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A Socio-Technical Framework for Semantically Interoperable Digital Humanities Information Systems
No full text© 2025 Rui LiuThis research focuses on semantic interoperability in Digital Humanities (after DH). Interoperability is a frequently mentioned concept in the field of information systems, traditionally understood as the seamless connection and data transfer between different information systems to improve efficiency and collaboration. Interoperability has already been well-studied in the information systems at enterprise and public institutions, such as healthcare information systems, e-government systems, and digital libraries. However, as a young field of scholarship, interoperability problems are beginning to hinder the development of DH. DH is a research field that using computing methods or digital tools to accelerate humanities studies. DH projects keep independently progressing based on diverse DH research centres globally. The heterogeneity of data and the varying understandings of data architecture in the construction of DH collections exacerbate issues of inconsistency and hinder global connection. Semantic interoperability becomes a crucial way to achieve knowledge sharing and data sustainability for the DH collections built from multiple DH projects. Multidisciplinary collaboration is a key feature of this research field, which further complicates achieving semantic interoperability in DH. This challenge goes beyond being a technical issue between systems and databases; it also involves organizational problems across different DH research centres. This research invests significant effort in evaluating existing problems and challenges in DH projects to assessing the semantic interoperability of DH information systems in detail from a socio-technical perspective and proposing suggestions from DH practitioners
Analysis of more than 400,000 women provides case-control evidence for BRCA1 and BRCA2 variant classification
No full textClinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyze germline sequencing data of BRCA1 and BRCA2 from 96,691 female breast cancer cases and 302,116 controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observe 11,207 BRCA1 and BRCA2 variants, with 6909 being coding, covering 23.4% of BRCA1 and BRCA2 VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control likelihood ratio (ccLR) evidence is highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.3% specificity for BRCA1 and 93.3% sensitivity and 86.6% specificity for BRCA2. This approach provides case-control evidence for 787 unclassified variants; these include 579 with strong or moderate benign evidence and 10 with strong pathogenic evidence for which ccLR evidence is sufficient to alter clinical classification
Extending the time window for tenecteplase by effective reperfusion of penumbral tissue in patients with large vessel occlusion: Rationale and design of a multicenter, prospective, randomized, open-label, blinded-endpoint, controlled phase 3 trial
No full textRATIONALE: The benefit of tenecteplase in the treatment of large vessel occlusion (LVO) patients presenting within 24 h of symptom onset remains unclear. AIM: This study aimed to assess the effectiveness and safety of tenecteplase, compared to standard of care, in patients presenting within the first 24 h of symptom onset with an LVO and target mismatch on perfusion computed tomography (CT). METHODS AND DESIGN: The "Extending the time window for Tenecteplase by Effective Reperfusion of peNumbrAL tissue in patients with Large Vessel Occlusion" (ETERNAL-LVO) trial is a prospective, randomized, open-label, blinded-endpoint, phase 3, parallel-group, superiority trial with covariate-adjusted 1:1 randomization, and adaptive sample size re-estimation. Patients with an anterior circulation LVO stroke, who present within 24 h of stroke onset or last known well with a target mismatch on computed tomography perfusion (CTP) or magnetic resonance imaging (MRI), will be randomized to tenecteplase (0.25 mg/kg) or standard of care (alteplase 0.90 mg/kg or conservative management at clinician discretion) prior to undergoing endovascular therapy. STUDY OUTCOMES: The primary outcome is the proportion of patients with a modified Rankin Scale (mRS) of 0-1 (no disability) or return to baseline mRS at 3 months. Secondary and safety outcomes include the proportion of patients with an mRS of 0-2 at 3 months, an ordinal analysis of the mRS at 3 months, the proportion of patients with symptomatic intracerebral hemorrhage (sICH), the proportion of patients with death due to any cause, and the proportion of patients with mRS 5-6 at 3 months (severe disability or death). DISCUSSION: The ETERNAL-LVO trial will build on the current evidence for tenecteplase in the > 4.5-h window. Specifically, this trial will evaluate tenecteplase in a patient population who have access to endovascular therapy but may incur delays to endovascular therapy commencement or require transfer from a primary to a comprehensive stroke center. TRIALS REGISTRATION: ClincialTrials.gov: NCT04454788
Updating statistical practice in ecotoxicology: reflections and recommendations
No full textApproaches to statistical analysis of data from ecotoxicity testing emerged in the 1980s and have grown over the decades, but with only intermittent involvement of statisticians. Consequently, statistical practices in this field have evolved in various directions. Fragmented, inconsistent, and outdated use of statistical methods for ecotoxicology by authorities and jurisdictions have further contributed to a landscape which can be confusing to navigate. A prominent example is the issue of whether no-observed effect concentrations (NOECs) should be banned from or used in regulatory ecotoxicology, which has been debated for more than 30 years (e.g., Laskowski, 1995; van Dam et al., 2012)
Developing a CityGML-based Graph Data Model for Utility Infrastructure in Smart Cities
No full textGraph data models are essential for the development of smart cities, where interconnected systems such as utility networks, transportation, and IoT devices must function cohesively. The complexity of smart city infrastructure necessitates 3D data structures capable of managing intricate relationships, dynamic environments, and high connectivity across diverse systems. Graph data models are particularly suited for this purpose, as they offer an integrated 3D digital representation of urban complexity and interconnectivity. This study employs the Labelled Property Graph (LPG) framework to develop a 3D graph data model based on the Utility Network Application Domain Extension (ADE) of the CityGML standard. The proposed approach enhances utility network data management, enabling advanced analyses such as connectivity assessment and pathfinding. The developed graph data model is evaluated in terms of constraint preservation, information integrity, and connection realism. Results demonstrate that the model accurately represents real-world utility network structures while preventing data loss and duplication
Securing Cloud-Based Internet of Things: Challenges and Mitigations
No full textThe Internet of Things (IoT) has seen remarkable advancements in recent years, leading to a paradigm shift in the digital landscape. However, these technological strides have introduced new challenges, particularly in cybersecurity. IoT devices, inherently connected to the internet, are susceptible to various forms of attacks. Moreover, IoT services often handle sensitive user data, which could be exploited by malicious actors or unauthorized service providers. As IoT ecosystems expand, the convergence of traditional and cloud-based systems presents unique security threats in the absence of uniform regulations. Cloud-based IoT systems, enabled by Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) models, offer flexibility and scalability but also pose additional security risks. The intricate interaction between these systems and traditional IoT devices demands comprehensive strategies to protect data integrity and user privacy. This paper highlights the pressing security concerns associated with the widespread adoption of IoT devices and services. We propose viable solutions to bridge the existing security gaps while anticipating and preparing for future challenges. This paper provides a detailed survey of the key security challenges that IoT services are currently facing. We also suggest proactive strategies to mitigate these risks, thereby strengthening the overall security of IoT devices and services
Instructional Approaches for Learner Engagement in Large Classes
No full textLarge class sizes are frequently necessitated by financial, resource, and logistical constraints. Teaching large classes presents pedagogical challenges impacting instructional quality and student learning, with student engagement emerging as a critical issue. This chapter explores instructional approaches to mitigate potential negative effects on engagement and learning, based on a rapid literature review. Key approaches identified include instructional strategies, active learning, collaborative learning, technology integration, flipped classrooms, peer instruction, learning assistants, and frequent formative assessments. Results highlight active learning and technology-enhanced strategies as strongly correlated with improved student engagement and academic outcomes, particularly when effectively integrated. The practical implementation challenges are resource demands, and increased instructor preparation. The chapter concludes with recommendations for prioritising structured collaborative activities, interactive technologies, and targeted instructor training to enhance engagement
How Knowledgeable is ChatGPT 4o? Assessing the Pedagogical Content Knowledge of a Generative Artificial Intelligence Tool
No full textGenerative Artificial Intelligence (GenAI) chatbots provide teachers with opportunities to enhance their expertise by exploring topics relevant to their practice. However, studies have raised concerns about ChatGPT’s mathematical accuracy. This pilot study evaluated ChatGPT’s Pedagogical Content Knowledge (PCK) and Content Knowledge (CK), revealing that while it generates varied and detailed responses indicating competence in PCK and CK, some responses are flawed or incorrect. While ChatGPT 4o can serve as a valuable professional learning resource in certain areas of mathematics education, traditional teaching methods and human insight remain essential
A Statistical Characterization of Dynamic Brain Functional Connectivity
No full textThis study examined the statistical underpinnings of dynamic functional connectivity in mental disorders, using resting-state fMRI signals. Notably, there has been an absence of research demonstrating the non-stationarity of the empirical probability distribution of functional connectivity. This gap has prompted debate on the existence of dynamic functional connectivity, leading skeptics to question its relevance and the reliability of research findings. Our aim was to fill this gap by conducting a comprehensive empirical distribution analysis of functional connectivity, using Pearson's correlation as a measure. We conducted our analysis on a set of preprocessed resting-state fMRI samples obtained from 186 subjects selected from the UCLA Consortium for Neuropsychiatric Phenomics dataset. Departing from conventional methods that aggregated signals over voxels within a region of interest, our approach leveraged individual voxel signals. Specifically, our approach offered a precise characterization of the empirical probability distribution of resting-state fMRI signals by evaluating the temporal variations and non-stationarity in dynamic functional connectivity, as measured by Pearson's correlation. Our study investigated functional connectivity patterns across 49 regions of interest, comparing healthy control subjects with patients diagnosed with ADHD, bipolar disorder, and schizophrenia. Our analysis revealed that (1) the empirical distribution of the correlation coefficient exhibited non-stationarity, (2) the beta distribution was an accurate approximation of the exact correlation coefficient distribution, and (3) the empirical distribution of means derived from the fitted beta distributions, unraveled distinctive dynamic functional connectivity patterns with potential as biomarkers associated with different mental disorders. A key contribution of our study was the presentation of the first comprehensive empirical distribution analysis of dynamic functional connectivity, thus providing compelling evidence for its existence. Overall, our study presented an innovative statistical approach that advances our understanding of the dynamic nature of functional connectivity patterns derived from resting-state fMRI. Our examination of the empirical distribution of dynamic functional connectivity provided solid evidence supporting its existence. The distinctive dynamic functional connectivity patterns we identified across various mental disorders hold promise as potential biomarkers for further development
Numerical study of a cylindrical release gravity current in a stratified ambient
No full text© 2025 Wai Kit LamDirect numerical simulations (DNSs) of three-dimensional cylindrical release gravity currents in a linearly stratified ambient are presented. The simulations cover a range of stratification strengths 0<S<0.8 (where S=(\rho_b^*-\rho_0^*)/(
\rho_c^*-\rho_0^*), \rho_b^*, \rho_0^* and \rho_c^* are the dimensional density at the bottom of the domain, top of the domain and the heavy fluid respectively) at two different Reynolds numbers. A comparison between the stratified and unstratified cases illustrates the influence of stratification strength on the dynamics of cylindrical gravity currents. Specifically, the front velocity in the slumping phase decreases with increasing stratification strength whereas the duration of the slumping phase increases with increments of S. The Froude number calculated in this phase shows a good agreement with models proposed by Ungarish & Huppert (2002) and Ungarish (2006), originally developed for planar gravity currents in a stratified ambient. In the inertial phase, the front velocity across cases with different stratification strengths adheres to a power-law scaling with an exponent of -1/2. Higher Reynolds numbers led to more frequent lobe splitting and merging, with lobe size diminishing as stratification strength increased. Strong interactions among inner vortex rings occurred during the slumping phase, leading to the early formation of hairpin vortices in weakly stratified cases, while strongly stratified cases exhibited delayed vortex formation and reduced turbuelence.
A comparative analysis is conducted between the stratified cases and the unstratified case to investigate the impact of ambient stratification strength on the mixing behaviour of cylindrical gravity currents. The energy conversion processes are analysed using the mechanical framework proposed by Winters et al. (1995). The energy budget is formulated by considering gravitational available potential energy (E_a), and the evolution of potential energy due to reversible stirring and irreversible diapycnal mixing in the system. The findings reveal a decrease in available potential energy and kinetic energy (K) with increasing stratification strength, indicating lower energy exchange for gravity currents propagating in the stratified environment. Instantaneous and cumulative mixing efficiencies during the slumping phase indicate that Kelvin-Helmholtz billows play an important role in stirring the heavy fluid and causing irreversible mixing with the ambient fluid.
The impact of ambient stratification on the dynamics and energy exchange of cylindrical gravity currents were also investigated. As stratification strength increases, the shape of the gravity current becomes smoother and the Kelvin-Helmholtz billows behind the head become less pronounced during the slumping phase. These billows play a crucial role in stirring the heavy fluid and causing irreversible mixing with the ambient fluid leading to a reduction in both available potential energy and kinetic energy, particularly in the strongly stratified cases (S=0.8). The available potential energy density, \mathscr{E}_a, was used to create spatial maps of local contributions to E_a, providing insights into the interaction between the Kelvin-Helmholtz billows and the head, as well as the formation of lobe and cleft structures at the front of the gravity current. This interaction demonstrates that increased stratification reduces turbulence and the available potential energy, due to the smaller density difference between the heavy fluid and the ambient fluid at the bottom wall, as well as the suppression of K-H billows by internal gravity waves in the subcritical regime. Notably, the predominant stirring process during the slumping phase is primarily attributed to the larger Kelvin-Helmholtz billows behind the head, rather than the smaller vortices within the head.
Fully resolved three-dimensional (3-D) simulations of planar gravity currents are conducted to investigate the influence of imposed spanwise perturbations on flow evolution and mixing at two Reynolds numbers (Re=3,450 and 10,000). The initial perturbations consist of sinusoidal waves with a varying wave number, with simulations spanning 0 0) exhibit a more rapid breakdown of spanwise coherence compared to the unperturbed case (k_y = 0), although the resulting structures retain spatial periodicity and remain relatively ordered. This earlier disruption leads to greater front propagation distances during the self-similar phase compared to the unperturbed case. Notably, imposed perturbations exhibit minimal influence on the flow transition; all cases follow the slumping velocity reported in the literature, with the onset of the inertial phase occurring at comparable times across different k_y values at both Re. The increased propagation is accompanied by reduced mixing efficiency due to the premature disruption of coherent Kelvin–Helmholtz (K–H) billows, which play a key role in maintaining multi-scale mixing. At high-Re, the influence of initial spanwise perturbations diminishes, as three-dimensional turbulence induces a more chaotic, fine-scale breakdown of spanwise coherence across all k_y cases, overriding the effects of the initial perturbations. Consequently, the dominant stirring mechanism shifts from K–H billows to vortices within the current head. Nevertheless, the unperturbed case maintains comparatively higher mixing efficiency at both low- and high-Re. This is attributed to the persistence of recognisable K–H billow structures, which, despite undergoing chaotic breakdown at high-Re, still contribute to effective stirring by stretching and folding the density interface. These results highlight the dual role of K–H billows: they promote efficient mixing, yet the enhanced mixing reduces the density difference between the current and the ambient fluid, weakening buoyancy and slowing front propagation despite stronger stirring. These findings are supported by consistent trends in streamwise density distribution and `local' energy exchange analyses