Search Results (468)

Computational fluid dynamics (CFD)-based simulation of coronary blood flow provides valuable hemodynamic markers, such as pressure gradients, for diagnosing coronary artery disease (CAD). However, CFD is computationally expensive, time-consuming, and difficult to integrate into large-scale clinical workflows. These limitations restrict the availability of labeled hemodynamic data for training AI models and hinder the broad adoption of non-invasive, physiology-based CAD assessment. To address these challenges, we develop an end-to-end pipeline that automates coronary geometry extraction from coronary computed tomography angiography (CCTA), streamlines simulation data generation, and enables efficient learning of coronary blood pressure distributions. The pipeline reduces the manual burden associated with traditional CFD workflows while producing consistent training data. Furthermore, we introduce a diffusion-based regression model. Specifically, the inverted conditional diffusion (ICD) model is designed to predict coronary blood pressure directly from CCTA-derived features, thereby bypassing the need for computationally intensive CFD during inference. The proposed model is trained and validated on two CCTA datasets using the Adam optimizer with a weight decay of $1\times {10}^{-3}$, a learning rate of $1\times {10}^{-5}$, a batch size of 100, and Huber loss. It is then evaluated on a test set of ten simulated coronary hemodynamic cases. Experimental results demonstrate state-of-the-art performance. Compared with Long Short-Term Memory (LSTM), the proposed model improves the $R^2$ score by 19.78%, reduces the root mean squared error (RMSE) by 19.44%, and lowers the normalized root mean squared error (NRMSE) by 18%. Compared with a multilayer perceptron (MLP), it improves the $R^2$ score by 8.38%, reduces RMSE by 4.3%, and reduces NRMSE by 5.4%. This work represents a first step toward a scalable and accessible framework for rapid, non-invasive, CFD-based blood pressure prediction, with the potential to support CAD diagnosis. Full article

Unmanned Aerial Vehicles (UAVs) are complex nonlinear systems characterized by high dimensionality. They are prone to aerodynamic effects, structural dynamics, actuation constraints, and networked interactions, requiring advanced mathematical models and precise control. Their governing equations involve nonlinear rigid-body dynamics coupled with fluid and elasticity models, while modern architectures introduce redundancy that creates constrained mappings between generalized forces and actuator inputs. Coordinated UAV teams add another layer of mathematical structure through graph-based interaction models that determine consensus, formation keeping, and distributed stability. These characteristics give rise to several interconnected challenges. High-fidelity aerodynamic and aeroelastic solvers provide accurate results; however, these are computationally intensive, motivating the development of reduced-order models and data-driven approximations that preserve dominant physical behavior. Methods for quantifying uncertainty support robustness assessments by characterizing the effects of parametric variation and model form error. At the actuation level, control allocation problems rely on constrained linear algebra, convex optimization, and dynamic formulations to ensure feasible and stable realization of command forces and moments. In multi-agent systems, the spectral properties of adjacency and Laplacian matrices govern convergence and cooperative behavior. This article reviews the state of the art in these areas, highlights the mathematical foundations that relate them, and provides a coherent perspective on the methods that enable reliable modeling and control of modern UAV systems. Full article

In recent years, the discovery of new ultra-deepwater reservoirs has significantly increased both the importance and the complexity of offshore oil production. One of the main challenges in qualifying structures to operate under such severe conditions is the fatigue limit state, particularly fatigue induced by ocean waves. Wave-induced fatigue remains, both at the design stage and during the operation of flexible risers, one of the most demanding issues for engineers responsible for ensuring their structural integrity. This study presents a state-of-the-art review of wave-induced fatigue analysis in flexible risers. It includes a brief historical overview of the problem, a summary of the fatigue assessment methodologies traditionally adopted in offshore engineering, a discussion of pioneering contributions to stress calculation, and an overview of the main research trends currently being pursued. These trends reflect emerging challenges related to fatigue life prediction, including the high computational cost of time-domain analyses, the presence of elevated contaminant levels in transported fluids, the development of new materials to reduce loads or enhance resistance to aggressive environments, and the assessment of remaining service life in the presence of damaged or corroded tensile wires. The potential use of monitored data to reduce uncertainties in numerical modelling is also addressed. Despite the challenges discussed, the main conclusion of this work is that ongoing technological developments are expected to ensure that flexible risers remain key components of offshore oil and gas production systems. Full article

Biological fish possess the intrinsic ability to dynamically modulate body stiffness to adapt to varying fluid environments, thereby optimizing propulsive efficiency, swimming speed, and maneuverability. In contrast, this capability remains a significant challenge for most existing robotic fish, which typically rely on fixed-stiffness configurations. This article presents a comprehensive review of variable stiffness structures and their applications in biomimetic robotic fish. The associated technologies are systematically classified into four categories: smart material-driven, bio-inspired, fluid-driven, and hybrid-driven mechanisms. A comparative analysis of state-of-the-art prototypes is conducted, evaluating critical performance metrics including physical dimensions, maximum swimming speed, minimum turning radius, maximum turning rate, and Strouhal number. Furthermore, the specific advantages and technical limitations of each variable stiffness category are critically assessed. Finally, existing challenges in current research are identified, and prospective directions are proposed. The review demonstrates that variable stiffness technology offers significant potential to advance the hydrodynamic performance of robotic fish and facilitate their deployment in practical engineering applications. Full article

Background and Objectives: Evidence on the impact of adherence to the Mediterranean diet alone—without supplementation—on serum and follicular fluid fatty acid profiles and assisted reproduction outcomes remains limited. This study evaluated the effects of a pre-treatment Mediterranean diet intervention on these parameters. Materials and Methods: In this prospective, non-randomized controlled trial, 32 women undergoing infertility treatment were allocated to a Mediterranean diet intervention group (n = 16) or a control group (n = 16). The intervention lasted 12 weeks, and adherence was assessed using validated dietary indices. Serum and follicular fluid fatty acid profiles were analyzed, and implantation and pregnancy outcomes were recorded. Results: The diet group showed increased ω-6 and ω-3 intake with decreased LA/ALA and ω-6/ω-3 ratios. In the control group, serum EPA + DHA levels declined, whereas in the diet group serum LA/ALA decreased. Follicular fluid in the intervention group had lower EPA + DHA and ω-6 ratios. Diet adherence correlated positively with MII oocytes (r = 0.797) and pronuclei (r = 0.741). No significant associations were found between follicular fluid fatty acids and IVF outcomes. A total of four implantation events were observed (intervention: n = 3; control: n = 1). Two of the implantations in the intervention group resulted in live births, while the remaining implantation events did not result in live birth. Conclusions: A Mediterranean diet-aligned dietary intervention may induce favorable changes in blood and follicular fluid parameters; however, the underlying metabolic mechanisms warrant further investigation. Three implantations were observed in the intervention group and one in the control group; given the low number of events, comparisons of live birth outcomes should be interpreted with caution. Overall, the findings regarding ART outcomes and clinical translation remain exploratory due to the limited sample size. Well-designed randomized controlled trials in which ART and clinical endpoints are defined as primary outcomes are needed. Full article

Background: Endometriosis is associated with infertility and impaired assisted reproductive technology (ART) outcomes, potentially due to an altered follicular microenvironment characterized by chronic inflammation. This study investigates the systemic and local inflammatory profiles in women with endometriosis and assesses their impact on oocyte and embryo quality using both static and dynamic embryo evaluation. Methods: A prospective, monocentric observational study enrolled 47 women undergoing controlled ovarian stimulation for ART, including 29 with laparoscopically confirmed endometriosis and 18 controls with tubal or male-factor infertility. Serum and follicular fluid cytokines (TGF-β1, NF-κB, IL-10, HIF-1α) were quantified. A sub-study analyzed embryo quality and development in 36 patients subdivided into static morphological assessment and dynamic time-lapse monitoring cohorts. Results: Endometriosis patients exhibited significantly elevated pro-inflammatory cytokines (TGF-β1, NF-κB) and reduced anti-inflammatory IL-10 in serum, alongside decreased NF-κB in follicular fluid. These alterations correlated with diminished ovarian reserve, reduced oocyte yield, and lower fertilization rates. Embryos from endometriosis patients showed increased multinucleation and persistent fragmentation, features more sensitively detected via dynamic time-lapse imaging. Clinical pregnancy rates were significantly lower in the endometriosis group. Conclusions: Endometriosis induces a dysregulated inflammatory follicular milieu that adversely affects oocyte competence and embryo morphodynamics. Dynamic embryo assessment provides enhanced detection of subtle developmental abnormalities. Integration of immunomodulatory strategies and advanced embryo monitoring may improve ART success in this population. Full article

Due to recent developments across the aerospace, power generation and defense sectors, the demand for flat-surfaced components with extremely high surface quality is rapidly increasing. In this regard, although abrasive machining processes often produce fine, contaminated swarf that is frequently relegated to landfill, these processes remain critical for the engineering sector. Motivated by increasing sustainability and circularity pressures, this narrative review examines the current state of the art in recycling and repurposing the chips, tooling and cutting fluids that are typically generated or consumed within grinding processes. In doing so, a number of methodologies for extracting useful materials from swarf slurries are identified, including pyrometallurgical routes (applied successfully to Ni–Co alloys, for example), hydrometallurgical strategies (e.g., iron leaching from ferrous swarf) and, in the case of non-metallic materials such as CMCs and CFRPs, chemical processing methods. Various means of separating abrasive constituents and removing contaminants from grinding swarf are also highlighted, within which centrifugation and heat treatment are found to be particularly useful for non-ferrous materials such as titanium alloys or composites, whilst ferrous materials are largely magnetically separated. Prospective applications for spent abrasive tooling are also explored, including reuse as shot, waterjet machining feedstock, road surface additives, or mortar in the context of cement production. Likewise, heat- and radiation-based strategies for prolonging cutting-fluid life are highlighted, and their associated sustainability benefits and limitations discussed, despite ultimate disposal still being relegated to fuel usage or landfill. Ultimately, this review identifies the scarcity of grinding-specific recycling process data and highlights the need for robust, publicly accessible recycling strategies for novel material systems. Full article

Background: Human polyomavirus JC (JCV) causes progressive multifocal leukoencephalopathy (PML), a deadly brain demyelinating illness stemming from oligodendrocyte lytic infection in immunocompromised patients, especially those with untreated HIV infection. Methods: We conducted a case series report on patients with HIV/AIDS who presented progressive multifocal leukoencephalopathy and were hospitalized at the “St. Parascheva” Clinical Hospital of Infectious Diseases in Iasi, northeastern Romania, to emphasize the comorbidities of HIV/AIDS cases. Hospital medical data from 10 January 2025 to 30 September 2025 served as the basis for this investigation. Results: We examined three cases that presented neurological symptoms (ataxia, aphasia, language comprehension, and expression disorders). The cases were evaluated imagistically via nuclear magnetic resonance, and we conducted a polymerase chain reaction test on the spinal fluid to confirm the presence of JCV. It was necessary to take a multidisciplinary approach with a neurologist or pneumologist. All cases were evaluated immunologically, revealing low Ly T CD4 levels and increased HIV viremia levels. Progressive multifocal leukoencephalopathy is an AIDS-defining disease, manifesting in immunocompromised patients, including late presenter cases, and patients who are non-adherent to their antiretroviral treatment. Therefore, it is important to test every patient who has mild to severe neurological symptoms for HIV. Furthermore, some cases require a multidisciplinary approach to ensure a better quality of life. Conclusions: Treating a patient with HIV requires a multidisciplinary strategy that includes a neurology specialist and access to antiretroviral treatment. To boost ART uptake, we must identify and remove barriers that impact patients and the healthcare system. Full article

Awaken, aware, arise, perform, pause, and repeat. The actions of the everyday. Without it, we fall into dysregulation. This paper seeks to examine creative research developed as an experiment during COVID-19, an audiovisualscape in virtual reality (VR). Rhythmanalysis+ is a social, ecological, and sensorial enquiry into materiality, grounded in archipelagic thinking, through the lens of Rhythmanalysis, a form of analysis focusing on the everyday, through the lens of cyclical and linear rhythms. (Lefebvre). The research will also draw on Deleuze and Guattari’s rhizome theory, a botanical and philosophical investigation into networks. Networks form the backbone of the research. Lars Bang Larsen also argues that networks offer a distinctive view on how factual, speculative, historical, and non-human elements envelop and intertwine. Glissant’s archipelagic thought promotes transformation, multiplicity, and a sense of unpredictability. For this work, four inhabitants from Sherkin, a small island off the southwest coast of Ireland with a population of 100, became the research focus. Across four weeks, islanders gathered data from their daily sensory rhythms. Flight patterns of birds and bats were recorded, daily tasks noted, pathways cycled. Relational impacts of animal-odour on farming, weather, and tides were processed remotely, and an immersive cartographic score was created as a direct response in a three-dimensional virtual space. Rhythmanalysis+ analyses our newly altered perceptions of time and space as a material within a virtual world. VR, created as a gaming platform, is being pushed by art itself, forcing us to relook at the natural world, which is not static, but relational. Fluid but equally extractive, it is important to look at technology’s impact on all that is human and how it is perceived within the body as it is reframed digitally. Full article

Background/Objectives: Periodontitis is a chronic inflammatory disease with marked inter-individual heterogeneity and well-established links to cardiometabolic and other systemic conditions. Conventional clinical diagnostics remain indispensable. However, they provide limited real-time insight into molecular activity and host-response biology. This review aimed to synthesize recent advances in point-of-care diagnostics and emerging molecular biomarkers relevant to periodontal disease and its systemic associations. Methods: We performed a state-of-the-art narrative review of literature published between 2018 and 2026. The focus was on point-of-care biosensing technologies and molecular biomarkers assessed in oral and related biological matrices. These included saliva, gingival crevicular fluid, blood, and dental plaque. Evidence was prioritized based on analytical performance, clinical validity, and translational readiness. Results: Substantial progress has been made in multiplex optical and electrochemical point-of-care platforms. These include microfluidic systems and early intraoral wearable sensors. Such technologies enable quantification of host-response proteins, including MMP-8, cytokines, and chemokines. In parallel, omics-derived biomarkers are emerging as clinically informative adjuncts for diagnosis and monitoring. MicroRNAs, cell-free DNA, extracellular vesicle–derived signals, proteomic profiles, and microbiome classifiers demonstrate promising discrimination. They also provide mechanistic links to systemic inflammation. Clinical translation remains limited by study heterogeneity, spectrum bias, and insufficient external validation. Conclusions: Near-term clinical value lies in adjunctive risk stratification and longitudinal disease monitoring. Replacement of conventional periodontal examination is not currently justified. Meaningful clinical and public-health impact will require standardized disease definitions. Harmonized sampling and reporting protocols are essential. Multicenter validation across comorbidity strata is needed. Regulatory-grade evidence must be generated for in vitro diagnostics and artificial intelligence software classified as medical devices. Full article

This review synthesizes the state of the art in flapping foil technology and bridges the distinct engineering domains of bio-inspired propulsion and power generation via flow energy harvesting. This review is motivated by the observation that propulsion and power-generation studies are frequently presented separately, even though they share common unsteady vortex dynamics. Accordingly, we adopt a unified unsteady-aerodynamic perspective to relate propulsion and energy-extraction regimes within a common framework and to clarify their operational duality. Within this unified framework, the feathering parameter provides a theoretical delimiter between momentum transfer and kinetic energy extraction. A critical analysis of experimental foundations demonstrates that while passive structural flexibility enhances propulsive thrust via favorable wake interactions, synchronization mismatches between deformation and peak hydrodynamic loading constrain its benefits in power generation. This review extends the analysis to complex and non-homogeneous environments and identifies that density stratification fundamentally alters the hydrodynamic performance. Specifically, resonant interactions with the natural Brunt–Väisälä frequency of the fluid shift the optimal kinematic regimes. The present study also surveys computational methodologies and highlights a paradigm shift from traditional parametric sweeps to high-fidelity three-dimensional (3D) Large-Eddy Simulations (LESs) and Deep Reinforcement Learning (DRL) to resolve finite-span vortex interconnectivities. Finally, this review outlines the critical pathways for future research. To bridge the gap between computational idealization and physical reality, the findings suggest that future systems prioritize tunable stiffness mechanisms, multi-phase environmental modeling, and artificial intelligence (AI)-driven digital twin frameworks for real-time adaptation. Full article

Background/Objectives: Volatile organic compounds (VOCs) have emerged as promising non-invasive biomarkers for assessing metabolic and reproductive health. In the context of assisted reproductive techniques (ARTs), the volatilomic composition of follicular fluid (FF) may reflect the biochemical environment surrounding the oocyte, influencing fertilization success and embryo development. This study aimed to characterize the volatilomic profile of FF in women undergoing ARTs and to explore associations between specific VOCs and female fertilization-related parameters (FFRPs). Methods: A total of 54 Caucasian women aged 19–39 years, enrolled between October 2015 and July 2019, were recruited at the Assisted Reproduction Laboratory of the Local Health Unit of Cova da Beira, Covilhã. FF samples were analyzed via gas chromatography–mass spectrometry (GC–MS) in scan mode, identifying 136 VOCs, of which 72 were selected based on prevalence. Sixteen FFRPs were evaluated, including age, body mass index (BMI), smoking habits, infertility factor, oocyte yield, embryo quality, β-hCG levels, country of birth, and reproductive history. Associations between VOCs and FFRPs were assessed using the Chi-square (χ²) test. Results: Significant correlations (p ≤ 0.05) were identified between 45 VOCs and 11 FFRPs. The detected compounds comprised alkanes, siloxanes, aromatics, alcohols, ketones, aldehydes, carboxylic acids and esters, fatty acid derivatives, epoxides, acrylates, nitriles, and sterols. Several VOCs were associated with more than one FFRP, indicating overlapping metabolic pathways that may influence reproductive performance. Conclusions: The volatilomic profile of FF demonstrates significant variability linked to individual reproductive and metabolic factors. VOC analysis may provide novel insights into follicular physiology, representing a promising approach for identifying potential biomarkers of infertility and ART outcomes. Full article

Engineering is suffering a significant change driven by the integration of artificial intelligence (AI) into engineering optimization in design, analysis, and operational efficiency across numerous disciplines. This review synthesizes the current landscape of AI-driven optimization methodologies and their impacts on engineering applications. In the literature, several frameworks for AI-based engineering optimization have been identified: (1) machine learning models are trained as objective and constraint functions for optimization problems; (2) machine learning techniques are used to improve the efficiency of optimization algorithms; (3) neural networks approximate complex simulation models such as finite element analysis (FEA) and computational fluid dynamics (CFD) and this makes it possible to optimize complex engineering systems; and (4) machine learning predicts design parameters/initial solutions that are subsequently optimized. Fundamental AI technologies, such as artificial neural networks and deep learning, are examined in this paper, along with commonly used AI-assisted optimization strategies. Representative applications of AI-driven engineering optimization have been surveyed in this paper across multiple fields, including mechanical and aerospace engineering, civil engineering, electrical and computer engineering, chemical and materials engineering, energy and management. These studies demonstrate how AI enables significant improvements in computational modelling, predictive analytics, and generative design while effectively handling complex multi-objective constraints. Despite these advancements, challenges remain in areas such as data quality, model interpretability, and computational cost, particularly in real-time environments. Through a systematic analysis of recent case studies and emerging trends, this paper provides a critical assessment of the state of the art and identifies promising research directions, including physics-informed neural networks, digital twins, and human–AI collaborative optimization frameworks. The findings highlight AI’s potential to redefine engineering optimization paradigms, while emphasizing the need for robust, scalable, and ethically aligned implementations. Full article

Traditional Turkish marbling (Ebru) art is an intangible cultural heritage characterized by highly asymmetric, fluid, and non-reproducible patterns, making its long-term preservation and large-scale dissemination challenging. It is highly sensitive to environmental conditions, making it enormously difficult to mass produce while maintaining its original aesthetic qualities. A data-driven generative model is therefore required to create unlimited, high-fidelity digital surrogates that safeguard this UNESCO heritage against physical loss and enable large-scale cultural applications. This study introduces a deep generative modeling framework for the digital reconstruction of traditional Turkish marbling (Ebru) art using a Deep Convolutional Generative Adversarial Network (DCGAN). A dataset of 20,400 image patches, systematically derived from 17 original marbling works, was used to train the proposed model. The framework aims to mathematically capture the asymmetric, fluid, and stochastic nature of Ebru patterns, enabling the reproduction of their aesthetic structure in a digital medium. The generated images were evaluated using multiple quantitative and perceptual metrics, including Fréchet Inception Distance (FID), Kernel Inception Distance (KID), Learned Perceptual Image Patch Similarity (LPIPS), and PRDC-based indicators (Precision, Recall, Density, Coverage). For experimental validation, the proposed DCGAN framework is additionally compared against a Vanilla GAN baseline trained under identical conditions, highlighting the advantages of convolutional architectures for modeling marbling textures. The results show that the DCGAN model achieved a high level of realism and diversity without mode collapse or overfitting, producing images that were perceptually close to authentic marbling works. In addition to the quantitative evaluation, expert qualitative assessment by a traditional Ebru artist confirmed that the model reproduced the organic textures, color dynamics, and compositional asymmetrical characteristic of real marbling art. The proposed approach demonstrates the potential of deep generative models for the digital preservation, dissemination, and reinterpretation of intangible cultural heritage recognized by UNESCO. Full article

Kallikrein-related peptidases (KLKs) are hallmarks of higher vertebrates, in particular of mammals. While the 15 human KLKs occur in nearly all tissues and body fluids and participate in many physiological processes, they are also involved in severe diseases. Among them are prostate, ovarian and breast cancer, as well as inherited skin and neurological disorders. Thus, KLKs have become targets for inhibitory compounds in academic and commercial research. The most prominent clinical biomarker and anti-cancer target for various approaches is PSA/KLK3. Already in the distant past, natural crude extracts were the source of medicine, while purified natural compounds and their derivatives are still the basis of about 50% of all pharmaceuticals. Nevertheless, structure-based rational design and high-throughput screening of natural and synthetic compound libraries are highly effective approaches for discovering lead compounds in the development of new drugs. Recently, computer-aided virtual or in silico screening has become a rapid method for such discoveries when combined with in vitro assays using protein targets or tests in cell cultures. To date, the successful implementation of artificial intelligence (AI) in the biosciences has significantly contributed to drug discovery. Our review focuses on state-of-the-art strategies and techniques in the context of KLK targets. Full article