pervasive sense of not belonging continue to negatively affect students. This paper presents the results of a campus-wide survey conducted at a Minority-Serving Institution (MSI), with a sample of 820 undergraduate, master’s, Ph.D., and non-degree students. The authors explore students’ experiences on campus in relation to their identities as well as students’ perceptions of campus climate. Specifically, the paper examines students’ intergroup relations and how these influence their sense of belonging. The survey instrument developed in the frame of this project also included questions designed to assess opportunities students have to develop key values, attitudes, skills, knowledge, and critical understanding related to intercultural and democratic competences necessary for life and work in multicultural societies. This study identifies the areas students perceive as important for development, highlighting which values, attitudes, skills, knowledge, and critical understanding they have had the opportunity to cultivate during their time at the university and those they would like to develop further. The authors hope these findings will inform efforts to strengthen institutional support for more inclusive practices on culturally diverse university campuses and provide evidence-based guidance for designing effective pedagogical interventions. Full article

Off-chain payments in the Internet of Things (IoT) enhance the efficiency and scalability of blockchain transactions. However, existing privacy mechanisms face challenges, such as the disclosure of payment channels and transaction traceability. Additionally, the rise of quantum computing threatens traditional public key cryptography, making the development of post quantum secure methods for privacy protection essential. This paper proposes a post-quantum ring signature scheme based on hash functions that can be applied to off-chain payments, enhancing both anonymity and linkability. The scheme is designed to resist quantum attacks through the use of hash-based signatures and to prevent double spending via its linkable properties. Furthermore, the paper introduces an improved Hash Time-Locked Contract (HTLC) that incorporates a Signature of Knowledge (SOK) to conceal the payment path and strengthen privacy protection. Security analysis and experimental evaluations demonstrate that the system strikes a favorable balance between privacy, computational efficiency, and security. Notably, the efficiency benefits of basic signature verification are particularly evident, offering new insights into privacy protection for post-quantum secure blockchain. Full article

This study aimed to develop a Causal Loop Diagram (CLD) to visualise how urban environment factors impact dementia and cognitive decline, and potential causal mechanisms. In Group Model Building workshops with 12 researchers, a CLD was created to identify factors contributing to cognitive decline, and the dynamic interrelationships between these factors. The factors were classified in nine main themes: urban design, social environment, travel behaviours, urban design by-products, lifestyle, mental health conditions, disease/physiology, brain physiology, and cognitive decline outcomes. Five selected feedback loops illustrated some dynamics in the system. The workshops helped develop a shared language and understanding of different perspectives from an interdisciplinary team. The CLD creation was part of a comprehensive modelling approach based on experts’ knowledge which informed other research outputs such as an evidence gap map and an umbrella review, helped the identification of environmental variables for future studies and analyses, and helped to identify future possible systems-based interventions to prevent cognitive decline. The study highlights the utility of CLDs and Group Model Building workshops in interdisciplinary research projects investigating complex systems. Full article

To address the limitations of existing knowledge graph-based recommendation algorithms, including insufficient utilization of semantic information and inadequate modeling of user behavior motivations, we propose SKGRec, a novel recommendation model that integrates knowledge graph and semantic features. The model constructs a semantic interaction graph (USIG) of user behaviors and employs a self-attention mechanism and a ranked optimization loss function to mine user interactions in fine-grained semantic associations. A relationship-aware aggregation module is designed to dynamically integrate higher-order relational features in the knowledge graph through the attention scoring function. In addition, a multi-hop relational path inference mechanism is introduced to capture long-distance dependencies to improve the depth of user interest modeling. Experiments on the Amazon-Book and Last-FM datasets show that SKGRec significantly outperforms several state-of-the-art recommendation algorithms on the Recall@20 and NDCG@20 metrics. Comparison experiments validate the effectiveness of semantic analysis of user behavior and multi-hop path inference, while cold-start experiments further confirm the robustness of the model in sparse-data scenarios. This study provides a new optimization approach for knowledge graph and semantic-driven recommendation systems, enabling more accurate capture of user preferences and alleviating the problem of noise interference. Full article

Federated learning promotes the development of cross-domain intelligent applications under the premise of protecting data privacy, but there are still problems of sensitive parameter information leakage of multi-party data temporal alignment and resource scheduling process, and traditional symmetric encryption schemes suffer from low efficiency and poor security. To this end, in this paper, based on the modified NTRU-type multi-key fully homomorphic encryption scheme, an asymmetric algorithm, a secure computation scheme of multi-party least common multiple and greatest common divisor without full set under the semi-honest model is proposed. Participants strictly follow the established process. Nevertheless, considering that malicious participants may engage in poisoning attacks such as tampering with or uploading incorrect data to disrupt the protocol process and cause incorrect results, a scheme against malicious spoofing is further proposed, which resists malicious spoofing behaviors and not all malicious attacks, to verify the correctness of input parameters or data through hash functions and zero-knowledge proof, ensuring it can run safely and stably. Experimental results show that our semi-honest model scheme improves the efficiency by 39.5% and 45.6% compared to similar schemes under different parameter conditions, and it is able to efficiently process small and medium-sized data in real time under high bandwidth; although there is an average time increase of 1.39 s, the anti-malicious spoofing scheme takes into account both security and efficiency, achieving the design expectations. Full article

Background/Objectives: Epilepsy affects approximately 50 million people worldwide, with antiepileptic drugs (AEDs) remaining the cornerstone of treatment. Due to their narrow therapeutic windows, AEDs are ideal candidates for therapeutic drug monitoring (TDM). Oral fluid is increasingly considered a viable alternative to blood and urine, as it reflects the free (active) concentration of many AEDs. Its non-invasive collection, which does not require trained personnel, makes it particularly suitable for TDM in paediatric and geriatric populations. However, as samples are often stored for extended periods before analysis, analyte stability becomes a critical concern. This study aimed to evaluate the stability of four commonly used AEDs in dried saliva spot (DSS) samples. Methods: Phenobarbital, phenytoin, carbamazepine, and carbamazepine-10,11-epoxide were analysed in oral fluid samples collected via spitting and stored as DSSs. Quantification was performed using high-performance liquid chromatography with diode array detection (HPLC-DAD). Design of experiments tools were used to assess the effects of preservatives, storage temperatures, light exposure, and storage durations on analyte stability. Results: Optimal conditions were refrigeration in the dark, with a low concentration of ascorbic acid as preservative. Samples at 10 µg/mL remained stable for 14 days longer than those without preservative or reported in previous studies. Unexpectedly, at 0.5 µg/mL, analytes in samples without preservative showed greater stability. Conclusions: To our knowledge, this is the first study combining DSS and HPLC-DAD to assess the stability of these AEDs in oral fluid, providing valuable insights for non-invasive TDM strategies and supporting the feasibility of saliva-based monitoring in clinical settings. Full article

To enhance the accuracy and reliability of large language models (LLMs) in regulatory question-answering tasks, this study addresses the complexity and domain-specificity of regulatory texts by designing a retrieval-augmented generation (RAG) testing framework. It proposes a dimensionality reduction-based semantic similarity measurement method and a retrieval optimization approach leveraging information reasoning. Through the construction of the technical route of the intelligent knowledge management system, the semantic understanding capabilities of multiple mainstream embedding models in the text matching of financial regulations are systematically evaluated. The workflow encompasses data processing, knowledge base construction, embedding model selection, vectorization, recall parameter analysis, and retrieval performance benchmarking. Furthermore, the study innovatively introduces a multidimensional scaling (MDS) based semantic similarity measurement method and a question-reasoning processing technique. Compared to traditional cosine similarity (CS) metrics, these methods significantly improved recall accuracy. Experimental results demonstrate that, under the RAG testing framework, the mxbai-embed-large embedding model combined with MDS similarity calculation, Top-k recall, and information reasoning effectively addresses core challenges such as the structuring of regulatory texts and the generalization of domain-specific terminology. This approach provides a reusable technical solution for optimizing semantic matching in vertical-domain RAG systems, particularly for MDSs such as law and finance. Full article

Toponyms are fundamental geographical resources characterized by their spatial attributes, distinct from general nouns. While natural language provides rich toponymic data beyond traditional surveying methods, its qualitative ambiguity and inherent uncertainty challenge systematic extraction. Traditional toponym recognition methods based on part-of-speech tagging only focus on the surface-level features of words, failing to effectively handle complex scenarios such as alias nesting, metonymy ambiguity, and mixed punctuation. This leads to the loss of toponym semantic integrity and deviations in geographic entity recognition. This study proposes a set of Chinese toponym annotation specifications that integrate spatial semantics. By leveraging the XML markup language, it deeply combines the spatial location characteristics of toponyms with linguistic features, and designs fine-grained annotation rules to address the limitations of traditional methods in semantic integrity and geographic entity recognition. On this basis, by integrating multi-source corpora from the Encyclopedia of China: Chinese Geography and People’s Daily, a large-scale Chinese toponym annotation corpus (CHTopo) covering five major categories of toponyms has been constructed. The performance of this annotated corpus was evaluated through toponym recognition, exploring the construction methods of a large-scale, diversified, and high-coverage Chinese toponym annotated corpus from the perspectives of applicability and practicality. CHTopo is conducive to providing foundational support for geographic information extraction, spatial knowledge graphs, and geoparsing research, bridging linguistic and geospatial intelligence. Full article

Background/Objectives: This prospective intervention study examined the learning effect of using 3D-printed phantom heads with cleft lip and palate (CLP) and upper jaw models with CLP and maxillary plates during a lecture for dental students in their fourth year at J. W. Goethe Frankfurt University. The primary aim was to evaluate the impact of 3D-printed models on students’ satisfaction levels along with their understanding and knowledge in dental education. Methods: Six life-sized phantom heads with removable mandibles (three with unilateral and three with bilateral CLP) were designed using ZBrush software (Pixologic Inc., Los Angeles, CA, USA) based on MRI images and printed with an Asiga Pro 4K 3D printer (Asiga, Sydney, Australia). Two groups of students (n = 81) participated in this study: the control (CTR) group (n = 39) attended a standard lecture on cleft lip and palate, while the intervention (INT) group (n = 42) participated in a hands-on seminar with the same theoretical content, supplemented by 3D-printed models. Before and after the session, students completed self-assessment questionnaires and a multiple-choice test to evaluate knowledge improvement. Data analysis was conducted using the chi-square test for individual questions and the Wilcoxon rank test for knowledge gain, with the significance level set at 0.05. Results: The study demonstrated a significant knowledge increase in both groups following the lecture (p < 0.001). Similarly, there were significant differences in students’ self-assessments before and after the session (p < 0.001). The knowledge gain in the INT group regarding the anatomical features of unilateral cleft lip and palate was significantly higher compared to that in the CTR group (p < 0.05). Conclusions: The results of this study demonstrate the measurable added value of using 3D-printed models in dental education, particularly in enhancing students’ understanding of the anatomy of cleft lip and palate. Full article

Reproductive health literacy (RHL) is essential to women’s ability to make informed reproductive health (RH) decisions and is a key determinant of RH outcomes. Resettled refugee women often experience poorer RH outcomes, yet there is limited research on their RHL and its influence on RH decision-making. This scoping review aims to (1) to evaluate existing methods for measuring RHL among resettled refugee women and (2) to characterize the relationship between RHL, RH decision-making, behavior, and outcomes among refugee women residing in high-income countries. A search of peer-reviewed literature published in English found limited direct measurement of RHL. Measurement methods were primarily qualitative or based on unvalidated survey instruments, limiting comparability and generalizability. The current methodologies do not adequately capture RH knowledge or RHL proficiency. A range of additional factors were found to influence RH decision-making and behavior, supporting the need for a means to accurately measure RHL. Further quantitative research is needed to clarify the extent to which RHL and knowledge influence RH behavior and outcomes. The development of a culturally relevant, validated RHL instrument that integrates knowledge and contextual influences would support healthcare providers and public health agents in serving and designing effective interventions for refugee women post-resettlement. Full article

Federated learning (FL), which enables collaborative learning across distributed nodes, confronts a significant heterogeneity challenge, primarily including resource heterogeneity induced by different hardware platforms, and statistical heterogeneity originating from non-IID private data distributions among clients. Neural architecture search (NAS), particularly one-shot NAS, holds great promise for automatically designing optimal personalized models tailored to such heterogeneous scenarios. However, the coexistence of both resource and statistical heterogeneity destabilizes the training of the one-shot supernet, impairs the evaluation of candidate architectures, and ultimately hinders the discovery of optimal personalized models. To address this problem, we propose a heterogeneity-aware personalized federated NAS (HAPFNAS) method. First, we leverage lightweight knowledge models to distill knowledge from clients to server-side supernet, thereby effectively mitigating the effects of heterogeneity and enhancing the training stability. Then, we build random-forest-based personalized performance predictors to enable the efficient evaluation of candidate architectures across clients. Furthermore, we develop a model-heterogeneous FL algorithm called heteroFedAvg to facilitate collaborative model training for the discovered personalized models. Comprehensive experiments on CIFAR-10/100 and Tiny-ImageNet classification datasets demonstrate the effectiveness of our HAPFNAS, compared to state-of-the-art federated NAS methods. Full article

Background: Childhood hearing loss is a public health problem of critical importance associated with speech development, academic achievement, and quality of life. Parents’ awareness and knowledge about risk factors contribute to early detection and timely intervention.  Objective: This study aims to examine Jordanian mothers’ knowledge of childhood hearing loss risk factors and investigate the impact of education level and socioeconomic status (SES) on the accuracy and comprehensiveness of this knowledge with the moderating effect of health literacy. Material and Methods: The approach employed an embedded mixed-methods design with a dominant quantitative strand supported by qualitative data, utilizing quantitative surveys (n = 250), analyzed using structural equation modeling (SEM) in SmartPLS, and qualitative interviews (n = 10), analyzed thematically to expand upon the quantitative findings by exploring barriers to awareness and healthcare-seeking behaviors. Results: The accuracy and comprehensiveness of knowledge of hearing loss risk factors were also positively influenced by maternal knowledge of hearing loss risk factors. Maternal knowledge was significantly associated with both education level and socioeconomic status (SES). Furthermore, maternal knowledge and accuracy were significantly moderated by health literacy, such that mothers with higher health literacy exhibited a stronger relationship between knowledge and accuracy. Qualitative findings revealed that individuals encountered barriers to accessing reliable information and comprehending medical advice and faced financial difficulties due to limited options for healthcare services. Conclusions: These results underscore the need for maternal education programs that address specific issues, provide simplified healthcare communication, and enhance access to pediatric audiology services. Future research should explore longitudinal assessments and intervention-based strategies to enhance mothers’ awareness and detect early childhood hearing loss. Full article

Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting. Full article

Accurate pre-drilling mud weight window (MWW) prediction is crucial for drilling fluid design and wellbore stability in complex geological formations. Traditional physics-based approaches suffer from subjective parameter selection and inadequate handling of multi-mechanism over-pressured formations, while machine learning methods lack physical constraints and interpretability. This study develops a novel physics-guided deep learning framework integrating rock mechanics theory with deep neural networks for enhanced MWW prediction. The framework incorporates three key components: first, a physics-driven layer synthesizing intermediate variables from rock physics calculations to embed domain knowledge while preserving interpretability; second, a geological sequence-matching algorithm enabling precise stratigraphic correlation between offset and target wells, compensating for lateral geological heterogeneity; third, a long short-term memory network capturing sequential drilling characteristics and geological structure continuity. Case study results from 12 wells in northwestern China demonstrate significant improvements over traditional methods: collapse pressure prediction error reduced by 40.96%, pore pressure error decreased by 30.43%, and fracture pressure error diminished by 39.02%. The proposed method successfully captures meter-scale pressure variations undetectable by conventional approaches, providing critical technical support for wellbore design optimization, drilling fluid formulation, and operational safety enhancement in challenging geological environments. Full article

This paper presents the design, development, and pilot deployment of Vitalia, an AR-gamified application targeting the formation of healthy habits in primary education children. Developed within the EU DUSE project, Vitalia integrates physical activity, nutritional education, and immersive storytelling into a gamified framework to promote sustained behavioral change. Grounded in evidence-based behavior change models and co-designed with health, nutrition, and physical activity experts, the system envisions high daily engagement rates and measurable knowledge improvements. The concept positions Vitalia as a scalable model for child-centric, ethically responsible digital health interventions, with the potential to be integrated into school curricula and public health strategies. Full article