Browse Articles

Integrated TE optical isolator based on magneto-optical perturbation in coupled waveguides

Abstract Photonic integrated circuits (PICs) increasingly require more advanced integrated functionalities and devices to meet the key application challenges. This evolution with the serial integration of optical functions in the same photonic circuit often results in internal reflections and optical feedback, which can specifically destabilize lasers. One solution to overcome this issue is to integrate an optical isolator at the output of the lasers. Among various proposed designs, magneto-optical-based isolators have significantly improved over the past decades in terms of compactness, insertion loss, isolation ratio, and spectral isolation bandwidth. Despite these improvements, the TE optical isolator still lacks performance. This paper introduces a new operational principle for a TE optical isolator based on modal beating in a transverse magneto-optical Kerr effect (TMOKE) coupled-waveguide system. This approach combines evanescent-coupled silicon waveguides and the magneto-optical effect, resulting in a nonreciprocal propagation that can be optimized for optical isolation. This new concept shows promise in achieving a high-performing TE optical isolator, as it does not depend on resonance and is free from constraints associated with interferometers. Based on data from magneto-optical garnet materials, the simulated device has a length of 500  $$\upmu \textrm{m}$$ and its 20 dB-isolation bandwidth is as high as 35 nm. With broadband and high isolation, this simulated device opens possibilities for miniaturizing complex photonic circuits used in optical communication, data communication, and optical sensing.

Research on the simulation methods and shock resistance performance of energy absorbing columns in rockburst roadway supports

Setdb1 ablation in macrophages attenuates fibrosis in heart allografts

Tissue fibrosis is commonly associated with organ malfunction and is strongly associated with the development of chronic rejection, cardiovascular diseases, and other chronic diseases. Fibrosis also contributes to immune exclusion in tumor tissues. Targeting fibrosis might be a strategy for prolonging allograft survival while suppressing cancer development. Here, single-cell transcriptomes of human and mouse heart allografts showed that macrophages accumulated in grafts with fibrosis were reprogrammed via histone methylation regulated by Setdb1, an H3K9 methyltransferase. Myeloid-specific deletion of Setdb1 prolonged heart allograft survival but reversed immune exclusion in tumor tissues. Interestingly, myeloid-specific Setdb1-knockout led to lower fibrosis in heart allografts and tumor tissues in mice. Our single-cell sequencing data showed that Setdb1 ablation impaired Fn1 + and SPP1 + profibrogenic macrophage reprogramming. Mechanistically, Fn1, which was induced by the CCR2-Creb/Setdb1 axis, upregulated the expression of genes related to fibrosis in fibroblasts and macrophages via ITGA5 and PIRA receptors. Blocking the interaction between FN1 and these receptors inhibited fibrosis in allograft and tumor tissues. Our results reveal a target, histone methylation in macrophages, for the treatment of fibrosis-related disease.

The burden of age-related macular degeneration and its socioeconomic associates in the Eastern Mediterranean Region from 1990 to 2021

Compound climate events and their role in the decay of Greece’s cultural heritage

Synergistic effect of Agrococcus and Rossellomorea Marisflavi species assisted probiotic functional feed on Vibrio affected Nile tilapia fish

Characterizing full-shift worker exposures to VOCs in small-sized auto repair shops in the Tucson, Arizona, USA metropolitan area

Survival benefits of primary tumor resection in metastatic differentiated thyroid cancer: an analysis of SEER data

Multi-objective artificial-intelligence-based parameter tuning of antennas using variable-fidelity machine learning

Abstract Multi-objective optimization (MO) is an important topic in contemporary antenna design. Due to the reliance on computationally-expensive electromagnetic (EM) simulations, the use of conventional algorithms is prohibitive. These costs can be reduced by appropriate algorithmic tools involving surrogate modeling and soft computing methods. This study introduces an innovative artificial intelligence (AI)-based approach to antenna MO. Our algorithm is a machine learning (ML) procedure employing artificial neural network models. In each iteration, multiple infill vectors are produced, using Pareto ranking of the candidate solution set produced by a multi-objective evolutionary algorithm. The full-wave simulation results acquired for all infill points are incorporated into the dataset to refine the metamodel. Termination of the procedure is based on a comparison of non-dominated solutions obtained in subsequent iterations. Additional reduction of the expenses is enabled through the use of multi-resolution electromagnetic simulations. The presented methodology has been extensively demonstrated with the help of four planar devices, including broadband monopoles and a quasi-Yagi antenna. As shown, the average cost of MO is equivalent to approximately two hundred high-fidelity EM analyses. In absolute terms, 40% of relative speedup is achieved due to variable-fidelity modeling, and almost 90% savings over the one-shot approach. Comparative experiments indicate that the improved computational efficiency of the presented framework is not detrimental to reliability. Consequently, the introduced algorithm can be regarded a feasible alternative to the current MO methodologies for antennas, especially when computational budget is a critical constraint.

Effects of 8-week complex and resistance training on strength and power in adolescent long jumpers

Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems

Repeated occurrences of marine anoxia under high atmospheric O ₂ and icehouse conditions

The Late Paleozoic Ice Age (~340 to 260 Ma) occurred under peak atmospheric O 2 (1.2 to 1.7 PIAL, pre-industrial atmospheric levels) for Earth history and CO 2 concentrations comparable to those of the preindustrial to that anticipated for our near future. The evolution of the marine redox landscape under these conditions remains largely unexplored, reflecting that oceanic anoxia has long been considered characteristic of carbon cycle perturbation during greenhouse times. Despite elevated O 2 , a 10 5 -y period of CO 2 -forced oceanic anoxia was recently identified, but whether this short-term interval of widespread oceanic anoxia was anomalous during this paleo-ice age is unexplored. Here, we investigate these issues by building a high-resolution record of carbonate uranium isotopes (δ 238 U carb ) from an open-marine succession in South China that permits us to reconstruct the global marine redox evolution through the deep glacial interval (310 to 290 Ma) of near peak O 2 . Our data reveal repeated, short-term decreases in δ 238 U carb coincident with negative C isotopic excursions and rises in paleo-CO 2 , all superimposed on a longer-term rise in δ 238 U carb . A carbon–phosphorus–uranium biogeochemical model coupled with Bayesian inversion is employed to quantitatively explore the interplay between marine anoxia, carbon cycling, and climate evolution during this paleo-glacial period. Although our results indicate that protracted, enhanced organic carbon burial can account for the long-term O 2 increase, seafloor oxygenation, and overall low CO 2 , episodic pulses of C emissions had the potential to drive recurring short-term periods of marine anoxia (with 4 to 12% of seafloor anoxia) despite up to 1.7 times higher atmospheric O 2 than present day.

Fast charging coordination for electric vehicles in a charging station based on heuristics and metaheuristics

A dual-domain perception gate-controlled adaptive fusion algorithm for road crack detection

Multiple subcortical and subcortico–cortico dynamic network reconfigurations characterize focal-to-bilateral tonic–clonic seizures

A novel decision-making approach for the selection of best deep learning techniques under logarithmic fractional fuzzy set information

Investigation of mechanical and metallurgical properties of commercially pure titanium grade 2 tube-to-tubesheet joints

Multiphysics coupling analysis and structure optimization of flux switching permanent magnet linear motors

Optimization of thermal barrier coating with induced copper oxide nanoparticles in CI engine using algae methyl ester as fuel

Morphometric analysis of rat and mouse musculoskeletal tissues using high field MRI

Abstract The knee is a complex articulating joint composed of bones and fibrous connective tissues with anatomy retained across species including humans, pigs, dogs, rats, and mice. Imaging developments in high field magnetic resonance imaging (MRI) has enabled non-destructive 3D structural analysis of small animal joints to further these preclinical models. The goal of this work was to apply MRI techniques for rodent knee joints using a high field MRI scanner and to characterize the morphometry of the four primary ligaments and medial and lateral menisci. Briefly, female rat and mouse knees were imaged in a 9.4T MRI scanner and the cross-sectional area (CSA) of the ligaments and the meniscal heights and widths were recorded. Tissue dependent relationships were observed in the rat and mouse ligaments. The PCL was the largest ligament in the rats with a CSA of 0.35 ± 0.08 mm 2 , while the LCL was the largest ligament in the mice, with a CSA of 0.054 ± 0.017 mm 2 . Rat and mouse meniscal width had an anatomical location dependent relationship, while meniscal height did not. This will support future work exploring morphometric effects due to aging, injury, and disease in preclinical animal models.