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Molecularly resolved mapping of heterogeneous ice nucleation and crystallization pathways using in-situ cryo-TEM
Applications of bamboo fiber and bamboo stem ash with styrene butadiene rubber in cement mortar for sustainable structural application
Abstract In several developing countries, rapid population growth has led to a shortage of adequate housing. This issue is further intensified by the overuse of traditional construction materials and the environmental concerns linked to their production, particularly the high levels of carbon dioxide emissions. Consequently, the search for environmentally sustainable alternatives has gained momentum. Bamboo, a traditional building resource, has re-emerged as a promising material due to its potential application in construction. This study explored the replacement of Ordinary Portland Cement (OPC) in mortar with bamboo stem ash and alkali-treated bamboo fibers. Various mix ratios were developed and analyzed using the Taguchi method, and the results were validated through analysis of variance (ANOVA), microstructural observations, and energy efficiency evaluation. The optimized mixes demonstrated notable improvements compared to conventional mortar, with an increase in compressive strength of up to 98.42%, flexural strength gains of up to 24%, a 21.7% decrease in dry density, and water absorption limited to 1.52%. Important characteristics of mortar mixes reinforced with bamboo fibers, including as compressive strength, flexural strength, water absorption, and dry density, were efficiently evaluated using the Taguchi technique and is recommend for sustainable bamboo reinforced wall panels.
DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge
Abstract Diffusion-weighted MRI is critical for diagnosing and managing ischemic stroke, but variability in images and disease presentation limits the generalizability of AI algorithms. We present DeepISLES, a robust ensemble algorithm developed from top submissions to the 2022 Ischemic Stroke Lesion Segmentation challenge we organized. By combining the strengths of best-performing methods from leading research groups, DeepISLES achieves superior accuracy in detecting and segmenting ischemic lesions, generalizing well across diverse axes. Validation on a large external dataset (N = 1685) confirms its robustness, outperforming previous state-of-the-art models by 7.4% in Dice score and 12.6% in F1 score. It also excels at extracting clinical biomarkers and correlates strongly with clinical stroke scores, closely matching expert performance. Neuroradiologists prefer DeepISLES’ segmentations over manual annotations in a Turing-like test. Our work demonstrates DeepISLES’ clinical relevance and highlights the value of biomedical challenges in developing real-world, generalizable AI tools. DeepISLES is freely available at https://github.com/ezequieldlrosa/DeepIsles.
Manufacturing of high-quality green CSA-supported OPC cement with optimum physical and mechanical properties
Abstract In the current study, we aim to enhance the physical and mechanical properties of the ordinary Portland cement (OPC) by incorporating small amounts of calcium sulfoaluminate (CSA) at a rate of 0, 1, 2 and 3% (mixtures A, B, C, and D) to improve its physical and mechanical properties. These mixtures allowed the CSA clinker to emit less CO2 and clink at a low temperature of 1250 °C, which is significantly lower than that of the OPC clinker (1450 °C). The apparent density, specific gravity, porosity, and unconfined compressive strength (UCS) of the pure OPC (0%, mixture A) and the OPC-CSA pastes (B, C, & D mixtures) were measured at curing periods of 1, 3, 7, 28, and 90 days. The scanning electron microscopy (SEM) coupled with the energy dispersive X-ray (EDS) was employed to investigate the internal structure of these pastes, while the XRF technique was employed to determine their chemical composition. Additionally, the optical characteristics of the formulated and disintegrated phases were delineated using FTIR spectroscopy, while the differential thermal analysis (DTA) and thermogravimetric analysis (TGA) were employed to examine them as the curing age increased. The mixture C (2% CSA + 98% OPC) sintered at 90 days is characterized by the best physical and mechanical properties (compressive strength = 52.5 MPa, porosity = 2.86%, apparent density = 2.015 g/cc, and specific gravity = 2.405 g/cc). This procedure is beneficial on a laboratory scale and applicable on the industrial scale to improve the physical and mechanical characteristics of the OPC and decrease its energy consumption.
Lattice-matched antiperovskite-perovskite system toward all-solid-state batteries
Prognostic comparison of partial and radical nephrectomy for T1b renal cell carcinoma with SEER database analysis
Cell free RNA detection of pancreatic cancer in pre diagnostic high risk and symptomatic patients
Artificial intelligence with feature fusion empowered enhanced brain stroke detection and classification for disabled persons using biomedical images
Abstract Brain stroke is an illness which affects almost every age group, particularly people over 65. There are two significant kinds of strokes: ischemic and hemorrhagic strokes. Blockage of brain vessels causes an ischemic stroke, while cracks in blood vessels in or around the brain cause a hemorrhagic stroke. In the prompt analysis of brain stroke, patients can live an easier life. Recognizing strokes using medical imaging is crucial for early diagnosis and treatment planning. Conversely, access to innovative imaging methods is restricted, particularly in emerging states, so it is challenging to analyze brain stroke cases of disabled people appropriately. Hence, the development of more accurate, faster, and more reliable diagnostic models for the timely recognition and efficient treatment of ischemic stroke is greatly needed. Artificial intelligence technologies, primarily deep learning (DL), have been widely employed in medical imaging, utilizing automated detection methods. This paper presents an Enhanced Brain Stroke Detection and Classification using Artificial Intelligence with Feature Fusion Technologies (EBSDC-AIFFT) model. This paper aims to develop an enhanced brain stroke detection system for individuals with disabilities, utilizing biomedical images to improve diagnostic accuracy. Initially, the image pre-processing stage involves various steps, including resizing, normalization, data augmentation, and data splitting, to enhance image quality. In addition, the EBSDC-AIFFT model combines the Inception-ResNet-v2 model, the convolutional block attention module-ResNet18 method, and the multi-axis vision transformer technique for feature extraction. Finally, the variational autoencoder (VAE) model is implemented for the classification process. The performance validation of the EBSDC-AIFFT technique is performed under the brain stroke CT image dataset. The comparison study of the EBSDC-AIFFT technique demonstrated a superior accuracy value of 99.09% over existing models.
Single-molecule tweezers decode hidden dimerization patterns of membrane proteins within lipid bilayers
Design, analysis and experiment of a novel repeatable buffer landing mechanism
Mitochondrial damage triggers the concerted degradation of negative regulators of neuronal autophagy
Abstract Mutations that disrupt the clearance of damaged mitochondria via mitophagy are causative for neurological disorders including Parkinson’s. Here, we identify a Mitophagic Stress Response (MitoSR) activated by mitochondrial damage in neurons and operating in parallel to canonical Pink1/Parkin-dependent mitophagy. Increasing levels of mitochondrial stress trigger a graded response that induces the concerted degradation of negative regulators of autophagy including Myotubularin-related phosphatase (MTMR)5, MTMR2 and Rubicon via the ubiquitin-proteasome pathway and selective proteolysis. MTMR5/MTMR2 inhibit autophagosome biogenesis; consistent with this, mitochondrial engulfment by autophagosomes is enhanced upon MTMR2 depletion. Rubicon inhibits lysosomal function, blocking later steps of neuronal autophagy; Rubicon depletion relieves this inhibition. Targeted depletion of both MTMR2 and Rubicon is sufficient to enhance mitophagy, promoting autophagosome biogenesis and facilitating mitophagosome-lysosome fusion. Together, these findings suggest that therapeutic activation of MitoSR to induce the selective degradation of negative regulators of autophagy may enhance mitochondrial quality control in stressed neurons.
Surrogate-assisted optimization of roll-to-roll slot die coating
Abstract Roll-to-roll slot die coating is a key wet processing technique, where achieving a specific thickness with minimal variability is crucial. However, the numerous input parameters make optimization complex. Despite its advanced applications, computer-aided optimization remains underutilized, leaving potential performance improvements untapped. Due to the lack of accurate first-principle models, machine learning offers a promising approach. This study employs Radial Basis Function Neural Networks as surrogate models trained on experimental data to optimize roll-to-roll slot die coating. These models predict coating thickness and uniformity with mean absolute errors below 11.5 %. Key process parameters are identified, with shim thickness and substrate velocity having the greatest impact on coating uniformity, while coating gap played a lesser role. An evolutionary optimization approach identified new operating parameters, leading to improved coating properties. Experimentally, these optimized conditions achieved the five lowest recorded uniformity values and increased the hyper-volume fraction from 0.68 to 0.84. Some prediction inconsistencies were observed, likely due to the high sensitivity of lab-scale equipment, which is expected to improve at an industrial scale. This work paves the way for wider adoption of machine learning and accurate metrology in slot die coating.
High-dimensional one-shot optical field compressive sensing of structured light
Analysis of gas bubbling dynamics via lie symmetry approach of nonlinear wave equation
Abstract Bubbles formed by the introduction of gas into a liquid are a common phenomenon, known as gas bubbling in liquids. This process is widely utilized in various industries for aeration, mixing, and purification. An optimal system of Lie symmetry analysis is employed to investigate the generalized (3 + 1)-dimensional nonlinear wave equation (NLWE). Single, double, triple, and quadruple linear combinations are constructed to derive novel solutions that represent different dynamic and turbulent behaviors of the bubbles. This equation models a wide range of nonlinear phenomena occurring in liquids containing gas bubbles. The proposed methodology is used to obtain a diverse set of accurate soliton solutions to the equation. Furthermore, the resulting solutions are analyzed in terms of their physical interpretations.
Guanidine aptamers are present in vertebrate RNAs associated with calcium signaling and neuromuscular function
Abstract Guanidine is a protein denaturant that is a widely used constituent in explosives, plastics, and resins. Its effects on muscle contraction were initially reported in 1876, which eventually led to the use of guanidine as a treatment for certain ataxia symptoms such as those caused by Lambert-Eaton disease. However, its mechanisms of therapeutic action remained unknown. Guanidine was recently found to be a widespread natural metabolite through the discovery of four bacterial riboswitch classes that selectively recognize this compound. Here, we report the discovery and biochemical validation of vertebrate members of guanidine-I and -II riboswitch aptamer classes that are associated with numerous genes relevant to neuromuscular function, mostly involved in Ca2+ transport or signaling. These findings suggest that guanidine is a widely used signaling molecule that serves as an additional layer of regulation of genes relevant to neuromuscular disorders.
An enhanced fruit fly optimization algorithm with random spare and double adaptive weight strategies for oil and gas production optimization
Abstract In the field of petroleum extraction, enhancing oil and gas recovery processes is essential for sustaining the economic viability of energy enterprises and addressing the continuously increasing global energy demand. Efficient subsurface production plays a pivotal role in strategic decision-making, including the selection of optimal drilling sites and the determination of effective well control parameters. However, conventional reservoir optimization techniques are often computationally intensive and may struggle to deliver satisfactory solutions. As a promising alternative, evolutionary algorithms—rooted in the principles of natural selection—have demonstrated strong potential for addressing complex optimization problems due to their gradient-free nature and inherent suitability for parallel computation. In this study, we propose an enhanced evolutionary algorithm tailored for global optimization and oil and gas production improvement. This method builds upon the original Fruit Fly Optimization Algorithm (FOA) by incorporating a random spare mechanism and a dual adaptive weighting scheme, aiming to achieve a more effective balance between exploration and exploitation during the search process. Specifically, after the standard FOA updates the population, the random spare mechanism is introduced to enhance exploratory capabilities and avoid premature convergence. Subsequently, the dual adaptive weighting strategy is employed to improve convergence speed and solution refinement. The proposed RDFOA algorithm is rigorously validated through comprehensive experiments on benchmark test suites from IEEE CEC 2017 and IEEE CEC 2022. These evaluations include ablation studies, scalability analyses, visualization of search trajectories, and comparative assessments against state-of-the-art algorithms. On the CEC 2017 benchmark, RDFOA outperforms CLACO in 17 functions and QCSCA in 19 functions. On the CEC 2022 benchmark, it surpasses CCMSCSA and HGWO in 10 functions. The experimental results clearly demonstrate that RDFOA consistently achieves superior performance in oil and gas production optimization scenarios.