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Dynamic Stiffness Method for Free Vibration of Beams and Frameworks Using Higher-Order Shear Deformation Theory
Full text linkThe dynamic stiffness method for free vibration of beams and frameworks is developed using a higher-order shear deformation theory. Starting with the displacement field, the potential and kinetic energies of the beam in flexural vibration are first formulated. Then, Hamilton's principle is applied to derive the governing differential equations and the associated natural boundary conditions. Next, the differential equations are solved to obtain the expressions for flexural displacement, bending rotation, and the first derivative of the flexural displacement. The expressions for the shear force, bending moment, and the higher-order moment are obtained from the natural boundary conditions resulting from the Hamiltonian formulation. Finally, the force vector comprising the amplitudes of the shear force, bending moment, and the higher-order moment is related to the amplitudes of the displacement vector comprising the flexural displacement, bending rotation, and the first derivative of the flexural displacement through the frequency-dependent dynamic stiffness matrix. The dynamic stiffness matrix for axial motion that is uncoupled from the flexural motion is now implemented in the dynamic stiffness matrix in flexural motion to analyze individual beams and frameworks for their free vibration characteristics by applying the Wittrick–Williams algorithm. Illustrative examples are given, and significant conclusions are drawn
Agentic systems in radiology: Principles, opportunities, privacy risks, regulation, and sustainability concerns
Full text linkThe rapid rise of transformer-based large language models (LLMs) has introduced new opportunities for automation and decision support in radiology, particularly in applications such as report generation, protocol optimization, and structured interpretation. Despite their impressive performance in producing contextually coherent text, conventional LLMs remain limited by their inability to interact autonomously with external systems, retrieve data, or execute code, restricting their role in real-world clinical and research workflows. To address these limitations, agentic systems have emerged as a new paradigm. By embedding LLMs within frameworks that enable reasoning, planning, and action, agentic systems extend LLM capabilities to dynamic interaction with users, tools, and data sources. This review provides a comprehensive overview of the foundations, architectures, and operational mechanisms of agentic systems, focusing on their applications in medical imaging and radiology. It summarizes key developments in the literature, including recent multi-agent frameworks for automated radiomics pipelines, and discusses the potential benefits of these systems in enhancing the reproducibility, interpretability, and accessibility of AI-driven workflows. The review critically examines current regulatory considerations, ethical implications, and sustainability challenges to highlight essential gaps that must be addressed for the safe and responsible clinical integration of these systems
Does a new MRI on-call service improve the timely imaging for suspected cauda equina syndrome?
No full textIntroduction
Cauda equina syndrome (CES) develops due to compression of cauda equina nerve roots and requires urgent diagnosis, preferably using MRI. This will allow timely intervention to prevent irreversible neurological problems. On-call services can potentially reduce time taken to diagnose CES outside of standard operational hours. Most acute hospitals in the UK do not have on-call provisions for CES. This study therefore assessed whether introducing a short period of on-call service at an acute hospital significantly reduced the time in diagnosing CES. This can form the decision for future operational changes with possible replication in similar settings.
Methods
The study was retrospective, comparing MRI exam time between the 12 months prior to the introduction of the on-call service and the 12 months post-introduction. One hundred sixteen patients with suspected CES during each timepoint were randomly sampled and data were analysed using Mann Whitney U, Kruskal-Wallis and chi-squared tests.
Results
Average MRI examination time (from request to report) was reduced by 0.7 h in the post-on-call timepoint compared to the pre-on-call timepoint, but this was not statistically significant (U = 6558.0, p = 0.739). However, for patients referred during the on-call period (19.30 to 22.00), examination time was reduced by 14.2 h (over 70 %) in the post on-call timepoint compared to the corresponding period in the pre on-call timepoint. Also, grouping data by referral periods, there was a statistically significant difference between the two timepoints (H = 74.5, d. f = 5, p < 0.001). All the requests received during the on-call hours of the post on-call timepoint were completed within 24 h which was above the 95 % target while only 85 % completion was achieved in the corresponding period of the pre on-call timepoint. However, this difference was not statistically significant (χ2 (5) =8.4, p = 0.137)
Conclusion
This study demonstrated that though the short period of on-call reduced the overall MRI examination time for CES slightly, the reduction was not statistically significant
Generalised oedema monitoring utilising a NIR hyperspectral camera in critically ill neonates: A feasibility study
Full text linkGeneralised oedema is common in neonatal intensive care units (NICUs), particularly in preterm and low-birth-weight infants. Characterised by tissue swelling from excess water accumulation, it can reflect systematic illness such as congestive heart failure, hepatic cirrhosis, nephrotic syndrome, sepsis, and acute kidney injury. Current clinical assessment methods, including formulas based on weight and fluid input/output and visual skin observation, lack accuracy and sensitivity, especially in critically ill infants. Techniques such as bioimpedance and ultrasound have been explored but are unsuitable for neonates and do not provide direct water content measurements. Spectroscopy, a non-invasive optical method, offers a promising solution by measuring tissue water content through light interactions in the Near Infrared (NIR) spectrum. This study investigates oedema in neonates using an NIR hyperspectral system in the NICU. Data was collected from 20 neonates, both with and without oedema over the course of three consecutive days. Spectral analysis revealed significant differences, notably at water absorption peaks around 1200 nm (p = 0.012). A Partial Least Squares Discriminant Analysis (PLS-DA) model effectively differentiated between oedematous and non-oedematous infants using spectral and standard clinical features, achieving 85.56 % recall and 100 % precision in testing. These findings suggest NIR spectroscopy combined with PLS-DA offers a reliable, non-contact method for early oedema detection in neonates, potentially enhancing monitoring and outcomes in the NICU
Joining Decision-Making, Moral Thinking, and Collective Action: Grand Challenges as a Phenomenology of Deliberation
No full textThis conceptual article argues that the mutual relevance of grand challenges and organization and management studies is best approached phenomenologically. Rather than constituting objects to be observed and theorized, grand challenges structure researchers’ attention and contribute to their interpretations of situated deliberative processes and systems through which collective action is coordinated. From this perspective, grand challenges demand that organization and management researchers innovate in their understanding of such processes and systems and ensure that newly generated knowledge is redirected towards management and policymaking. The article integrates the Carnegie School theory of organization with French pragmatic sociology’s theory of justification, or economies of worth, to develop a phenomenological model of deliberation. This model highlights deliberation’s articulated, evaluative, contestable, and trans-institutional character, and its grounding in the cognitive capacities and social embedding of actors and observers—regardless of the scale, scope, or stratification of underlying coordination problems. Building on this framework, the article advocates that grand-challenge researchers adopt the standpoint of entrepreneurial observers: actors who envision new deliberative processes capable of integrating disjointed systems in situ, thereby extending coordination and redirecting collective action in view of long-range socioeconomic and scientific commitments
Developmental Trajectories of Reading Ability in Adolescents with Intellectual Disabilities
Full text linkIndividuals with ID often struggle with decoding and reading comprehension, and some studies indicate that these students do not progress beyond the early stages of decoding development. The aim of this study was to investigate the developmental trajectories of reading abilities in relation to mental age in a sample of 136 adolescents with mild, non-specific ID. Decoding and reading comprehension, together with their predictors of phonological awareness, RAN, phonological working memory, and vocabulary, were fitted against mental age. The results showed that, after 105 months, there was an unexpected plateau in the development of decoding, phonological awareness, rapid automatised naming (RAN), and phonological working memory in this sample, while reading comprehension and vocabulary continued to show growth in relation to mental age. The implications of these different trajectories are discussed in relation to developmental models of disability, and possible reasons for the plateau in decoding are suggested
Design Method for Transverse Load Resistance of Web-Posts in Cellular Beams
Full text linkThe effect of transverse loads on the buckling resistance of the web-post between circular openings is assessed by finite element (FE) models that are calibrated against the results of a test on a cellular beam with transverse loads applied above two adjacent openings. The test failure load of the web-post was 250 kN of which 96 kN is attributed to web-post buckling and the rest to the transfer of load by local bending of the web-flange T-sections. The FE modelling showed that buckling of the web-post subject to transverse load can be predicted using the method of BS EN 1993-1-5 with a buckling coefficient that is a function of the edge spacing of the openings to the beam web depth. The FE results considered a range of geometric parameters and two steel strengths. It was found that the stresses across the web-post are non-uniform and a formula for the effective web-post width is presented. With this modification, the ratio of the predicted web-post buckling resistance to the isolated web-post buckling load obtained from the FEA was in the range of 0.80 to 0.99. The load transfer by local bending of the top Tee was determined from the elastic bending resistance of the Tee and the ratio to the failure load from the FEA was conservatively in the range of 0.62 to 0.77. Based on this study, a design formula for the web-post buckling resistance of cellular beams to transverse loads is derived
Material-Efficient 2D Skeletal Structure Design Using Convolutional Neural Networks
No full textStructural optimization plays a critical role in achieving efficient material utilization and maximizing structural performance. However, conventional approaches often suffer from high computational costs due to their iterative nature. Incorporating code-compliant design criteria enhances structural integrity and practical applicability but further escalates computational demands. This study presents a convolutional neural network-based framework augmented with a member section classification algorithm, achieving over a sixfold improvement in computational efficiency for generating code-compliant skeletal structures. The proposed workflow generates robust, ready-to-assemble frame designs based on user-defined parameters such as boundary conditions, initial domain geometry, and loading scenarios. Unlike most existing approaches that focus solely on material layout, this study introduces a method capable of jointly determining both structural topology and member dimensions, which is essential for creating assembly-ready designs. The generative convolutional neural models achieve over 91% accuracy in generating optimal frame images. However, by integrating a strategic geometric feature identification algorithm, geometric features are identified with near-perfect accuracy, effectively overcoming the inherent limitations of generator-induced losses. The effectiveness of the approach is demonstrated through multiple case studies, validating the accuracy of predicted member sections under combined axial, bending, and shear loading, in accordance with established design codes. By incorporating a robust geometric feature extraction mechanism, the framework reliably produces designs that pass critical integrity checks and can be applied across multiple engineering domains
Cohomology of Lie coalgebras
Full text linkA Koszul duality-type correspondence between coderived categories of conilpotent differential graded Lie coalgebras and their Chevalley–Eilenberg differential graded algebras is established. This gives an interpretation of Lie coalgebra cohomology as a certain kind of derived functor. A similar correspondence is proved for coderived categories of commutative cofibrant differential graded algebras and their Harrison differential graded Lie coalgebras
How malicious AI swarms can threaten democracy
Full text linkAdvances in artificial intelligence (AI) offer the prospect of manipulating beliefs and behaviors on a population-wide level (1). Large language models (LLMs) and autonomous agents (2) let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility (3) and inexpensively create falsehoods that are rated as more human-like than those written by humans (3, 4). Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multiagent architectures (2), these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, we prioritize interventions at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance
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