Additional Branch Research Articles

An Active Damping Control Strategy for Grid-Tied Type-4 Wind Turbine Generator Based on Energy Feedback

In a grid-tied type-4 wind turbine generator (WTG) system, the frequency of sub/super-synchronous oscillation varies in a wide range, making it difficult to suppress the oscillation. In view of this problem, an active damping control strategy based on energy feedback is proposed. First, the energy balance equation of stored energy, dissipation energy, and interaction energy in a grid-tied type-4 WTG system is established. And the mathematical relationship between the increasing/decreasing trend of stored energy and the divergence/convergence of oscillation, no matter how the oscillation frequency varies, is derived. Then, by calculating the contribution degrees of different energy feedback branches to the variation rate of stored energy, the key energy feedback branches that cause the stored energy to increase are selected. And then, with the variation rate of stored energy being made negative as the control objective, this article derives a reactive current compensation function and constructs an additional energy feedback branch to eliminate the effect of key energy feedback branches, thus realizing active damping suppression of oscillations in a broad frequency band. Finally, the real-time digital simulator based hardware-in-loop test platform with a practical controller of a real 2.5-MW type-4 WTG and experimental platform are both built on which the proposed strategy is verified. Simulation results show that the proposed method can realize effective damping control of oscillations in a broad frequency range. In addition, this article is accompanied by a video demonstrating hardware-in-loop test results.

Horseshoe Kidney with Surplus Renal Arteries, Veins, and Roots of the Ureters

We report the anatomical findings of a kidney discovered in a 92‐year‐old male during a routine dissection of the abdominal viscera performed by medical students at the Oakland University William Beaumont School of Medicine. The lower poles of the kidney are fused together, forming an isthmus anterior to the aorta and inferior vena cava at the L3‐L4 vertebral level. The right and left kidneys ascend past the isthmus, forming a “U” shape, the defining feature of a horseshoe kidney. The left kidney stops prematurely, approximately 3.2 cm below the inferior mesenteric artery at the level of the left inferior renal artery. The right kidney drains into superior, inferior, and middle renal arteries which have three, two, and one branches respectively. Due to the size and position of the hilum, five separate roots extend outside the kidney into the right ureter. The left side is supplied by a superior and inferior renal artery, each dividing into two branches. The left inferior renal artery branches off the ventral face of the descending aorta. The left renal vein notably has three prominent tributaries from the kidney and an additional branch of the left adrenal vein. Six roots extend from the left hilum and drain into the left ureter. In total, the kidney has nine veins, six arteries, and eleven ureteric branches draining into the left and right ureters. These findings will be useful in regard to research and surgery on other horseshoe kidneys.

Uncertainty‐guided symmetric multilevel supervision network for 3D left atrium segmentation in late gadolinium‐enhanced MRI

Atrial fibrillation (AF) is a common arrhythmia and requires volumetric imaging to guide the therapy procedure. Late gadolinium-enhanced magnetic resonance imaging (LGE MRI) is an efficient noninvasive technology for imaging the diseased heart. Three-dimensional segmentation of the left atrium (LA) in LGE MRI is a fundamental step for guiding the therapy of patients with AF. However, the low contrast and fuzzy surface of the LA in LGE MRI make accurate and objective LA segmentation a challenge. The purpose of this study is to propose an automatic and efficient LA segmentation model based on a convolutional neural network to obtain a more accurate predicted surface and improve the LA segmentationresults. In this study, we proposed an uncertainty-guided symmetric multilevel supervision (SML) network for 3D LA segmentation in LGE MRI. First, we constructed an SML structure to combine the corresponding features from the encoding and decoding stages to learn the multiscale representation of LA. Second, we formulated the discrepancy of predictions of our model as model uncertainty. Then we proposed an uncertainty-guided objective function to further increase the segmentation accuracy on thesurface. We evaluated our proposed model on the public LA segmentation database using four universal metrics. The proposed model achieved Hausdorff Distance (HD) of 11.68 mm, average symmetric surface distance of 0.92 mm, Dice score of 0.92, and Jaccard of 0.85. Compared with state-of-the-art models, our model achieved the best HD that is sensitive to surface accuracy. For the other three metrics, our model also achieved better or comparableperformance. We proposed an efficient automatic LA segmentation model that consisted of an SML structure and an uncertainty-guided objective function. Compared to other models, we designed an additional supervision branch in the encoding stage to learn more detailed representations of LA while learning global context information through the multilevel structure of each supervision branch. To address the fuzzy surface challenge of LA segmentation in LGE MRI, we leveraged the model uncertainty to enhance the distinguishing ability of the model on the surface, thereby the predicted accuracy of the LA surface can be further increased. We conducted extensive ablation and comparative experiments with state-of-the-art models. The experiment results demonstrated that our proposed model could handle the complex structure of LA and had superior advantages in improving the segmentation performance on thesurface.

MCW-Net: Single image deraining with multi-level connections and wide regional non-local blocks

A recent line of convolutional neural network-based works has succeeded in capturing rain streaks. However, difficulties in detailed recovery still remain. In this paper, we present a multi-level connection and wide regional non-local block network (MCW-Net) to properly restore the original background textures in rainy images. Unlike existing encoder–decoder-based image deraining models that improve performance with additional branches, MCW-Net improves performance by maximizing information utilization without additional branches through the following two proposed methods. The first method is a multi-level connection that repeatedly connects multi-level features of the encoder network to the decoder network. Multi-level connection encourages the decoding process to use the feature information of all levels. In multi-level connection, channel-wise attention is considered to learn which level of features is important in the decoding process of the current level. The second method is a wide regional non-local block. As rain streaks primarily exhibit a vertical distribution, we divide the grid of the image into horizontally-wide patches and apply a non-local operation to each region to explore the rich rain-free background information. Experimental results on both synthetic and real-world rainy datasets demonstrate that the proposed model significantly outperforms existing state-of-the-art models. Furthermore, the results of the joint deraining and segmentation experiment prove that our model contributes effectively to other vision tasks.

Brown Rice Germ Integrity Identification Based on Deep Learning Network

For the quality inspection of brown rice, the segmentation of connected brown rice and the identification of germ integrity are very important. However, there is no better traditional algorithm to achieve better segmentation and recognition results. This paper improves the brown rice (BR) segmentation algorithm based on background skeleton. The candidate matching points are obtained by the background skeleton method, and the optimal matching points are found by the ant colony algorithm. Experimental results show that the proposed segmentation algorithm achieves 96% accuracy, indicating that it can effectively suppress the interference from the endosperm surface. After segmentation is complete, identification of embryo integrity is performed. Firstly, a convolutional neural network (CNN) is built to identify the germ direction; then, the germ direction is normalized; finally, an improved Inception-v3 network is built to identify the germ integrity. On the basis of the Inception-v3 network, additional branches are added to improve the detection accuracy of small objects. In addition, mutual-channel loss and mlpconv are added to enable the model to better approximate the abstraction of the latent space. The experimental results show that the comprehensive recognition accuracy of the proposed algorithm is as high as 94.83%, which is significantly higher than the current mainstream recognition algorithms.

Topography of the common fibular nerve terminal division in human foetuses.

The progress of paediatric surgery and increasingly better diagnosis of foetal defects require detailed knowledge of human developmental anatomy. Precise knowledge of the anatomy of innervation of the lower extremities corresponds to this subject and is not only cognitive but also clinically important. The end of the common fibular nerve is superficially located in the area exposed to frequent injuries as well as in the area subject to possible surgical repair procedures. The analysis was carried out on 200 human foetuses aged from the 113th day to 222nd day of foetal life. The study material is a part of local foetal collection. The study incorporated the following methods: anthropological, preparational and image acquisition which was acquired with the use of high-resolution digital camera. Statistical analysis was carried out with the use of STATISTICA package. Based on the research results the new typology of the examined nerve was determined. The head of the fibula was the criterion: (i) high division - above the head of the fibula (1%); (ii) intermediate division - at the height of the head of the fibula (34%); (iii) low division - below the head of the fibula (65%). The mathematical analysis did not reveal statistically significant bilateral and gender differences. Moreover the additional branch was observed in 30% of foetuses, regardless of age class. This branch occurred in 50% of cases in both sides of the foetus. This nerve was defined as the accessory fibular nerve (nervous fibularis/peroneus accessorius). The created unique typology of the terminal division of common fibular nerve is an important supplement to the anatomical knowledge and at the same time, due to the peripheral and superficial location of the described structures, it has a relatively high clinical significance.

Conformational preferences of triantennary and tetraantennary hybrid N-glycans in aqueous solution: Insights from 20 μs long atomistic molecular dynamic simulations

In the current study, we have investigated the conformational dynamics of a triantennary (N-glycan1) and tetraantennary (N-glycan2) hybrid N-glycans found on the surface of the HIV glycoprotein using 20 μs long all-atom molecular dynamics (MD) simulations. The main objective of the present study is to elucidate the influence of adding a complex branch on the overall glycan structural dynamics. Our investigation suggests that the average RMSD value increases when a complex branch is added to N-glycan1. However, the RMSD distribution is relatively wider in the case of N-glycan1 compared to N-glycan2, which indicates that multiple complex branches restrict the conformational variability of glycans. A similar observation is obtained from the principal component analysis of both glycans. All the puckering states (4C1 to 1C4) of each monosaccharide except mannose are sampled in our simulations, although the 4C1 chair form is energetically more favorable than 1C4. In N-glycan1, the 1–6 linkage in the mannose branch [Man(9)-α(1-6)-Man(5)] stays in the gauche-gauche cluster, whereas it moves towards trans-gauche in N-glycan2. For both glycans, mannose branches are more flexible than the complex branches, and adding a complex branch does not influence the dynamics of the mannose branches. We have noticed that the end-to-end distance of the complex branch shortens by ∼ 10 Å in the presence of another complex branch. This suggests that in the presence of an additional complex branch, the other complex branch adopts a close folded structure. All these conformational changes involve the selective formation of inter-residue and water-mediated hydrogen-bond networks.

Astrometric and Photometric Verification of Faint Blue White Dwarfs in the Gaia Catalog of nearby Stars

Abstract The Gaia catalog of nearby stars (GCNS) divided all objects with parallaxes >10 mas into GCNS-selected and GCNS-rejected 100 pc samples. Below the white dwarf (WD) sequence in the complete GCNS color–magnitude diagram (CMD), at Gabs > 14.7 + 4.7(G − RP), there appear 60 GCNS-selected faint blue white dwarfs (FBWDs). However this CMD region is also populated by 411 GCNS-rejected objects, mainly from crowded regions toward the Galactic center and the Magellanic Clouds. The WD catalog of Gentile et al. lists only 47 GCNS-selected but also 8 GCNS-rejected objects. I confirm 59 of the GCNS-selected but none of the GCNS-rejected objects as FBWDs from visual inspection and a proper motion check using additional optical sky surveys. Hence FBWDs form an additional branch in the CMD. Compared to the full GCNS-selected 100 pc sample, FBWDs have relatively high proper motions and tangential velocities. They represent interesting targets for studies of ultracool or infrared-faint, and possibly also ultramassive WDs.

The Putative Drosophila TMEM184B Ortholog Tmep Ensures Proper Locomotion by Restraining Ectopic Firing at the Neuromuscular Junction.

TMEM184B is a putative seven-pass membrane protein that promotes axon degeneration after injury. TMEM184B mutation causes aberrant neuromuscular architecture and sensory and motor behavioral defects in mice. The mechanism through which TMEM184B causes neuromuscular defects is unknown. We employed Drosophila melanogaster to investigate the function of the closely related gene, Tmep (CG12004), at the neuromuscular junction. We show that Tmep is required for full adult viability and efficient larval locomotion. Tmep mutant larvae have a reduced body contraction rate compared to controls, with stronger deficits in females. In recordings from body wall muscles, Tmep mutants show substantial hyperexcitability, with many postsynaptic potentials fired in response to a single stimulation, consistent with a role for Tmep in restraining synaptic excitability. Additional branches and satellite boutons at Tmep mutant neuromuscular junctions are consistent with an activity-dependent synaptic overgrowth. Tmep is expressed in endosomes and synaptic vesicles within motor neurons, suggesting a possible role in synaptic membrane trafficking. Using RNAi knockdown, we show that Tmep is required in motor neurons for proper larval locomotion and excitability, and that its reduction increases levels of presynaptic calcium. Locomotor defects can be rescued by presynaptic knockdown of endoplasmic reticulum calcium channels or by reducing evoked release probability, further suggesting that excess synaptic activity drives behavioral deficiencies. Our work establishes a critical function for Tmep in the regulation of synaptic transmission and locomotor behavior.

Anomalous dispersion of Lamb waves in Ge cubic crystal

The current research is concerned with the study of Lamb waves propagating in a traction-free Ge plate with its basal (0,0,1) plane coinciding with the median plane of the plate. The analysis reveals (i) additional dispersion modes stepping down below the lowest cutoff frequency and resembling the (quasi) symmetric fundamental mode; (ii) the additional low-frequency limits; and (iii) multiple crossing points at the intersections of the transonic Rayleigh branch with the higher Lamb modes. The observed phenomena indicate a substantial difference in dispersion properties of Lamb waves in Ge plates compared to those of isotropic plates, namely additional low-frequency limits, multiple crossing points, and the additional fundamental branch.

MPMR: Multi-Scale Feature and Probability Map for Melanoma Recognition.

Malignant melanoma (MM) recognition in whole-slide images (WSIs) is challenging due to the huge image size of billions of pixels and complex visual characteristics. We propose a novel automatic melanoma recognition method based on the multi-scale features and probability map, named MPMR. First, we introduce the idea of breaking up the WSI into patches to overcome the difficult-to-calculate problem of WSIs with huge sizes. Second, to obtain and visualize the recognition result of MM tissues in WSIs, a probability mapping method is proposed to generate the mask based on predicted categories, confidence probabilities, and location information of patches. Third, considering that the pathological features related to melanoma are at different scales, such as tissue, cell, and nucleus, and to enhance the representation of multi-scale features is important for melanoma recognition, we construct a multi-scale feature fusion architecture by additional branch paths and shortcut connections, which extracts the enriched lesion features from low-level features containing more detail information and high-level features containing more semantic information. Fourth, to improve the extraction feature of the irregular-shaped lesion and focus on essential features, we reconstructed the residual blocks by a deformable convolution and channel attention mechanism, which further reduces information redundancy and noisy features. The experimental results demonstrate that the proposed method outperforms the compared algorithms, and it has a potential for practical applications in clinical diagnosis.

CCAD-Net: A Cascade Cloud Attribute Discrimination Network for Cloud Genera Segmentation in Whole-Sky Images

Cloud detection and recognition are two important tasks usually referring to image binary segmentation and image-level classification individually. Cloud genera segmentation has more practical significance but is much more challenging as a fine-grained pixel-level dense prediction problem. In this letter, a cascade cloud attribute discrimination network (CCAD-Net) is proposed. Based on an improved encoding-decoding model, CCAD-Net adds a binary segmentation branch for cloud detection and a attribute discrimination branch for cloud attribute feature learning in the decoding stage. Especially, in the attribute discrimination branch, several visual attributes are selected to design the attribute discrimination constraint according to prior professional knowledge and corresponding loss function is defined. These two additional branches and the final cloud genera segmentation branch extract their task-specific features successively and form a cascade structure. Due to the fusion of raw feature, binary segmentation feature, attribute discrimination feature, and cloud genera feature, CCAD-Net can achieve significant better performance than the state-of-the-art methods in cloud genera segmentation in whole-sky images.

Anatomical Variations of the Celiac Trunk in Adult Filipino Cadavers: A Retrospective Study.

The celiac trunk (CT) is the first major branch of the abdominal aorta and typically gives rise to the left gastric artery (LGA), common hepatic artery (CHA), and splenic artery (SA), which supply blood to the abdominal viscera. Variations in the branching pattern of the CT exist and knowledge of such is crucial when performing surgical, laparoscopic, and angiographic procedures. The aim of this study is to determine the anatomic variations of the CT in adult Filipino cadavers and to compare the proportions of these with those reported in the foreign literature. Adult Filipino cadavers from the University of the Philippines College of Medicine Anatomy Laboratory were dissected by first year medical students from 2014-2019. The CT and its branches were identified, drawn on a separate piece of paper, and confirmed to be correct and accurate by an anatomist. The data collected from that period was reviewed. Percentages were calculated for the branching patterns identified. A Z-test of Two Populations was used to compare the results of the current study to that of Pinal-Garcia (2018), Pillay (2020) and Venieratos (2013). A total of 107 drawings based on 107 dissected preserved cadavers were reviewed. Ninety-two specimens (85.98%) showed typical branching into the LGA, CHA, and SA. The CT presented as a true tripod (tripus Halleri) in 75 specimens (70.09%) and as a bifurcation with one of the three arteries arising first along the trunk (false tripod) in 17 specimens (15.89%). Nine cadavers (8.41%) showed additional branches arising from the CT and four (3.74%) showed bifurcation of the CT with the third branch arising from a different artery. The most common configuration of the CT among Filipino adult cadavers is a true tripod, followed by a false tripod, additional branching, and bifurcation of the CT with the third branch arising elsewhere. The present study most closely resembles the results of the study of Venieratos.

A Degraded Reconstruction Enhancement-Based Method for Tiny Ship Detection in Remote Sensing Images With a New Large-Scale Dataset

The rapid detection of ships within the wide sea area is essential for intelligence acquisition. Most modern deep learning-based ship detection methods focus on locating ships in high-resolution (HR) remote sensing (RS) images. Seldom efforts have been made on ship detection in medium-resolution (MR) RS images. An MR image covers a much wider area than an HR one of the same size, thus facilitating quick ship detection. To this end, we propose a tiny ship detection method namely, Degraded Reconstruction Enhancement Network (DRENet), for MR RS images. Different from previous methods that mainly focus on feature fusion strategies to improve the expression ability of the detector, we design an additional network branch, i.e., degraded reconstruction enhancer, to learn to regress an object-aware blurred version of the input image in the training phase. Our intuition is that the proposed reconstruction branch may guide the backbone to focus more on tiny ship targets instead of the vast background. Moreover, we incorporate a CRoss-stage Multi-head Attention module in the detector to further improve the feature discrimination by leveraging the self-attention mechanism. To fill the gap of lacking a large-scale MR ship detection dataset, we introduce Levir-Ship, which contains 3876 GF-1/GF-6 multi-spectral images and over 3K tiny ship instances. Experiments on Levir-Ship validate the effectiveness and efficiency of the proposed method. Our method achieves 82.4 AP with 85 FPS, which outperforms many state-of-the-art ship detection methods. Our code and dataset will be made public.

Connection between two historical tuberculosis outbreak sites in Japan, Honshu, by a new ancestral Mycobacterium tuberculosis L2 sublineage.

Abstract By gathering 680 publicly available Sequence Read Archives from isolates of Mycobacterium tuberculosis complex (MTBC) including 190 belonging to the lineage 2 Beijing, and using an in-house bioinformatical pipeline, the TB-Annotator, that analyses more than 50 000 characters, we describe herein a new L2 sublineage from 20 isolates found in the Tochigi province, (Japan), that we designate as asia ancestral 5 (AAnc5). These isolates harbour a number of specific criteria (42 SNPs) and their intra-cluster pairwise distance suggests historical and not epidemiological transmission. These isolates harbour a mutation in rpoC, and do not fulfil, any of the modern Beijing lineage criteria, nor any of the other ancestral Beijing lineages described so far. Asia ancestral 5 isolates do not possess mutT2 58 and ogt 12 characteristics of modern Beijing, but possess ancestral Beijing SNPs characteristics. By looking into the literature, we found a reference isolate ID381, described in Kobe and Osaka belonging to the ‘G3’ group, sharing 36 out of the 42 specific SNPs found in AAnc5. We also assessed the intermediate position of the asia ancestral 4 (AAnc4) sublineage recently described in Thailand and propose an improved classification of the L2 that now includes AAnc4 and AAnc5. By increasing the recruitment into TB-Annotator to around 3000 genomes (including 642 belonging to L2), we confirmed our results and discovered additional historical ancestral L2 branches that remain to be investigated in more detail. We also present, in addition, some anthropological and historical data from Chinese and Japan history of tuberculosis, as well as from Korea, that could support our results on L2 evolution. This study shows that the reconstruction of the early history of tuberculosis in Asia is likely to reveal complex patterns since its emergence.

Unsupervised Spatial-Spectral CNN-Based Feature Learning for Hyperspectral Image Classification

The rapid development of remote sensing sensors makes the acquisition, analysis, and application of hyperspectral images (HSIs) more and more extensive. However, the limited sample sets, high-dimensional features, highly correlated bands, and mixing spectral information make the classification of HSIs a great challenge. In this article, an unsupervised multiscale and diverse feature learning (UMsDFL) method is proposed for HSI classification, which deeply considers the spatial–spectral features via convolutional neural networks (CNNs). Specifically, after employing the simple noniterative clustering (SNIC) algorithm with the heuristic calculation of superpixel size, the HSIs are segmented into superpixels for feature learning. The unsupervised network is designed with the convolutional encoder and decoder, the additional clustering branch, and the multilayer feature fusion to enhance the distinguishability of feature learning and the reusability of feature maps. Then, the spatial relationships and object attributes in large- and small-scale contexts are learned collaboratively through the unsupervised network to utilize the complementary multiscale characteristics. Moreover, the diverse features of hyperspectral information and nonsubsampled contourlet transform (NSCT) textures are learned simultaneously via the unsupervised network to alleviate the insufficiency of geometric representation. Finally, the random forest (RF) is adopted as the comprehensive classifier for land cover mapping based on the UMsDFL, and superpixel regularization is adopted to optimize the classification results. A series of experiments are performed on three real-world HSI datasets to demonstrate the effectiveness of our UMsDFL approach. The experimental results show that the proposed UMsDFL can achieve the overall accuracy of 79.23%, 96.49%, and 77.26% for Houston, Pavia, and Dioni datasets, respectively, when there are only five samples per class for training.

Possibility for the anisotropic acoustic plasmons in LaH10 and their role in enhancement of the critical temperature of superconducting transition

We discuss a possible role of the collective acoustic electronic excitations in the mechanism of the nearly room-temperature superconductivity in superhydrides at megabar pressures. While the dominant role of the phonon mechanism of superconductivity in such compounds is evident due to the measured isotope effect with deuterium (on LaD10), the small values of the effective Coulomb constant chosen in theoretical calculations of the critical temperature deserve further justification. We believe that the additional suppression of the Coulomb repulsion in superhydrides may be due to the appearance of additional acoustic plasmonic branches in their collective spectra. In LaH10 the conditions for such mechanism arise due to the hybridization of La 4f and H 1s states near the Fermi level in the vicinity of the L point of the Brillouin zone. We propose an analytical model approximation for the resulting conducting band and show that in a certain range of directions in quasimomentum space an acoustic branch should appear in the spectrum of the collective electronic excitations in LaH10. The contribution of these excitations to the suppression of the effective Coulomb constant is analyzed.

LCS: A Collaborative Optimization Framework of Vector Extraction and Semantic Segmentation for Building Extraction

In the field of building extraction, many CNN-based methods have been developed to solve the problem of the irregular boundaries in their predictions. The prevailing approach is to build an additional edge segmentation branch or obtain accurate vector components of buildings. However, pixel-based methods still cannot obtain accurate location of the boundary, while vector extraction will bring the problem of sample imbalance and missing detection. In this work, we utilize the complementarity of the two types of methods and propose the line segment collaborate segmentation (LCS) framework. In the proposed LCS framework, semantic segmentation provides location guidance for vector extraction, while vector extraction provides precise positioning for semantic segmentation. By this way, the two tasks can leverage their respective strengths. The results on three datasets show that the performance of vector extraction and semantic segmentation is improved simultaneously using the LCS framework, which proves the effectiveness of our method. At the same time, our framework is flexible and can be embedded in other vector extraction methods to improve performance.

Multiple Fano resonance refractive index sensor based on a plasmonic metal-insulator-metal based Taiji resonator

We propose and numerically analyze a multiple Fano resonance device with high quality ( Q ) factor and high modulation depth based on a metal-insulator-metal (MIM) waveguide laterally coupled Taiji resonator in the near-infrared regime. A silver-air-silver groove (SASG) is embedded in the MIM waveguide. The so-called Taiji resonator is an S-shaped additional crossover branch embedded in a ring cavity to selectively couple counterpropagating cavity modes, therefore increasing the propagating modes relative to the traditional ring cavity. Up to septuple Fano peaks rise from the interference between narrow discrete modes generated from the Taiji resonator and broad continuum modes stemming from the SASG, which can be easily tuned by adjusting the different geometric parameters of the proposed structure. The maximal Q -factor is about 317 with a modulation depth of 24.1 dB in transmission. Thanks to a narrow resonance linewidth with strong subwavelength light confinement of multiple Fano resonances, an optical refractive index sensor with a sensitivity of 2016 nm/RIU and figure of merit of 345 is demonstrated. Furthermore, the sensor can be used for sensing biological parameters such as glucose solution concentration with maximal sensitivity of 0.245 nm · L/g. This simple proposed structure may provide a further step for the development of Fano resonance applications in nanosensing, lasing, and nonlinear optics.

Frequency Modulation as a Cause of Additional Branches of the Gleissberg Cycle in Solar Activity

Frequency Modulation as a Cause of Additional Branches of the Gleissberg Cycle in Solar Activity