Search Results (2,318)

Arrhythmogenic cardiomyopathies (ACMs) are a phenotypically and etiologically heterogeneous group of myocardial disorders characterized by fibrotic or fibro-fatty replacement of ventricular myocardium, electrical instability, and an elevated risk of sudden cardiac death. Initially identified as a right ventricular disease, ACMs are now recognized to include biventricular and left-dominant forms. Genetic causes account for a substantial proportion of cases and include desmosomal variants, non-desmosomal variants, and familial gene-elusive forms with no identifiable pathogenic mutation. Nongenetic etiologies, including post-inflammatory, autoimmune, and infiltrative mechanisms, may mimic the phenotype. In many patients, the disease remains idiopathic despite comprehensive evaluation. Cardiac magnetic resonance imaging has emerged as a key tool for identifying non-ischemic scar patterns and for distinguishing arrhythmogenic phenotypes from other cardiomyopathies. Emerging classifications propose the unifying concept of scarring cardiomyopathies based on shared structural substrates, although global consensus is evolving. Risk stratification remains challenging, particularly in patients without overt systolic dysfunction or identifiable genetic markers. Advances in tissue phenotyping, multi-omics, and artificial intelligence hold promise for improved prognostic assessment and individualized therapy. Full article

Indonesia’s worsening air pollution and traffic emissions have thrust electric vehicles (EVs) into the spotlight, but what really drives Indonesians to make the switch? This study integrates Protection Motivation Theory with green branding and policy frameworks to explain electric vehicle (EV) adoption in Indonesia. Using a nationwide survey (n = 986) and partial-least-squares structural-equation modeling, we test how environmental awareness, consumer expectancy, threat appraisal, and coping appraisal shape adoption both directly and through green brand image (GBI), while perceived policy incentives moderate the GBI–adoption link. The model accounts for 54% of the variance in adoption intention. These findings highlight that combining public awareness campaigns, compelling green brand messaging, and carefully calibrated policy incentives is essential for accelerating Indonesia’s transition to cleaner transport. Full article

This work presents an innovative hydrothermal approach for fabricating flexible piezoelectric PZT thin films on 20 μm titanium foil substrates using TiO₂ and SrTiO₃ (STO) interlayers. Three heterostructures (Ti/PZT, Ti/TiO₂/PZT, and Ti/TiO₂/STO/PZT) were synthesized to enable low-temperature growth and improve ferroelectric performance for advanced flexible MEMS. Characterizations including XRD, PFM, and P–E loop analysis evaluated crystallinity, piezoelectric coefficient d₃₃, and polarization behavior. The results demonstrate that the multilayered Ti/TiO₂/STO/PZT structure significantly enhances performance. XRD confirmed the STO buffer layer effectively reduces lattice mismatch with PZT to ~0.76%, promoting stable morphotropic phase boundary (MPB) composition formation. This optimized film exhibited superior piezoelectric and ferroelectric properties, with a high d₃₃ of 113.42 pm/V, representing an ~8.65% increase over unbuffered Ti/PZT samples, and displayed more uniform domain behavior in PFM imaging. Impedance spectroscopy showed the lowest minimum impedance of 8.96 Ω at 10.19 MHz, indicating strong electromechanical coupling. Furthermore, I–V measurements demonstrated significantly suppressed leakage currents in the STO-buffered samples, with current levels ranging from 10⁻¹² A to 10⁻⁹ A over ±3 V. This structure also showed excellent fatigue endurance through one million electrical cycles, confirming its mechanical and electrical stability. These findings highlight the potential of this hydrothermally engineered flexible heterostructure for high-performance actuators and sensors in advanced MEMS applications. Full article

Novel high-entropy perovskite oxide powders were synthesized using a sol-gel process. The B-site contained five cations: chromium, cobalt, iron, manganese, and nickel. The B-site cations were present on an equiatomic basis. The A-site cation was lanthanum, with calcium doping. The amount of A-site doping varied from 0 to 30 at%, yielding a composition of La_1−xCa_x(Co_0.2Cr_0.2Fe_0.2Mn_0.2Ni_0.2)O_3−δ. The resulting perovskite powders were pressurelessly sintered in air at 1400 °C for 2 h. Sintered densities were measured, and the grain structure was imaged via scanning electron microscopy to investigate the effect of doping. Samples were cut and polished, and their resistance was measured at varying temperatures in air to obtain the electrical conductivity and the mechanism that governs it. Plots of electrical conductivity as a function of composition and temperature indicate that the increased configurational entropy of the perovskite materials has a demonstrable effect. Full article

Colorectal liver metastasis (CRLM) poses a significant challenge in oncology due to its high incidence and poor prognosis in unresectable cases. Current treatments, including surgical resection, systemic chemotherapy, and liver-directed therapies, often fail to effectively target hypoxic tumor regions, which are inherently more resistant to these interventions. This review examines the potential of a novel therapeutic strategy combining irreversible electroporation (IRE) ablation and Clostridium novyi-nontoxic (C. novyi-NT) bacterial therapy. IRE is a non-thermal tumor ablation technique that uses high-voltage electric pulses to create permanent nanopores in cell membranes, leading to cell death while preserving surrounding structures, and is often associated with temporary tumor hypoxia due to disrupted perfusion. C. novyi-NT is an attenuated, anaerobic bacterium engineered to selectively germinate and proliferate in hypoxic tumor regions, resulting in localized tumor cell lysis while sparing healthy, oxygenated tissue. The synergy between IRE-induced hypoxia and hypoxia-sensitive C. novyi-NT may enhance tumor destruction and stimulate systemic antitumor immunity. Furthermore, the integration of advanced imaging and artificial intelligence can support precise treatment planning and real-time monitoring. This integrated approach holds promise for improving outcomes in patients with CRLM, though further preclinical and clinical validation is needed. Full article

Vacuum degassing (VD) is a critical refining step in electric arc furnace (EAF) steelmaking for producing clean steel with reduced nitrogen and hydrogen content. This study develops an Effective Equilibrium Reaction Zone (EERZ) model focused on denitrogenation (de-N) by simulating interfacial reactions at the bubble–steel interface (Z1). The model incorporates key process parameters such as argon flow rate, vacuum pressure, and initial nitrogen and sulfur concentrations. A robust empirical correlation was established between de-N efficiency and the mass of Z1, reducing prediction time from a day to under a minute. Additionally, the model was further improved by incorporating a dynamic surface exposure zone (Z_eye) to account for transient ladle eye effects on nitrogen removal under deep vacuum (<10 torr), validated using synchronized plant trials and Python-based video analysis. The integrated approach—combining thermodynamic-kinetic modeling, plant validation, and image-based diagnostics—provides a robust framework for optimizing VD control and enhancing nitrogen removal control in EAF-based steelmaking. Full article

The high resolution of the Synthetic Aperture Radar (SAR) imagery, in addition to its capability to see through clouds and rain, makes it a crucial remote sensing technique. However, SAR images are very sensitive to radar parameters, the observation geometry and the scene’s characteristics. Moreover, for a complex scene of interest with targets located on a rough soil, a composite scattering between the target and the surface occurs and creates distortions on the SAR image. These characteristics can make the SAR images difficult to analyse and process. To better understand the complex EM phenomena and their signature in the SAR image, we propose a methodology to generate raw SAR signals and SAR images for scenes of interest with a target located on a rough surface. With this prospect, the entire radar acquisition chain is considered: the sensor parameters, the atmospheric attenuation, the interactions between the incident EM field and the scene, and the SAR image formation. Simulation results are presented for a rough dielectric soil and a canonical target considered as a Perfect Electric Conductor (PEC). These results highlight the importance of the composite scattering signature between the target and the soil. Its power is 21 dB higher that that of the target for the target–soil configuration considered. Finally, these simulations allow for the retrieval of characteristics present in actual SAR images and show the potential of the presented model in investigating EM phenomena and their signatures in SAR images. Full article

The scattering interaction between a circularly polarized Bessel pincer light-sheet beam and a chiral particle is investigated within the framework of generalized Lorenz–Mie theory (GLMT). The incident electric field distribution is rigorously derived via the vector angular spectrum decomposition method (VASDM), with subsequent determination of the beam-shape coefficients (BSCs) $p_{mn}^u$ and $q_{mn}^u$ through multipole expansion in the basis of vector spherical wave functions (VSWFs). The expansion coefficients for the scattered field ($A_{mn}^s$$B_{mn}^s$) and interior field ($A_{mn}$$B_{mn}$) are derived by imposing boundary conditions. Simulations highlight notable variations in the scattering field, near-surface field distribution, and far-field intensity, strongly influenced by the dimensionless size parameter $ka$, chirality $\kappa$, and beam parameters (beam order l and beam scaling parameter ${\alpha }_0$). These findings provide insights into the role of chirality in modulating scattering asymmetry and localization effects. The results are particularly relevant for applications in optical manipulation and super-resolution imaging in single-molecule microbiology. Full article

Blood is a widely used sample for diagnosing diseases such as malaria and diabetes. While diagnostic techniques have advanced, sample preparation remains labor-intensive, requiring steps like mixing and centrifugation. Microfluidic technologies have automated parts of this process, including cell lysis, yet challenges persist. Electrical lysis offers a chemical-free, continuous approach, but lysing small cells like red blood cells requires high electric fields, which can damage electrodes and cause system failures. Here, we present a microfluidic device utilizing ion concentration polarization (ICP) for rapid blood cell lysis at 75 V. Fluorescence imaging confirmed the formation of an ion depletion region near the Nafion^® nanochannel membrane, where the electric field was concentrated across the entire microchannel width. This phenomenon enabled the efficient trapping and lysis of blood cells under these conditions. Continuous blood injection achieved a lysis time of 0.3 s with an efficiency exceeding 99.4%. Moreover, lysed cell contents accumulated near the Nafion membrane, forming a concentrated lysate. This approach eliminates the need for high-voltage circuits or chemical reagents, offering a simple yet effective method for blood cell lysis. The proposed device is expected to advance lab-on-a-chip and point-of-care diagnostics by enabling rapid and continuous sample processing. Full article

This study addresses the limitations of conventional evaluation methods caused by low porosity, strong heterogeneity, and complex pore structures in tight sandstone reservoirs. Through integrated rock physics experiments and multi-physical field modeling, the research systematically investigates the coupled response mechanisms between electrical and elastic parameters. The experimental approach includes pore structure characterization, quantitative mineral composition analysis, resistivity and polarizability measurements under various saturation conditions, P- and S-wave velocity testing, and scanning electron microscopy (SEM) imaging. The key findings show that increasing porosity leads to significant reductions in resistivity and elastic wave velocities, while also increasing surface conductivity. Specifically, clay minerals enhance surface conductivity through interfacial polarization effects and decrease rock stiffness, which exacerbates wave velocity attenuation. Furthermore, resistivity exhibits a nonlinear negative correlation with water saturation, with sharp increases at low saturation levels due to the disruption of conductive pathways. By integrating the Modified Generalized Effective Medium Theory of Induced Polarization (MGEMTIP) and Kuster–Toksöz models, this study establishes quantitative relationships between porosity, saturation, and electrical/elastic parameters, and constructs cross-plot templates that correlate elastic wave velocities with resistivity and surface conductivity. These analyses reveal that high-porosity, high-saturation zones are characterized by lower resistivity and wave velocities, coupled with significantly higher surface conductivity. The proposed methodology significantly improves the accuracy of reservoir evaluation and enhances fluid identification capabilities, providing a solid theoretical foundation for the efficient exploration and development of tight sandstone reservoirs. Full article

Herein, electrospun nanofibers composed of polyaniline (PANI), polyethylene oxide (PEO), and Luffa cylindrica (LC) cellulose, reinforced with titanium dioxide (TiO₂) nanoparticles, were synthesized via electrospinning to investigate the effect of TiO₂ nanoparticles on PANI/PEO/LC nanocomposites and the effect of conductivity on nanofiber morphology. Cellulose extracted from luffa was added to the PANI/PEO copolymer solution, and two different ratios of TiO₂ were mixed into the PANI/PEO/LC biocomposite. The morphological, vibrational, and thermal characteristics of biocomposites were systematically investigated using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). As anticipated, the presence of TiO₂ enhanced the electrical conductivity of biocomposites, while the addition of Luffa cellulose further improved the conductivity of the cellulose-based nanofibers. FTIR analysis confirmed chemical interactions between Luffa cellulose and PANI/PEO matrix, as evidenced by the broadening of the hydroxyl (OH) absorption band at 3500–3200 cm⁻¹. Additionally, the emergence of characteristic peaks within the 400–1000 cm⁻¹ range in the PANI/PEO/LC/TiO₂ spectra signified Ti–O–Ti and Ti–O–C vibrations, confirming the incorporation of TiO₂ into the biocomposite. SEM images of the biocomposites reveal that the thickness of nanofibers decreases by adding Luffa to PANI/PEO nanofibers because of the nanofibers branching. In addition, when blending TiO₂ nanoparticles with the PANI/PEO/LC biocomposite, this increment continued and obtained thinner and smother nanofibers. Furthermore, the incorporation of cellulose slightly improved the crystallinity of the nanofibers, while TiO₂ contributed to the enhanced crystallinity of the biocomposite according to the XRD and DCS results. Similarly, the TGA results supported the DSC results regarding the increasing thermal stability of the biocomposite nanofibers with TiO₂ nanoparticles. These findings demonstrate the potential of PANI/PEO/LC/TiO₂ nanofibers for advanced applications requiring conductive and structurally optimized biomaterials, e.g., for use in humidity or volatile organic compound (VOC) sensors, especially where flexibility and environmental sustainability are required. Full article

The increasing demand for high-resolution subsurface imaging has driven significant advances in geophysical inversion methodologies. Despite the availability of various software packages for electrical resistivity tomography (ERT), time-domain induced polarization (TDIP), and seismic refraction tomography (SRT), significant challenges remain in selecting optimal regularization parameters and in the effective incorporation of prior information into the inversion process. In this study, we propose new strategies to address these critical issues by developing versatile and flexible tools for electrical and seismic tomographic data inversion. Specifically, we introduce two automated procedures for regularization parameter selection: a full loop method (fixed-λ optimization) where the regularization parameter is kept constant during the inversion process, and a single-inversion approach (automaticLam) where it varies throughout the iterations. Additionally, we present a novel constrained inversion strategy that effectively balances prior information, minimizes data misfit, and promotes model smoothness. This approach is thoroughly compared with the state-of-the-art methods, demonstrating its superiority in maintaining model reliability and reducing dependence on subjective operator choices. Applications to synthetic, laboratory, and real-world case studies validate the efficacy of our strategies, showcasing their potential to enhance the robustness of geophysical models and standardize the inversion process, ensuring its independence from operator decisions. Full article

This study investigates a lithium-ion battery failure in heating insoles that ignited during normal walking while powered off. Through comprehensive material characterization, electrical testing, thermal analysis, and mechanical gait simulation, we systematically excluded electrical or thermal abuse as failure causes. X-ray/CT imaging localized the ignition source to the lateral heel edge of the pouch cell, correlating precisely with peak mechanical stress identified through gait analysis. Remarkably, the cyclic load was less than 10% of the single crush load threshold specified in safety standards. Key findings reveal multiple contributing factors as follows: the uncoated polyethylene separator’s inability to prevent stress-induced internal short circuits, the circuit design’s lack of battery health monitoring functionality that permitted undetected degradation, and the hazardous placement inside clothing that exacerbated burn injuries. These findings necessitate a multi-level safety framework for lithium-ion battery products, encompassing enhanced cell design to prevent internal short circuit, improved circuit protection with health monitoring capabilities, optimized product integration to mitigate mechanical and environmental impact, and effective post-failure containment measures. This case study exposes a critical need for product-specific safety standards that address the unique demands of wearable lithium-ion batteries, where existing certification requirements fail to prevent real-use failure scenarios. Full article

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article

As power grids scale and aging assets edge toward obsolescence, grounding grid corrosion has become a critical vulnerability. Conventional diagnosis must fit high-dimensional electrical data to a physical model, typically yielding a nonlinear under-determined system fraught with computational burden and uncertainty. We propose the Enhanced Biomimetic Hippopotamus Optimization (EBOHO) algorithm, which distills the river-dwelling hippo’s ecological wisdom into three synergistic strategies: a beta-function herd seeding that replicates the genetic diversity of juvenile hippos diffusing through wetlands, an elite–mean cooperative foraging rule that echoes the way dominant bulls steer the herd toward nutrient-rich pastures, and a lens imaging opposition maneuver inspired by moonlit water reflections that spawn mirror candidates to avert premature convergence. Benchmarks on the CEC 2017 suite and four classical design problems show EBOHO’s superior global search, robustness, and convergence speed over numerous state-of-the-art meta-heuristics, including prior hippo variants. An industrial case study on grounding grid corrosion further confirms that EBOHO swiftly resolves the under-determined equations and pinpoints corrosion sites with high precision, underscoring its promise as a nature-inspired diagnostic engine for aging power system infrastructure. Full article