Search Results (58)

In this research project a large linear electromagnetic actuator (LLEA) was designed and manufactured. The electromagnetic performance was published in previous works, but in this paper we focus on the technological challenges related to the manufacturing in particular. This LLEA was based on the magnet-free switched-reluctance principle, having six effective energised stator “teeth” and four passive mover parts (4:6 ratio). Various aspects and challenges encountered during the manufacturing, transport, and assembly are discussed. Thermal expansion of steel contributed to the decision of the modular design, with each module having 1.3 m in length, with a 2 mm longitudinal dilatation gap. The initial prototype was tested with a 10.6 m length, with plans to extend the test track to 60 m, which was fully achievable due to the modular design and required 29 tons of electrical steel to be built. The stator laminations were cut by a bespoke progressive tool with stamping, and other parts by a CO₂ laser. Mounting was based on welding (back of the stator) and clamping plates (through insulated bolts). The linear longitudinal force was on the order of 8 kN, with the main air gap of 7.5–10 mm on either side of the mover. The lateral forces could exceed 40 kN and were supported by appropriate construction steel members bolted to the concrete floor. The overall mechanical tolerances after installation remained below 0.5 mm. The technology used for constructing this prototype demonstrated the cost-effective way for a semi-industrial manufacturing scale. Full article

Infrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. To address the issues of sparse feature loss for small targets during the down-sampling phase of the traditional U-Net network and the semantic gap in the feature fusion process, a multilevel feature extraction and fusion attention network (MEFA-Net) is designed. Specifically, the dilated direction-sensitive convolution block (DDCB) is devised to collaboratively extract local detail features, contextual features, and Gaussian salient features via ordinary convolution, dilated convolution and parallel strip convolution. Furthermore, the encoder attention fusion module (EAF) is employed, where spatial and channel attention weights are generated using dual-path pooling to achieve the adaptive fusion of deep and shallow layer features. Lastly, an efficient up-sampling block (EUB) is constructed, integrating a hybrid up-sampling strategy with multi-scale dilated convolution to refine the localization of small targets. The experimental results confirm that the proposed algorithm model surpasses most existing recent methods. Compared with the baseline, the intersection over union (IoU) and probability of detection $P_d$ of MEFA-Net on the IRSTD-1k dataset are increased by 2.25% and 3.05%, respectively, achieving better detection performance and a lower false alarm rate in complex scenarios. Full article

Autonomic imbalance is one of the major pathological disturbances in chronic heart failure (CHF). Additionally, enhanced oxidative stress and inflammation are considered to be the main contributors to the disease progression. A growing body of evidence suggests cholinergic stimulation as a potential therapeutic approach in CHF, since it corrects the autonomic imbalance and alters the inflammatory response via the cholinergic anti-inflammatory pathway. Although previous research has provided some insights into the potential mechanisms behind these effects, there is a gap in knowledge regarding different cholinergic stimulation methods and their specific mechanisms of action. In the present study, an isoprenaline model (5 mg/kg/day s.c. for 7 days, followed by 4 weeks of CHF development) was used. Afterwards, rats received pyridostigmine (22 mg/kg/day in tap water for 14 days) or no treatment. Pyridostigmine treatment prevented the progression of CHF, decreasing chamber wall thinning (↑ PWDd, ↑ PWDs) and left ventricle dilatation (↓ LVIDd, ↓ LVIDs), thus improving cardiac contractile function (↑ EF). Additionally, pyridostigmine improved antioxidative status (↓ TBARS, ↓ NO₂⁻; ↑ CAT, ↑ GSH) and significantly reduced cardiac fibrosis development, confirmed by pathohistological findings and biochemical marker reduction (↓ MMP2, ↓ MMP9). However, further investigations are needed to fully understand the exact cellular mechanisms involved in the CHF attenuation via pyridostigmine. Full article

Lane detection is a key technology in automatic driving environment perception, and its accuracy directly affects vehicle positioning, path planning, and driving safety. In this study, an enhanced real-time model for lane detection based on an improved DeepLabV3+ architecture is proposed to address the challenges posed by complex dynamic backgrounds and blurred road boundaries in suburban road scenarios. To address the lack of feature correlation in the traditional Atrous Spatial Pyramid Pooling (ASPP) module of the DeepLabV3+ model, we propose an improved LC-DenseASPP module. First, inspired by DenseASPP, the number of dilated convolution layers is reduced from six to three by adopting a dense connection to enhance feature reuse, significantly reducing computational complexity. Second, the convolutional block attention module (CBAM) attention mechanism is embedded after the LC-DenseASPP dilated convolution operation. This effectively improves the model’s ability to focus on key features through the adaptive refinement of channel and spatial attention features. Finally, an image-pooling operation is introduced in the last layer of the LC-DenseASPP to further enhance the ability to capture global context information. DySample is introduced to replace bilinear upsampling in the decoder, ensuring model performance while reducing computational resource consumption. The experimental results show that the model achieves a good balance between segmentation accuracy and computational efficiency, with a mean intersection over union (mIoU) of 95.48% and an inference speed of 128 frames per second (FPS). Additionally, a new lane-detection dataset, SubLane, is constructed to fill the gap in the research field of lane detection in suburban road scenarios. Full article

Gender-specific cardiology has gained increasing recognition in recent years, emphasizing the need for tailored management strategies for women with cardiovascular disease. Among these, cardiomyopathies—dilated, arrhythmogenic, hypertrophic, and restrictive—pose unique challenges throughout a woman’s reproductive life, affecting contraception choices, pregnancy outcomes, and breastfeeding feasibility. Despite significant advances in cardiovascular care, there is still limited guidance on balancing maternal safety and neonatal well-being in this complex setting. This review provides a comprehensive overview of the current evidence on reproductive counseling, pregnancy management, and postpartum considerations in women with cardiomyopathies. We discuss the cardiovascular risks associated with each cardiomyopathy subtype during pregnancy, highlighting risk stratification tools and emerging therapeutic strategies. Additionally, we address the safety and implications of breastfeeding, an often overlooked but increasingly relevant aspect of postpartum care. A multidisciplinary approach involving cardiologists, gynecologists, obstetricians, and anesthesiologists is crucial to optimizing maternal and fetal outcomes. Improved risk assessment, tailored patient counseling, and careful management strategies are essential to ensuring safer reproductive choices for women with cardiomyopathy. From now on, greater attention is expected to be given to bridging existing knowledge gaps, promoting a more personalized and evidence-based approach to managing these patients throughout different stages of reproductive life. Full article

Understanding the mechanical behavior of frozen clay subgrade soils was essential for ensuring the safe and stable operation of transportation lines. However, the influence of remolding water content w on this behavior remained unclear. To address this gap, this study examined the effect of w through monotonic triaxial testing. Three typical remolding water contents (w = 19%, 27.5% and 35%) and three confining pressures (σ₃ = 200 kPa, 700 kPa and 1200 kPa) were considered. Results showed that the mechanical behavior of frozen clay soils displayed a clear dependence on w, which was controlled by microstructural evolution. As w increased, the shear strength q_max, resilient modulus E₀ and cohesion c increased, which resulted from the progressive development of ice bonding within the shear plane. A threshold w value was found at w_opt = 27.5%, marking a structural transition and separating the variations of q_max, E₀ and c into two regimes. When w ≤ 27.5%, the soil fabric was controlled by clay aggregates. As w increased, the growth in ice cementation was confined within these aggregates, leading to limited increase in q_max, E₀ and c. However, as w exceeded 27.5%, the soil fabric transitioned into a homogeneous matrix of dispersed clay particles. In this case, increasing w greatly promoted the development of an interconnected ice cementation network, thus significantly facilitating the increase in q_max, E₀ and c. The friction angle φ decreased with w increasing, primarily due to the lubrication effect caused by the growing ice. In addition, the enhanced lubrication effect in the clay particle-dominated fabric (w > 27.5%) resulted in a larger reduction rate of φ. Regarding Poisson’s ratio v and dilation angle ψ, the w increase led to growth in both parameters. This phenomenon could be explained by the increased involvement of solid ice into the soil structure. Full article

Endurance training induces significant cardiac remodeling, with evidence suggesting that prolonged high-intensity exercise may increase the risk of atrial fibrillation (AF). However, physiological responses differ by event type. This review compares AF-related markers in marathon and ultramarathon runners, focusing on structural adaptations, inflammatory and endothelial biomarkers, and the incidence of arrhythmias. A systematic analysis of 29 studies revealed consistent left atrial (LA) enlargement in marathon runners linked to elevated AF risk and fibrosis markers such as Galectin-3 and PIIINP. In contrast, ultramarathon runners exhibited right atrial (RA) dilation and increased systemic inflammation, as indicated by elevated high-sensitivity C-reactive protein (hs-CRP) and soluble E-selectin levels. AF incidence in marathoners ranged from 0.43 per 100 person-years to 4.4%, while direct AF incidence data remain unavailable for ultramarathon populations, highlighting a critical evidence gap. These findings suggest distinct remodeling patterns and pathophysiological profiles between endurance disciplines, with implications for athlete screening and cardiovascular risk stratification. Full article

Background/Objectives: Artificial intelligence (AI) models for Alzheimer’s disease (AD) diagnosis often face the challenge of limited explainability, hindering their clinical adoption. Previous studies have relied on full-scale MRI, which increases unnecessary features, creating a “black-box” problem in current XAI models. Methods: This study proposes an explainable ensemble-based diagnostic framework trained on both clinical data and mid-slice axial MRI from the ADNI and OASIS datasets. The methodology involves training an ensemble model that integrates Random Forest, Support Vector Machine, XGBoost, and Gradient Boosting classifiers, with meta-logistic regression used for the final decision. The core contribution lies in the exclusive use of mid-slice MRI images, which highlight the lateral ventricles, thus improving the transparency and clinical relevance of the decision-making process. Our mid-slice approach minimizes unnecessary features and enhances model explainability by design. Results: We achieved state-of-the-art diagnostic accuracy: 99% on OASIS and 97.61% on ADNI using clinical data alone; 99.38% on OASIS and 98.62% on ADNI using only mid-slice MRI; and 99% accuracy when combining both modalities. The findings demonstrated significant progress in diagnostic transparency, as the algorithm consistently linked predictions to observed structural changes in the dilated lateral ventricles of the brain, which serve as a clinically reliable biomarker for AD and can be easily verified by medical professionals. Conclusions: This research presents a step toward more transparent AI-driven diagnostics, bridging the gap between accuracy and explainability in XAI. Full article

This study proposes an advanced thermodynamic energy equation to accurately simulate the stress–dilatancy relationship in granular soils for both uncrushed and crushed sands. Traditional energy formulations primarily consider dissipation energy and often neglect the role of free energy. Recent developments have introduced free energy components to account for plastic energy contributions from dilation and particle crushing. However, significant discrepancies between theoretical predictions and experimental observations remain, largely due to the omission of complex mechanisms such as contact network rearrangement, force-chain buckling, grain rolling, rotation without slip, and particle crushing. To address these gaps, the proposed model incorporates dual exponential decay functions into the free energy framework. Rather than explicitly modeling each mechanism, this formulation aims to phenomenologically capture the interplay between fundamentally opposing thermodynamic forces arising from complex mechanisms during granular microstructure evolution. The model’s applicability is validated using the experimental results from both uncrushed silica sand and crushed calcareous sand. Through extensive comparison with over 100 drained triaxial tests on various sands, the proposed model shows substantial improvement in reproducing stress–dilatancy behavior. The average discrepancy between predicted and measured $\eta –D$ relationships is reduced to below 15%, compared to over 60% using conventional models. This enhanced energy equation provides a robust and practical tool for predicting granular soil behavior, supporting a wide range of geotechnical engineering applications. Full article

Video anomaly detection (VAD) faces significant challenges in multimodal semantic alignment and long-term temporal modeling within open surveillance scenarios. Existing methods are often plagued by modality discrepancies and fragmented temporal reasoning. To address these issues, we introduce MT-CMVAD, a hierarchically structured Transformer architecture that makes two key technical contributions: (1) A Context-Aware Dynamic Fusion Module that leverages cross-modal attention with learnable gating coefficients to effectively bridge the gap between RGB and optical flow modalities through adaptive feature recalibration, significantly enhancing fusion performance; (2) A Multi-Scale Spatiotemporal Transformer that establishes global-temporal dependencies via dilated attention mechanisms while preserving local spatial semantics through pyramidal feature aggregation. To address the sparse anomaly supervision dilemma, we propose a hybrid learning objective that integrates dual-stream reconstruction loss with prototype-based contrastive discrimination, enabling the joint optimization of pattern restoration and discriminative representation learning. Our extensive experiments on the UCF-Crime, UBI-Fights, and UBnormal datasets demonstrate state-of-the-art performance, achieving AUC scores of 98.9%, 94.7%, and 82.9%, respectively. The explicit spatiotemporal encoding scheme further improves temporal alignment accuracy by 2.4%, contributing to enhanced anomaly localization and overall detection accuracy. Additionally, the proposed framework achieves a 14.3% reduction in FLOPs and demonstrates 18.7% faster convergence during training, highlighting its practical value for real-world deployment. Our optimized window-shift attention mechanism also reduces computational complexity, making MT-CMVAD a robust and efficient solution for safety-critical video understanding tasks. Full article

The accurate modeling of dynamic soil–structure interaction processes under impact loading is critical for advancing the design of soil-embedded barrier systems. Full-scale crash testing remains the benchmark for evaluating barrier performance; however, such tests are costly, logistically demanding, and subject to variability that limits repeatability. Recent advancements in computational methods, particularly the development of large-deformation numerical schemes, such as the multi-material arbitrary Lagrangian–Eulerian (MM-ALE) and smoothed particle hydrodynamics (SPH) approaches, offer viable alternatives for simulating soil behavior under impact loading. These methods have enabled a more realistic representation of granular soil dynamics, particularly that of the Manual for Assessing Safety Hardware (MASH) strong soil, a well-graded gravelly soil commonly used in crash testing of soil-embedded barriers and safety features. This soil exhibits complex mechanical responses governed by inter-particle friction, dilatancy, confining pressure, and moisture content. Nonetheless, the predictive fidelity of these simulations is governed by the selection and implementation of soil constitutive models, which must capture the nonlinear, dilatant, and pressure-sensitive behavior of granular materials under high strain rate loading. This review critically examines the theoretical foundations and practical applications of a range of soil constitutive models embedded in the LS-DYNA hydrocode, including elastic, elastoplastic, elasto-viscoplastic, and multi-yield surface formulations. Emphasis is placed on the unique behaviors of MASH strong soil, such as confining-pressure dependence, limited elastic range, and strong dilatancy, which must be accurately represented to model the soil’s transition between solid-like and fluid-like states during impact loading. This paper addresses existing gaps in the literature by offering a structured basis for selecting and evaluating constitutive models in simulations of high-energy vehicular impact events involving soil–structure systems. This framework supports researchers working to improve the numerical analysis of impact-induced responses in soil-embedded structural systems. Full article

Semantic segmentation plays a critical role in medical image analysis, offering indispensable information for the diagnosis and treatment planning of liver diseases. However, due to the complex anatomical structure of the liver and significant inter-patient variability, the current methods exhibit notable limitations in feature extraction and fusion, which pose a major challenge to achieving accurate liver segmentation. To address these challenges, this study proposes an improved U-Net-based liver semantic segmentation method that enhances segmentation performance through optimized feature extraction and fusion mechanisms. Firstly, a multi-scale input strategy is employed to account for the variability in liver features at different scales. A multi-scale convolutional attention (MSCA) mechanism is integrated into the encoder to aggregate multi-scale information and improve feature representation. Secondly, an atrous spatial pyramid pooling (ASPP) module is incorporated into the bottleneck layer to capture features at various receptive fields using dilated convolutions, while global pooling is applied to enhance the acquisition of contextual information and ensure efficient feature transmission. Furthermore, a Channel Transformer module replaces the traditional skip connections to strengthen the interaction and fusion between encoder and decoder features, thereby reducing the semantic gap. The effectiveness of this method was validated on integrated public datasets, achieving an Intersection over Union (IoU) of 0.9315 for liver segmentation tasks, outperforming other mainstream approaches. This provides a novel solution for precise liver image segmentation and holds significant clinical value for liver disease diagnosis and treatment. Full article

Indoor image semantic segmentation technology is applied to fields such as smart homes and indoor security. The challenges faced by semantic segmentation techniques using RGB images and depth maps as data sources include the semantic gap between RGB images and depth maps and the loss of detailed information. To address these issues, a multi-head self-attention mechanism is adopted to adaptively align features of the two modalities and perform feature fusion in both spatial and channel dimensions. Appropriate feature extraction methods are designed according to the different characteristics of RGB images and depth maps. For RGB images, asymmetric convolution is introduced to capture features in the horizontal and vertical directions, enhance short-range information dependence, mitigate the gridding effect of dilated convolution, and introduce criss-cross attention to obtain contextual information from global dependency relationships. On the depth map, a strategy of extracting significant unimodal features from the channel and spatial dimensions is used. A lightweight skip connection module is designed to fuse low-level and high-level features. In addition, since the first layer contains the richest detailed information and the last layer contains rich semantic information, a feature refinement head is designed to fuse the two. The method achieves an mIoU of 53.86% and 51.85% on the NYUDv2 and SUN-RGBD datasets, which is superior to mainstream methods. Full article

Aortic aneurysms (AAs), the dilation or widening of the aorta, lead to dissection or rupture with high morbidity and mortality if untreated. AA displays gender disparities in its prevalence, progression and outcomes, with women having worse outcomes and faster aneurysm growth. However, current guidelines do not address gender dimorphism, emphasizing the urgent need for personalized treatment strategies and further research. Perivascular adipose tissue (PVAT), a unique type of fat surrounding blood vessels, plays a critical role in maintaining vasomotor tone and vascular homeostasis, with dysfunction associated with chronic inflammation and vessel-wall remodeling. Indeed, PVAT dysfunction promotes the development of aortic aneurysms, with hormonal and biomechanical factors exacerbating the pathological vascular microenvironment. The sexually dimorphic characteristics of PVAT include morphological, immunological, and hormonally mediated differences. Thus, targeting PVAT-mediated mechanisms may be a promising option for the (gender-specific) therapeutic management of cardiovascular pathologies. This review examines the emerging importance of PVAT in vascular health, its potential therapeutic implications for AA, and identifies gaps in the current state of research. Full article

Introduction: Congenital duodenal atresia (DA) (Type I) with a fenestrated web can be characterized by a late presentation with a delayed diagnosis. It is even rarer and usually associated with proximal duodenomegaly. Conventional management involves web resection and duodeno–duodeno anastomosis with or without duodenoplasty. We describe our mininvasive surgical strategy and management, detailing the aspects of laparoscopic techniques. Material and Methods: We retrospectively reviewed the medical records of five patients affected by fenestrated duodenal web (DA) with a delayed onset of symptoms and diagnosis who were managed in our Department over a period of 10 years (2013–2023). We analyzed the age of patients at diagnosis, clinical signs and symptoms, associated congenital anomalies, radiological and intraoperative findings, surgical treatment, and outcomes. Diagnostic examinations included ultrasound (US), Upper-Gastrointestinal Study (UGI), and Esophagogastroduodenoscopy (EGDS). Results: Three boys and two girls, median age of 5.5 months (range 3–11 months), were included in this study. Three underwent previous surgery for long-gap esophageal atresia (EA), two of Type A, and one of Type C, requiring a gastrostomy immediately after birth (delayed esophageal repair for prematurity in Type C) and subsequent delayed primary anastomosis. Major associated anomalies were EA (3), anterior ectopic anus (1), cloaca (1), and Type IV laryngeal web (1). An antenatal diagnostic suspicion of duodenal atresia (obstruction) on ultrasound was described in two patients. UGI suggested a fenestrated duodenal web, visualized at ultrasound in two patients. Duodenal dilation was associated in two cases. The symptoms were feeding difficulties, nonbilious vomiting, upper abdominal distension, and poor growth. All presented with a pre-ampullary obstruction. Endoscopic confirmation was only possible in one patient. The older patient underwent an endoscopic resection of a duodenal web. In the other four, we performed a laparoscopic longitudinal antimesenteric duodenal incision, web resection (excision), and transverse suture (closure was performed) without duodenoplasty. Intraduodenal Indocyanine Green (ICG) visualization (under near-infrared light) was used in the last two cases. No postoperative complications were recorded, with a mean hospital stay of 8 days. A contrast study performed at 4 weeks demonstrated an improved proximal duodenal profile; patients tolerated a full diet and remained symptom-free. Conclusions: According to our experience with minimally invasive techniques, laparoscopy and endoscopy are effective and safe, supporting web resection for the management of a duodenal web without tapering of the proximal duodenum. They require advanced technical skills. Intraduodenal-ICG injection during laparoscopic treatment of Type 1 DA allows localization of the duodenal web, confirmation of bowel patency (bowel canalization) and the tightness of suture. Full article