Search Results (12,009)

This work aims to evaluate the economic and environmental performance of hybrid additive manufacturing (HAM) chains with metal forming operations in comparison with conventional manufacturing approaches. The approach integrates processes such as Wire-Arc Directed Energy Deposition (DED-Arc), machining, and incremental sheet forming to combine material deposition, shaping, and finishing within a single processing chain. To support this, a process-based cost model (PBCM) was developed to estimate production costs by linking process parameters with technological and operational variables and implementing computer-assisted modeling of the processing chain for identification of the production costs and corresponding key cost drivers. In parallel, a cradle-to-gate Life Cycle Assessment (LCA) was performed to evaluate environmental impacts across the stages of the HAM chain. The results indicate that direct labor, material, and machine usage are the primary cost drivers in the HAM chain. Compared to conventional chains of machining from solid or die casting, HAM achieves high reductions in production cost, from 67.8% to 84.5%, and in environmental impact of up to one order of magnitude, due to lower material consumption and independence from dedicated tooling. Overall, this work provides an integrated framework for the economic and environmental assessment of HAM, laying the foundation for future industrial implementation. Full article

Introduction: Rapid and reliable neurovascular imaging is critical for time-sensitive diagnosis in acute cerebrovascular disorders, yet conventional magnetic resonance imaging (MRI) workflows remain constrained by acquisition speed, motion sensitivity, and limited integration of physiological context. We introduce the Intelligent Neurovascular Imaging Engine (INIE), a sensor-informed, topology-aware framework that jointly optimizes accelerated data acquisition, physics-grounded reconstruction, and cross-scale physiological consistency. Methods: INIE combines adaptive sampling, structured low-rank (Hankel) priors, and topology-preserving objectives with multimodal physiological sensors and scanner telemetry, enabling phase-consistent gating and confidence-weighted reconstruction under realistic operating conditions. The framework was evaluated using synthetic phantoms, a translational porcine stroke recanalization model with repeated measures, and retrospective human datasets. Across $N_{\mathrm{runs}}=120$ acquisition–reconstruction runs derived from $N_{\mathrm{animals}}=18$ pigs with animal-level train/validation/test separation, performance was assessed using image quality, topological fidelity, and cross-modal consistency metrics. Multiple-comparison control was performed using Bonferroni/Holm–Bonferroni procedures. Results: INIE achieved acquisition acceleration exceeding 70% while maintaining high reconstruction fidelity (PSNR $\approx 35$–36 dB, SSIM $\approx 0.90$–0.92). Topology-aware analysis showed an approximately twofold reduction in Betti number deviation relative to baseline accelerated methods. Cross-modal validation in a PET subset demonstrated strong agreement between MRI-derived perfusion parameters and metabolic markers (Pearson $r\approx 0.9$). INIE improved large-vessel occlusion detection accuracy to approximately 93% and reduced automated time-to-decision to under three minutes. Conclusions: These results indicate that sensor-informed, topology-aware, closed-loop imaging improves the reliability and physiological consistency of accelerated neurovascular MRI and supports faster, more robust decision-making in acute cerebrovascular imaging workflows. Full article

Fragmented, irregular, and scale-heterogeneous farmland parcels introduce high spatial complexity into high-resolution remote sensing imagery, leading to boundary ambiguity and inter-class spectral confusion that hinder effective feature discrimination in semantic segmentation. To address these challenges, we propose CSMNet, which adopts a ConvNeXt V2 encoder for hierarchical representation learning and a multi-scale fusion architecture with redesigned skip connections and lateral outputs to reduce semantic gaps and preserve cross-scale information. An adaptive multi-head attention module dynamically integrates channel-wise, spatial, and global contextual cues through a lightweight gating mechanism, enhancing boundary awareness in structurally complex regions. To further improve robustness, a hybrid loss combining Binary Cross-Entropy and Dice loss is employed to alleviate class imbalance and ensure reliable extraction of small and fragmented parcels. Experimental results from Nong’an County demonstrate that the proposed model achieves superior performance compared with several state-of-the-art segmentation methods, attaining a Precision of 95.91%, a Recall of 93.95%, an F1-score of 94.92%, and an IoU of 90.85%. The IoU exceeds that of Unet++ by 8.92% and surpasses PSPNet, SegNet, DeepLabv3+, TransUNet, SeaFormer and SegMAN by more than 15%, 10%, 7%, 6%, 5% and 2%, respectively. These results indicate that CSMNet effectively improves information utilization and boundary delineation in complex agricultural landscapes. Full article

Delayed reperfusion of an ischemic heart is known to impair the recovery of cardiac function, and the occurrence of intracellular Ca²⁺ overload in the myocardium is considered to play a critical role in the development of ischemia-reperfusion (I/R) injury. Since Ca²⁺/Mg²⁺ ecto-ATPase, which is activated by millimolar concentrations of Ca²⁺ or Mg²⁺, has been shown to serve as a Ca²⁺ gating mechanism for the entry of Ca²⁺ and subsequent development of intracellular Ca²⁺ overload, we investigated the role of depression in Ca²⁺/Mg²⁺ ecto-ATPase activity by polyclonal antibodies against Ca²⁺/Mg²⁺ ecto-ATPase in promoting the recovery of cardiac function in isolated perfused rat hearts upon subjection to I/R injury. Incubation of sarcolemma (SL) membranes with immune serum or purified IgG antibody fraction was found to depress both Ca²⁺-ATPase and Mg²⁺-ATPase activities. Pretreatment of hearts with immune serum or purified antibodies was observed to improve the recovery of cardiac function and depress the SL Ca²⁺/Mg²⁺ ecto-ATPase activities in hearts subjected to I/R injury. A marked increase in myocardial Ca²⁺ content in I/R hearts was also attenuated by immune serum treatment. Furthermore, treatment of cardiomyocytes from normal hearts with immune serum or purified antibodies reduced the ATP-induced increase in intracellular Ca²⁺ concentration. These results suggest that improvement in the recovery of cardiac function in hearts subjected to I/R injury by polyclonal Ca²⁺/Mg²⁺ ecto-ATPase antibodies may be due to the attenuation of intracellular Ca²⁺ overload. Full article

Fibroblast growth factor 12 (FGF12), a member of the intracellular fibroblast growth factor homologous factor (iFGF) subfamily, has been widely studied for its role in the modulation of voltage-gated ion channels. However, recent studies suggest that FGF12 possesses various cellular functions beyond ion channel regulation, particularly in cancer progression. Accumulating evidence indicates that the upregulation of FGF12 is associated with tumor survival, therapeutic resistance, and poor prognosis through signaling pathways independent of its canonical ion channel interactions. This review summarizes the current understanding of FGF12’s non-canonical functions, highlights its emerging roles in cellular regulation, and discusses its potential mechanism in oncogenic progression. Understanding these novel functions may provide a new aspect for therapeutic targeting of FGF12 in malignancies. Full article

Ion-Sensitive Field-Effect Transistors (ISFETs) have been extensively used to detect various biomolecules, as the intrinsic charge of these molecules can change the transistor’s current or threshold voltage. Recently, realizing ISFET biosensors with better performance has attracted much attention. This paper proposes a novel ISFET biosensor by using the advantage of Tunnel Field-Effect Transistor (TFET). The device characteristics and sensing performance are systematically investigated by Silvaco Atlas TCAD simulations. Due to the novel structural design, the proposed sensor achieves a maximum current sensitivity (S_IDSmax) of 99.99% and a threshold voltage sensitivity (S_VTH) of 124%. To provide optimization guidelines, this work further explored the effect of geometric dimensions and gate dielectric materials on device performance. The excellent performance of the proposed biosensor makes it a promising candidate for future low-power, high-sensitivity biodetection applications. Full article

In the process of using the freezing method to uncover coal from stone gates, the thermal evolution profiles of the coal body during the freezing process tend to be complex due to the presence of gas and moisture. To investigate the temperature response of coal containing gas under different freezing temperature conditions, a self-developed low-temperature freezing test system for coal containing water and gas was used to conduct freezing and cooling tests at different freezing temperatures (−5 °C to −30 °C). The temperature changes at various measuring points inside the coal over time were monitored in real time, and the temperature distribution, cooling law, and strain evolution process of the coal in the axial and radial directions were analyzed. The experimental results show that the cooling process of the center point of the coal can be divided into four stages: rapid cooling, extremely slow temperature drop, relatively slow cooling, and stable constant temperature. The time required to reach the stable constant temperature stage is inversely proportional to the freezing temperature, and corresponding prediction formulas have been established based on this. The standardized coal briquettes exhibit a gradient distribution characteristic of gradually increasing temperature from outside to inside in both axial and radial directions, with the radial temperature distribution being well matched by an exponential decay model. The strain of coal is affected by both thermal shrinkage and ice-induced expansion. The occurrence time of frost heave is positively correlated with freezing temperature, while the strain of frost heave is negatively correlated with freezing temperature. The axial frost heave effect is significantly stronger than the radial effect, but the radial frost heave occurs slightly earlier than the axial effect. This study reveals the thermal-mechanical coupling response mechanism of gas-containing coal during the low-temperature freezing process, and the research results can provide theoretical support for parameter optimization and engineering application of low-temperature freezing anti-outburst technology. Full article

Single image super-resolution (SISR) is a classical computer vision task that aims to reconstruct a high-resolution image from a low-resolution input, thereby improving detail sharpness and visual quality. In recent years, convolutional neural network (CNN)-based methods and transformer-based methods using self-attention mechanisms have achieved significant progress in visible-image super-resolution. However, the direct application of these two types of methods to infrared images still poses considerable challenges. On the one hand, infrared images generally suffer from low signal-to-noise ratio, blurred edges, and missing details, and relying only on local convolutions makes it difficult to adequately model long-range dependencies across regions. On the other hand, although pure transformer models have a strong global modeling ability, they usually have large numbers of parameters and are sensitive to the amount of training data, making it difficult to balance efficiency and detail restoration in infrared imaging scenarios. To address these issues, we propose a hybrid neural network architecture for infrared image super-resolution reconstruction, termed RDSR (Residual Dual-branch Separable Super-Resolution Network), which organically integrates multi-scale depthwise separable convolutions with shifted-window self-attention. Specifically, we design a dual-branch spatial interaction module (BDSI, Dual-Branch Spatial Interaction) and a multi-scale separable spatial aggregation module (MSSA, Multi-Scale Separable Spatial Aggregation). The BDSI module models correlations along rows and columns through grouped convolutions in the horizontal and vertical directions, effectively strengthening the spatial information interaction between the convolution branch and the self-attention branch. The MSSA module replaces the conventional MLP with three parallel depthwise separable convolution branches, improving the feature representation and nonlinear modeling through multi-scale spatial aggregation and a star-shaped gating operation. The experimental results on multiple public infrared image datasets show that for $\times 2$ and $\times 4$ upscaling, the proposed RDSR achieves higher PSNR and SSIM values than CNN-based methods such as EDSR, RCAN, and RDN, as well as transformer-based methods such as SwinIR, DAT, and HAT, demonstrating the effectiveness of the proposed modules and the overall framework. Full article

Mechanosensitive ion channels (MSCs) are fundamental transducers that convert mechanical forces into electrochemical signals, enabling cells to regulate processes such as Ca²⁺ homeostasis, migration, proliferation, and adhesion. Located in both plasma and organellar membranes, MSCs, including Piezos, TRPs, K2Ps, MscL, and MscS families exhibit diverse ion selectivity, gating mechanisms and physiological roles. Emerging evidence demonstrates that lipids are dynamic regulators of MSC activation, sensitivity, and kinetics. Endogenous membrane lipids such as cholesterol, phospholipids, sphingolipids and fatty acids modulate MSC behavior by altering bilayer tension, curvature, stiffness and protein–lipid interactions. Exogenous lipids, including dietary fatty acids and lipid-derived metabolites, influence MSCs by modifying membrane physical properties or engaging specific lipid-binding sites on channel proteins. These interactions shape fundamental biological processes and contribute to disease mechanisms in cardiovascular dysfunction, neurological disorders, metabolic disease, and cancer. Despite significant progress, the molecular principles by which lipids regulate MSC conformational transitions and force sensing remain incompletely defined. This review synthesizes current knowledge on endogenous and exogenous lipid modulation of MSCs, integrating structural, computational and electrophysiological insights to highlight emerging therapeutic opportunities targeting lipid–mechanotransduction interfaces. Full article

Tunnel Field-Effect Transistors (TFETs) are being explored for ultra-low-power very-large-scale integrated circuits (VLSI) because their band-to-band tunnelling (BTBT) transport permits subthreshold swings (SS) below the 60 mV/dec thermionic limit at room temperature, along with significantly lower leakage than MOSFETs. This paper presents a systematic TCAD study of DG-TFETs that maps how four primary knobs–gate dielectric materials, silicon channel thickness, temperature variation, and different channel material shape key figures of merit: the ON current (I_ON), OFF current (I_OFF), threshold voltage (V_TH), SS, and the I_ON/I_OFF switching ratio. High-k gate enhances gate-to-channel coupling and boost tunnelling efficiency; rigorous body scaling enhances electrostatic control; and targeted source-proximal doping profiles elevate I_ON while minimizing leakage. We also measure the trade-offs between I_ON, SS, and I_OFF that occur when scaling is performed at the same time. This shows that careful coordination is needed instead of just tuning one parameter. This is a simulated work, and the physical models are calibrated to experimental TFET data and all parameters are checked against previously reported results. The device reaches SS = 31.4 mV/dec, V_TH = 0.46 V, I_ON = 5.91 × 10⁻⁵ A and an I_ON/I_OFF of about 4.5 × 10¹¹. This shows that it can switch quickly with little leakage. The design insights that come from this work provide useful advice regarding how to choose gate dielectric material, structures, and doping strategies to add DG-TFETs to the next generation of low-power semiconductor technologies. Full article

Human activity recognition (HAR) lies at the core of digital healthcare applications that monitor different types of physical activity. Traditional HAR methods often struggle to adapt to variable-length, real-world activity data and to generalise across cohorts (e.g., from young to old cohorts). Thus, the aim of this study was to investigate HAR using wearable sensor data, with a particular focus on cross-cohort evaluation. Each dataset included two accelerometers (right thigh and lower back) sampling at 50 Hz, capturing a range of daily-life activities that were annotated using video recordings from chest-mounted cameras synchronised with the accelerometers. Neural networks were trained on young cohorts’ data and tested on old cohorts’ data. The effects of network architecture, sampling frequency and sensor location on classification performance were investigated. Network performance was evaluated using accuracy, recall, precision, F1-score and confusion matrices. The gated recurrent unit architecture achieved the best performance when trained solely on young cohorts’ data, with weighted F1-score of 0.95 ± 0.05 and 0.93 ± 0.05 for young and old cohorts, respectively, resulting in a highly generalizable method. Classification performance across multiple sampling frequencies was comparable. The thigh-mounted sensor consistently achieved higher performance than the lower back sensor across activities except lying. Furthermore, combining datasets significantly improved performance on the old cohort (weighted F1-score: 0.97 ± 0.02) due to increased variability in the training data. This study highlights the importance of network architecture and dataset composition in HAR and demonstrates the potential of neural networks for robust, real-world activity recognition across age-defined cohorts, specifically between young and old cohorts. Full article

Skin-lesion recognition from clinical photographs is clinically valuable yet computationally challenging due to large intra-class variation, subtle inter-class boundaries, class imbalance, and heterogeneous acquisition conditions. To address these constraints under realistic compute budgets, we investigate Inception-family convolutional baselines and propose GISLC—a Gated-Inception model that augments a GoogLeNet/Inception-V1 backbone with a lightweight, spatial gating head inspired by ConvLSTM. Unlike static fusion (concatenation/summation) of multi-branch features, the proposed gated head performs per-location, learnable regulation of feature flow across branches, prioritizing diagnostically salient patterns while suppressing redundant activations. Experiments were conducted on the clinical-images subset of the Multimodal Augmented Skin Lesion Dataset (MASLD), an augmented derivative of HAM10000, using stratified train/validation/test splits, clinically motivated augmentation, and class-weighted optimization to mitigate skewed label frequencies. A controlled ablation study evaluates backbone choices and optimization settings and isolates the contribution of gated fusion relative to standard Inception heads. Across runs, the gated fusion strategy improves discriminative performance while remaining parameter-efficient, supporting the view that spatially adaptive regulation can enhance robustness on non-dermatoscopic clinical imagery. We further outline practical steps for calibration analysis and compression-aware deployment in clinical and edge settings. Full article

Retrieval-time augmentation can correct hard test samples but may also introduce harmful interference when retrieved neighbors are unreliable. We propose TDI-SF (trustworthy dynamic inference via similarity-gated strict fallback), a safety-oriented dynamic inference strategy that intervenes only when needed and falls back to a frozen baseline when retrieval quality is insufficient. Uncertainty-gated selective retrieval triggers on a hard subset, defined by high entropy or low margin predictions ($q=0.3$), and similarity-gated fusion weights neighbor evidence by maximum similarity with a strict fallback threshold (alpha-mode=maxsim, min_maxsim). We evaluate on ImageNet-100 (ResNet-50) and CICIDS2017 (MLP) and report overall accuracy, hard-subset accuracy, calibration, negative flips, and risk–coverage behavior alongside efficiency. Comprehensive evaluation under both clean and degraded retrieval conditions demonstrates the value of each component. On ImageNet-100, TDI-SF improves hard-subset accuracy by 0.92% and overall accuracy by 0.30%, applying retrieval to only 32.6% of samples with 1.38 ms overhead per triggered sample. On CICIDS2017, the same mechanism yields +1.30% hard-subset gains with only 0.43 ms/hard overhead. These results show a simple, auditable recipe for safer retrieval-augmented inference across heterogeneous domains. Full article

Identity-based communities that share common characteristics, beliefs, and experiences (e.g., Black LGBTQ+ communities) have historically been conceptualized as protective bubbles that buffer Black LGBTQ+ individuals against the deleterious effects of systemic racism and cisheterosexism. However, this monolithic narrative often masks the internal power dynamics that divide belonging. This study explores the exclusionary dynamics embedded within these safe spaces, examining how internal hierarchies of skin tone, socioeconomic status, and gender performance function as proximal stressors. Guided by a critical constructivist paradigm, this study utilized Reflexive Thematic Analysis to analyze open-ended survey responses from 74 Black LGBTQ+ adults. Data were drawn from a larger mixed-methods study and analyzed using a six-phase recursive process to identify latent patterns of intragroup gatekeeping. The analysis revealed that the sanctuary of the community is restricted. Three primary themes emerged: (1) Phenotypic Capital and the Politics of Authenticity, where lighter skin tone triggered authenticity scrutiny and darker skin tone faced rejection based on physical appearance; (2) Socioeconomic Gatekeeping, where belonging was stratified by the cost of participation and protective insularity within working-class spaces; and (3) Policing the Binary, where rigid adherence to gender archetypes created a landscape of performance surveillance. Access to community resilience is not a universal right but a negotiated status contingent upon the payment of a resilience tax. To promote genuine health equity, researchers and practitioners working with this population must move beyond the uncritical referral to “community” and actively dismantle the internalized systems of oppression that fracture collective survival. Full article

Remote sensing-based landslide segmentation is of great significance for geological hazard assessment and post-disaster rescue. Existing convolutional neural network methods, constrained by the inherent limitations of spatial convolution, tend to lose high-frequency edge details during deep semantic extraction, while frequency-domain analysis, although capable of globally preserving high-frequency components, struggles to perceive local multi-scale features. The lack of an effective synergistic mechanism between them makes it difficult for networks to balance regional integrity and boundary precision. To address these issues, this paper proposes the Geometric Context and Frequency Domain Fusion Network (GCF-Net), which achieves explicit edge enhancement through a three-stage progressive framework. First, the Pyramid Lightweight Fusion (PGF) block is proposed to aggregate multi-scale context and provide rich hierarchical features for subsequent stages. Second, the Geometric Context and Frequency Domain Fusion (GCF) module is designed, where the frequency-domain branch generates dynamic high-frequency masks via the Fourier transform to locate boundary positions, while the spatial branch models foreground–background relationships to understand boundary semantics, with both branches fused through an adaptive gating mechanism. Finally, Edge-aware Detail Consistency Improvement (EDCI) module is designed to balance boundary preservation and noise suppression based on edge confidence, achieving adaptive output refinement. Under the joint supervision of Focal loss, Dice loss, and Edge loss, experiments on the mixed dataset and LMHLD dataset demonstrate that GCF-Net achieves OAs of 96.42% and 96.71%, respectively. Ablation experiments and visualization results further validate the effectiveness of each module and the significant improvement in boundary segmentation. Full article