Search Results (4,011)

As control and execution components within hydraulic systems, hydraulic valves are critical to system efficiency and operational safety. However, existing research primarily focuses on specific valve designs, exhibiting limitations in versatility and task coordination that constrain their comprehensive diagnostic capabilities. To address these issues, this paper innovatively proposes a multi-modal feature deep fusion multi-task prediction (CLAF-MTL) model. This model employs a core architecture based on the CNN-LSTM-Additive Attention module and a fully connected network (FCN) for multi-domain features, while simultaneously embedding a multi-task learning mechanism. It resolves the multi-task prediction challenge of leakage rate regression and fault type classification, significantly enhancing diagnostic efficiency and practicality. This model innovatively designs a complementary fusion mechanism of “deep auto-features + multi-domain features” overcoming the limitations of single-modality representation. It integrates leakage rate regression and fault type classification into a unified modeling framework, dynamically optimizing dual-task weights via the MGDA-UB algorithm to achieve bidirectional complementarity between tasks. Experimental results demonstrate that the proposed method achieves an R² of 0.9784 for leakage rate prediction and a fault type identification accuracy of 92.23% on the test set. Compared to traditional approaches, this method is the first to simultaneously address the challenge of accurately predicting both leakage rate and fault type. It exhibits superior robustness and applicability across generic valve scenarios, providing an effective solution for intelligent monitoring of valve leakage faults in hydraulic systems. Full article

Dopamine (DA) is a critical catecholaminergic neurotransmitter that facilitates signal transduction across synaptic junctions and modulates essential neurophysiological processes, including motor coordination, motivational drive, and reward-motivated behaviors. The fabrication of cost-effective, miniaturized, and high-fidelity analytical platforms is imperative for real-time DA monitoring. Due to its inherent electrochemical activity, carbon-based amperometric sensors constitute the primary modality for DA quantification. In this study, graphite, multi-walled carbon nanotubes (MWCNTs), and graphene were immobilized within an ethyl 2-cyanoacrylate (ECA) polymer matrix. ECA was selected for its rapid polymerization kinetics and established biocompatibility in electrochemical frameworks. All fabricated composites demonstrated robust electrocatalytic activity toward DA; however, MWCNT- and graphene-based sensors exhibited superior analytical performance, characterized by highly competitive limits of detection (LOD) and quantification (LOQ). Specifically, MWCNT-modified electrodes achieved an interesting LOD of 0.030 ± 0.001 µM and an LOQ of 0.101 ± 0.008 µM. Discrepancies in baseline current amplitudes suggest that the spatial orientation of carbonaceous nanomaterials within the cyanoacrylate matrix significantly influences the electrochemical surface area and resulting baseline characteristics. The impact of interfering species commonly found in biological environments on the sensors’ response was systematically evaluated. The best-performing sensor, the graphene-based one, was used to measure the DA intracellular content of PC12 cells. Full article

Unmanned Aerial Vehicle (UAV) platforms enable flexible and cost-effective vehicle detection for intelligent transportation systems, yet small-scale vehicles in complex aerial scenes pose substantial challenges from extreme scale variations, environmental interference, and single-sensor limitations. We present AMSRDet (Adaptive Multi-Scale Remote Sensing Detector), an adaptive multi-scale detection network fusing infrared (IR) and visible (RGB) modalities for robust UAV-based vehicle detection. Our framework comprises four novel components: (1) a MobileMamba-based dual-stream encoder extracting complementary features via Selective State-Space 2D (SS2D) blocks with linear complexity $O\left(HWC\right)$, achieving 2.1× efficiency improvement over standard Transformers; (2) a Cross-Modal Global Fusion (CMGF) module capturing global dependencies through spatial-channel attention while suppressing modality-specific noise via adaptive gating; (3) a Scale-Coordinate Attention Fusion (SCAF) module integrating multi-scale features via coordinate attention and learned scale-aware weighting, improving small object detection by 2.5 percentage points; and (4) a Separable Dynamic Decoder generating scale-adaptive predictions through content-aware dynamic convolution, reducing computational cost by 48.9% compared to standard DETR decoders. On the DroneVehicle dataset, AMSRDet achieves 45.8% mAP@0.5:0.95 (81.2% mAP@0.5) at 68.3 Frames Per Second (FPS) with 28.6 million (M) parameters and 47.2 Giga Floating Point Operations (GFLOPs), outperforming twenty state-of-the-art detectors including YOLOv12 (+0.7% mAP), DEIM (+0.8% mAP), and Mamba-YOLO (+1.5% mAP). Cross-dataset evaluation on Camera-vehicle yields 52.3% mAP without fine-tuning, demonstrating strong generalization across viewpoints and scenarios. Full article

Recent years have witnessed performance improvements in Multimodal Large Language Models (MLLMs) on downstream natural image understanding tasks. However, when applied to the function graph reasoning task, which is highly information-dense and abundant in fine-grained structural details, these models face pronounced performance degradation. The challenges are primarily characterized by several core issues: the static projection bottleneck, inadequate cross-modal interaction, and insufficient visual context in text embeddings. To address these problems, this study proposes a multimodal feature fusion enhancement method for function graph reasoning and constructs the FuncFusion-Math model. The core innovation of this model resides in its design of a dual-path feature fusion mechanism for both image and text. Specifically, the image fusion module adopts cross-attention and self-attention mechanisms to optimize visual feature representations under the guidance of textual semantics, effectively mitigating fine-grained information loss. The text fusion module, through feature concatenation and Transformer encoding layers, deeply integrates structured mathematical information from the image into the textual embedding space, significantly reducing semantic deviation. Furthermore, this study utilizes a four-stage progressive training strategy and incorporates the LoRA technique for parameter-efficient optimization. Experimental results demonstrate that the FuncFusion-Math model, with 3B parameters, achieves an accuracy of 43.58% on the FunctionQA subset of the MathVista test set, outperforming a 7B-scale baseline model by 13.15%, which validates the feasibility and effectiveness of the proposed method. Full article

Diabetic retinopathy (DR) remains the leading cause of preventable blindness among working-age adults worldwide, driven by the growing prevalence of diabetes mellitus. The aim of this comprehensive literature review is to provide an insightful analysis of recent advances in the pathogenesis of DR, followed by a summary of emerging technologies for its diagnosis and treatment. Recent studies have explored the roles of cell death pathways, immune activation, and lipid peroxidation in the pathology of DR. However, at the core of DR pathology lies neovascularization driven by vascular endothelial growth factor (VEGF), and mitochondrial damage due to dysregulated oxidative stress. These dysregulated pathways manifest clinically as DR, with specific subtypes including non-proliferative DR, proliferative DR and diabetic macular edema, which can be diagnosed through various imaging modalities. Recently, novel advances have been made using liquid biopsy and artificial (AI)-based algorithms with the goal of transforming DR diagnostics. AI models show distinct promise with the capacity to provide automated interpretation of retinal imaging. Furthermore, conventional anti-VEGF injectable agents have revolutionized DR treatment in the past decades. Today, as the pathogenesis of DR becomes better understood, new pathways, such as the ROS-VEGF loop, are being elucidated in greater depth, enabling the development of targeted therapies. In addition, new innovations such as intravitreal implants are transforming the delivery of DR-specific medication. This paper will discuss the current understanding of the pathogenesis of DR, which is leading to new diagnostic and therapeutic tools that will transform clinical management of DR. Full article

Bioreactors used for the maturation of cell-seeded tissue-engineered scaffolds should essentially mimic the dynamic in vivo environments experienced by the native tissues they intend to substitute. In addition to perfusion of growth medium to facilitate continuous mass transfer, application of appropriate mechanical stimulation is important to enhance cellular responses in scaffolds for tissues such as tendons, skin, and cardiac muscle that experience dynamic loading. This study focuses on the development of a multi-modal custom bioreactor capable of applying cyclic tensile stimulation and perfusion within physiologically relevant ranges while minimizing shear stress detrimental to cells seeded on scaffolds. To validate the bioreactor design and operation, we assessed the effects of tensile stimulation (0.1 Hz, 2000 cycles/day) and perfusion (media flow rate = 0.15 mL/min) over 21 days on the biofunctional performance of 3D-bioplotted polycaprolactone (PCL) auxetic scaffolds with a representative design (missing-rib pattern) characterized by negative Poisson’s ratio similar to the aforementioned soft tissues. The scaffold had a tensile yield strain of 9.14%, yield strength of 0.25 MPa, elastic modulus of 2.85 MPa, and ultimate tensile strength (UTS) of 1.32 MPa. The application of perfusion and tensile stimulation (0–5% cyclic strain) for 21 days did not adversely affect the yield strength and elastic modulus of the scaffold but affected its UTS (22.5% decrease) compared to the control cultured without perfusion or stimulation. Notably, it resulted in significantly improved fibroblast cellular responses (DNA = 29 µg/g sample and collagen = 371.78 µg/g sample) compared to the control (7.52 µg/g sample and 163.51 µg/g sample, respectively). These results validate the bioreactor system operation and the ability of multi-modal stimulation to control biofunctional responses of auxetic scaffolds, which will serve as the basis for future studies that will optimize auxetic scaffold design and dynamic culture parameters for NPR tissue-specific applications. Full article

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide. CVDs are associated with multiple factors, including oxidative stress, mediated endothelial dysfunction, vascular inflammation, and atherothrombosis. Although traditional antioxidant supplementation (such as vitamins C, E, and β-carotene) has shown promising results in rigorous animal model studies, it has consistently failed to demonstrate clinical benefit in most human trials. Consequently, there is a substantial unmet need for novel paradigms involving mechanistically and biologically relevant pharmaceutical-grade antioxidant therapies (“next-generation antioxidants”). Rapid advancements in redox biology, nanotechnology, genetic modulation of redox processes, and metabolic regulation have enabled the development of new antioxidant therapeutics, including mitochondrial-targeted agents, NADPH oxidase (NOX) inhibitors, selenoprotein and Nrf2 activators, engineered nanoparticles, catalytic antioxidants, and RNA-based and gene-editing strategies. These interventions have the potential to modulate specific oxidative pathways that contribute to CVD pathogenesis. This review provides a comprehensive assessment of current oxidative stress–modulating modalities and their potential to inform personalized cardiovascular prevention and treatment strategies. Full article

Estimating the relative position and velocity of micro aerial vehicles (MAVs) using visual signals is a critical issue in numerous tasks. However, traditional relative motion estimation algorithms suffer severely from non-Gaussian noise interference and have limited observability, making it difficult to meet the practical requirements of complex dynamic scenarios. To address this dilemma, this paper proposes a Multimodal Decoupled Spatiotemporal Adaptive Network (MDSAN). Designed for air-to-air scenarios, MDSAN achieves high-precision relative pose and velocity estimation of dynamic MAVs while overcoming the observability limitations of traditional algorithms. In detail, MDSAN is collaboratively composed of two core sub-modules: Modality-Specific Convolutional Normalization (MSCN) blocks and Spatiotemporal Adaptive State (STAS) blocks. Specifically, MSCN uses custom convolution kernels tailored to three modalities—visual, physical, and geometric—to separate their features. This prevents interference between modalities and reduces non-Gaussian noise. STAS, built on a state-space model, combines two key functions: it tracks long-term MAV motion trends over time and strengthens the synergy between different modal features across space. Adaptive weights balance these two functions, enabling stable estimation, even when traditional methods struggle with low observability. Furthermore, MDSAN adopts a full-vision multimodal fusion scheme, completely eliminating the dependence on wireless communication and reducing hardware costs. Extensive experimental results demonstrate that MDSAN achieves the best performance in all scenarios, significantly outperforming existing motion estimation algorithms. It provides a new technical path that balances high precision, high robustness, and cost-effectiveness for technologies such as MAV swarm perception. Full article

Background: Hip joint disorders exhibit diverse and overlapping radiological features, complicating early diagnosis and limiting the diagnostic value of single-modality imaging. Isolated imaging or clinical data may therefore inadequately represent disease-specific pathological characteristics. Methods: This retrospective study included 605 hip joints from Center A (2018–2024), comprising normal hips, osteoarthritis, osteonecrosis of the femoral head (ONFH), and femoroacetabular impingement (FAI). An independent cohort of 24 hips from Center B (2024–2025) was used for external validation. A multimodal deep learning framework was developed to jointly analyze radiographs, CT volumes, and clinical texts. Features were extracted using ResNet50, 3D-ResNet50, and a pretrained BERT model, followed by attention-based fusion for four-class classification. Results: The combined Clinical+X-ray+CT model achieved an AUC of 0.949 on the internal test set, outperforming all single-modality models. Improvements were consistently observed in accuracy, sensitivity, specificity, and decision curve analysis. Grad-CAM visualizations confirmed that the model attended to clinically relevant anatomical regions. Conclusions: Attention-based multimodal feature fusion substantially improves diagnostic performance for hip joint diseases, providing an interpretable and clinically applicable framework for early detection and precise classification in orthopedic imaging. Full article

Postpericardiotomy syndrome (PPS) is the most frequent inflammatory after-effect of cardiac surgery and is characterized by high morbidity, delayed hospitalization, and increased long-term mortality rates. Although PPS is common, empirical anti-inflammatory therapy has historically been employed for its prevention, and mechanism-based approaches have not yet been standardized. In this literature review, which was conducted on the basis of randomized controlled trials, meta-analyses, cohort studies, and mechanistic research regarding pharmacologic interventions, surgical modalities, and biomarker-based preventive strategies, the deficiencies of a critical synthesis of existing preventive strategies and emerging risk stratification instruments for PPS are addressed. The review affirms that the most evidence-based pharmacologic intervention is colchicine, which demonstrates a consistent reduction in PPS incidence across a range of randomized trials. Nonsteroidal anti-inflammatory drugs show variable responses, whereas corticosteroids are no longer recommended for routine prophylaxis due to relapse. Specific anti–interleukin-1 therapies represent a promising novel approach for high-risk patients. Surgical interventions, such as pericardial closure using biomaterials and posterior pericardiotomy, are important and do not lead to increased hemodynamic complications, while postoperative effusions, atrial fibrillation, and tamponade are reduced. Less invasive methods may also be employed to mitigate inflammatory causes, particularly in valve-sparing procedures and congenital operations. Emerging biomarker data, including postoperative neutrophil-to-lymphocyte ratios, C-reactive protein levels, and pericardial fluid cytokines, enable the identification of high-risk patients and form the basis for a personalized prevention approach. In summary, pharmacologic prophylaxis, innovative surgical techniques, and biomarker-based risk stratification represent a pathway toward reducing the incidence and burden of PPS in modern cardiac surgery. Full article

Dynamic contrast-enhancement (DCE) modality of MRI is typically considered secondary in prostate cancer (PCa) diagnostics, due to the common interpretation that its diagnostic power is lower than that of other modalities like T2-weighted (T2W) or diffusion-weighted imaging (DWI). To challenge this paradigm, this study introduces a novel concept of a difference map, which relies exclusively on DCE-MRI for the localization of peripheral zone prostate cancer using functional data analysis-based (FDA) signal processing. The proposed workflow uses discrete voxel-level DCE time–signal curves that are transformed into a continuous functional form. First-order derivatives are then used to determine patient-specific time points of greatest enhancement change that adapt to the intrinsic characteristics of each patient, producing diffmaps that highlight regions with pronounced enhancement dynamics, indicative of malignancy. A subsequent normalization step accounts for inter-patient variability, enabling consistent interpretation across subjects and probabilistic PCa localization. The approach is validated on a curated dataset of 20 patients. Evaluation of eight workflow variants is performed using weighted log loss, the best variant achieving a mean log loss of 0.578. This study demonstrates the feasibility and effectiveness of a single-modality, automated, and interpretable approach for peripheral prostate cancer localization based solely on DCE-MRI. Full article

Background and Clinical Significance: Acute aortic dissection is the most common and most severe manifestation of acute aortic syndrome. An isolated dissecting aneurysm of the abdominal aorta is defined as a dissecting aneurysm distal to the diaphragm and is an extremely rare disease. Detection of an intimal flap between two lumens using different imaging methods is a definitive diagnostic sign of aortic dissection. A number of studies have validated ultrasound, including point-of-care ultrasound, as the standard initial imaging modality for the diagnosis of aortic dissection. Case Presentation: We present a 39-year-old patient who was sent to our institution under the suspicion of renal colic. The clinical findings revealed pale discoloration of the skin with sweating and abdominal pain. An emergency ultrasound showed an abdominal aortic aneurysm with an intimal flap, as well as free perirenal fluid on the left side. Multislice computed tomography aortography was then performed and the findings indicated rupture of a dissecting aneurysm of the abdominal aorta with a large retroperitoneal hematoma. The patient was then sent to a tertiary institution where he underwent emergency surgery and successfully recovered. Conclusions: Isolated abdominal aortic dissection is a rare condition with often non-specific clinical presentation, making imaging crucial for diagnosis. Ultrasound plays an important role as an initial imaging modality, as the detection of direct or indirect signs of dissection enables timely referral for CT aortography, confirmation of the diagnosis, and initiation of appropriate treatment. Full article

Background/Objectives: Sensory deprivation, defined as a reduction or absence of external sensory input across one or more modalities, has long been investigated in extreme and experimental settings. More recently, its relevance has expanded to clinical contexts and environmental conditions. The present narrative review aims to synthesize current evidence on the neurobiological mechanisms, psychological effects, and clinical implications of sensory deprivation, with particular attention to its dual role as both a risk factor and, under controlled conditions, a potential therapeutic tool. Methods: A narrative literature search was conducted using PubMed, Scopus, and PsycINFO, covering studies published up to August 2025. Search terms included sensory deprivation, neuroplasticity, neurotransmitters, HPA axis, neuro-inflammation, circadian rhythms, psychopathology, extreme environments, and spaceflight. Preclinical and clinical studies examining biological, cognitive, and psychological consequences of reduced sensory stimulation were included. Data were synthesized thematically without quantitative meta-analysis. Results: Evidence indicates that sensory deprivation induces widespread neurobiological adaptations involving neurotransmitter systems (particularly dopaminergic pathways), dysregulation of the hypothalamic–pituitary–adrenal axis, neuroimmune activation, circadian rhythm disruption, and structural and functional brain changes, notably affecting the hippocampus. These alterations are associated with increased vulnerability to depression, anxiety, hallucinations, dissociative symptoms, and cognitive impairment. Duration, voluntariness, and individual differences (e.g., baseline vulnerability/resilience, trait anxiety, and prior psychiatric history) critically modulate outcomes. However, short-term and voluntary sensory restriction, such as Floatation-REST, may promote relaxation and emotional regulation under specific conditions. Conclusions: Sensory deprivation exerts complex, context-dependent effects on brain function and mental health. Duration, individual vulnerability, and voluntariness critically modulate outcomes. Understanding these mechanisms is increasingly relevant for clinical practice and for developing preventive strategies in extreme environments, including future long-duration space missions. Full article

Deep vision foundation models such as DINOv3 offer strong visual representation capacity, but their direct deployment in medical image segmentation remains difficult due to the limited availability of annotated clinical data and the computational cost of full fine-tuning. This study proposes an adaptation framework called StrDiSeg that integrates lightweight bottleneck adapters between selected transformer layers of DINOv3, enabling task-specific learning while preserving pretrained knowledge. An attention-enhanced U-Net decoder with multi-scale feature fusion further refines the representations. Experiments were performed on two publicly available ischemic stroke lesion segmentation datasets—AISD (Non Contrast CT) and ISLES22 (DWI). The proposed method achieved Dice scores of 0.516 on AISD and 0.824 on ISLES22, outperforming baseline models and demonstrating strong robustness across different clinical imaging modalities. These results indicate that adapter-based fine-tuning provides a practical and computationally efficient strategy for leveraging large pretrained vision models in medical image segmentation. Full article

Background: Conservative treatment of chronic musculoskeletal pain includes exercise, manual therapy, medications, physical agents/modalities, and Therapeutic Patient Education (TPE). Research on TPE has predominantly focused on spinal pain, so we do not know the extent and scope of clinical research in other areas, particularly lower extremities. This review aimed to map current research on this topic. Methods: We searched PubMed, PEDro, CINAHL, PsycINFO, and Cochrane Library up to 1 April 2024. We included RCTs on adults with chronic lower limb musculoskeletal pain, written in English, French, Spanish, or Italian. Results: Fifty-two records concerning knee osteoarthritis (n.33), hip and knee osteoarthritis (n.8), hip osteoarthritis (n.3), chronic knee pain (n.3), patellofemoral pain (n.3), and gluteal tendinopathy (n.2) were included. TPE was delivered through self-management, disease-specific information, pain education, and the management of physical activity, load, diet, stress, and sleep. Interventions were both individual- and group-based; delivery methods included in-person intervention, telephone/video calls, and web tools/apps. TPE combined with exercise seemed to be more effective than exercise alone, information/little education, or usual care. The effects of TPE as a stand-alone intervention appeared uncertain. Conclusions: There is considerable variability in TPE in terms of teaching topics, providers, administration methods, and dosage of interventions. Future studies should investigate the effects of TPE in young adult populations and in ankle conditions. They should also investigate the effects of TPE on pain intensity versus pain interference with activities, by deepening TPE effects on disability and quality of life. Full article