Nonlinear optimization for a low-emittance storage ring.

A multi-objective genetic algorithm (MOGA) is a powerful global optimization tool, but its results are considerably affected by the crossover parameter ηc. Finding an appropriate ηc demands too much computing time because MOGA needs be run several times in order to find a good ηc. In this paper, a self-adaptive crossover parameter is introduced in a strategy to adopt a new ηc for every generation while running MOGA. This new scheme has also been adopted for a multi-generation Gaussian process optimization (MGGPO) when producing trial solutions. Compared with the existing MGGPO and MOGA, the MGGPO and MOGA with the new strategy show better performance in nonlinear optimization for the design of low-emittance storage rings.

Hard X-ray operation of X-ray gas monitors at the European XFEL.

X-ray gas monitors (XGMs) are operated at the European XFEL for non-invasive single-shot pulse energy measurements and average beam-position monitoring. The underlying measurement principle is the photo-ionization of rare gas atoms at low gas pressures and the detection of the photo-ions and photo-electrons created. These are essential for tuning and sustaining self-amplified spontaneous emission (SASE) operation, machine radiation safety, and sorting single-shot experimental data according to pulse energy. In this paper, the first results from XGM operation at photon energies up to 30 keV are presented, which are far beyond the original specification of this device. Here, the Huge Aperture MultiPlier (HAMP) is used for single-shot pulse energy measurements since the standard X-ray gas monitor detectors (XGMDs) do not provide a sufficient signal-to-noise ratio, even at the highest operating gas pressures. A single-shot correlation coefficient of 0.98 is measured between consecutive XGMs operated with HAMP, which is as good as measuring with the standard XGMD detectors. An intra-train non-linearity of the HAMP signal is discovered, and operation parameters to mitigate this effect are studied. The upper repetition rate limit of HAMP operation at 2.25 MHz is also determined. Finally, the possibilities and limits for future XGM operation at photon energies up to 50 keV are discussed.

X-ray ghost imaging with a specially developed beam splitter.

X-ray ghost imaging with a crystal beam splitter has advantages in highly efficient imaging due to the simultaneous acquisition of signals from both the object beam and reference beam. However, beam splitting with a large field of view, uniform distribution and high correlation has been a great challenge up to now. Therefore, a dedicated beam splitter has been developed by optimizing the optical layout of a synchrotron radiation beamline and the fabrication process of a Laue crystal. A large field of view, consistent size, uniform intensity distribution and high correlation were obtained simultaneously for the two split beams. Modulated by a piece of copper foam upstream of the splitter, a correlation of 92% between the speckle fields of the object and reference beam and a Glauber function of 1.25 were achieved. Taking advantage of synthetic aperture X-ray ghost imaging (SAXGI), a circuit board of size 880 × 330 pixels was successfully imaged with high fidelity. In addition, even though 16 measurements corresponding to a sampling rate of 1% in SAXGI were used for image reconstruction, the skeleton structure of the circuit board can still be determined. In conclusion, the specially developed beam splitter is applicable for the efficient implementation of X-ray ghost imaging.

TaMYB44-5A reduces drought tolerance by repressing transcription of TaRD22-3A in the abscisic acid signaling pathway.

MAIN CONCLUSION: TaMYB44-5A identified as a transcription factor negatively regulates drought tolerance in transgenic Arabidopsis. Drought can severely reduce yields throughout the wheat-growing season. Many studies have shown that R2R3-MYB transcription factors are involved in drought stress responses. In this study, the R2R3-MYB transcription factor MYB44-5A was identified in wheat (Triticum aestivum L.) and functionally analyzed. Three homologs of TaMYB44 were isolated, all of which localized to the nucleus. Overexpression of TaMYB44-5A reduced drought tolerance in Arabidopsis thaliana. Further analysis showed that TaMYB44-5A reduced the sensitivity of transgenic Arabidopsis to ABA. Genetic and transcriptional regulation analyses demonstrated that the expression levels of drought- and ABA-responsive genes were downregulated by TaMYB44-5A, and TaMYB44-5A directly bound to the MYB-binding site on the promoter to repress the transcription level of TaRD22-3A. Our results provide insights into a novel molecular pathway in which the R2R3-MYB transcription factor negatively regulates ABA signaling in response to drought stress.

DISP1 deficiency: Monoallelic and biallelic variants cause a spectrum of midline craniofacial malformations.

PURPOSE: DISP1 encodes a transmembrane protein that regulates the secretion of the morphogen, Sonic hedgehog, a deficiency of which is a major cause of holoprosencephaly (HPE). This disorder covers a spectrum of brain and midline craniofacial malformations. The objective of the present study was to better delineate the clinical phenotypes associated with division transporter dispatched-1 (DISP1) variants. METHODS: This study was based on the identification of at least 1 pathogenic variant of the DISP1 gene in individuals for whom detailed clinical data were available. RESULTS: A total of 23 DISP1 variants were identified in heterozygous, compound heterozygous or homozygous states in 25 individuals with midline craniofacial defects. Most cases were minor forms of HPE, with craniofacial features such as orofacial cleft, solitary median maxillary central incisor, and congenital nasal pyriform aperture stenosis. These individuals had either monoallelic loss-of-function variants or biallelic missense variants in DISP1. In individuals with severe HPE, the DISP1 variants were commonly found associated with a variant in another HPE-linked gene (ie, oligogenic inheritance). CONCLUSION: The genetic findings we have acquired demonstrate a significant involvement of DISP1 variants in the phenotypic spectrum of midline defects. This underlines its importance as a crucial element in the efficient secretion of Sonic hedgehog. We also demonstrated that the very rare solitary median maxillary central incisor and congenital nasal pyriform aperture stenosis combination is part of the DISP1-related phenotype. The present study highlights the clinical risks to be flagged up during genetic counseling after the discovery of a pathogenic DISP1 variant.

MdASMT9-mediated melatonin biosynthesis enhances basal thermotolerance in apple plants.

High temperatures negatively impact the yield and quality of fruit crops. Exogenous melatonin (MT) application has been shown to enhance heat tolerance, but the response of endogenous MT to heat stress, particularly in perennial fruit trees, remains unclear. The present study investigated the effects of high temperatures on transgenic apple plants overexpressing the MT biosynthesis gene N-acetylserotonin methyltransferase 9 (MdASMT9). Endogenous MT protected transgenic plants from heat stress by increasing antioxidant enzyme activity and scavenging reactive oxygen species (ROS), and protecting the chloroplasts from damage. Application of MT and overexpression of MdASMT9 also reduced abscisic acid accumulation through promoting MdWRKY33-mediated transcriptional inhibition of MdNCED1 and MdNCED3, thus inducing stomatal opening for better heat dissipation. Furthermore, MT-enhanced autophagic activity through promoting MdWRKY33-mediated transcriptional enhancement of MdATG18a under heat stress. These findings provide new insights into the regulation of endogenous MT and its role in improving basal thermotolerance in perennial fruit trees.

Unravelling the neglected role of ultraviolet radiation on stomata: A meta-analysis with implications for modelling ecosystem-climate interactions.

Stomata play a pivotal role in regulating gas exchange between plants and the atmosphere controlling water and carbon cycles. Accordingly, we investigated the impact of ultraviolet-B radiation, a neglected environmental factor varying with ongoing global change, on stomatal morphology and function by a Comprehensive Meta-Analysis. The overall UV effect at the leaf level is to decrease stomatal conductance, stomatal aperture and stomatal size, although stomatal density was increased. The significant decline in stomatal conductance is marked (6% in trees and >10% in grasses and herbs) in short-term experiments, with more modest decreases noted in long-term UV studies. Short-term experiments in growth chambers are not representative of long-term field UV effects on stomatal conductance. Important consequences of altered stomatal function are hypothesized. In the short term, UV-mediated stomatal closure may reduce carbon uptake but also water loss through transpiration, thereby alleviating deleterious effects of drought. However, in the long term, complex changes in stomatal aperture, size, and density may reduce the carbon sequestration capacity of plants and increase vegetation and land surface temperatures, potentially exacerbating negative effects of drought and/or heatwaves. Therefore, the expected future strength of carbon sink capacity in high-UV regions is likely overestimated.

Exploring the performance of the topological energy method for object and damage detection from noisy and poor databases.

In this work, we study the performance of numerical methods based on the computation of topological energies to process data from synthetic and real experiments where only noisy limited-view data corresponding to a few frequencies are available. We show numerical experiments in two problems of practical interest. The first one corresponds to experimental measurements of the electromagnetic scattering produced by different objects extracted from the Fresnel database. The second one is related to synthetic experiments in a simplified model where steel welding joints are acoustically tested to identify possible flaws (air bubbles and inclusions) produced during the welding process. This article is part of the theme issue 'Non-smooth variational problems with applications in mechanics'.

RotatedStomataNet: a deep rotated object detection network for directional stomata phenotype analysis.

KEY MESSAGE: Innovatively, we consider stomatal detection as rotated object detection and provide an end-to-end, batch, rotated, real-time stomatal density and aperture size intelligent detection and identification system, RotatedeStomataNet. Stomata acts as a pathway for air and water vapor in the course of respiration, transpiration, and other gas metabolism, so the stomata phenotype is important for plant growth and development. Intelligent detection of high-throughput stoma is a key issue. Nevertheless, currently available methods usually suffer from detection errors or cumbersome operations when facing densely and unevenly arranged stomata. The proposed RotatedStomataNet innovatively regards stomata detection as rotated object detection, enabling an end-to-end, real-time, and intelligent phenotype analysis of stomata and apertures. The system is constructed based on the Arabidopsis and maize stomatal data sets acquired destructively, and the maize stomatal data set acquired in a non-destructive way, enabling the one-stop automatic collection of phenotypic, such as the location, density, length, and width of stomata and apertures without step-by-step operations. The accuracy of this system to acquire stomata and apertures has been well demonstrated in monocotyledon and dicotyledon, such as Arabidopsis, soybean, wheat, and maize. The experimental results that the prediction results of the method are consistent with those of manual labeling. The test sets, the system code, and their usage are also given ( https://github.com/AITAhenu/RotatedStomataNet ).

Heated humidified high-flow nasal cannula: a new conservative approach for neonatal nasal stenosis.

PURPOSE: The aim of this study is to evaluate the efficacy of heated humidified high flow nasal cannula (HFNC) therapy as a conservative treatment option for newborns suffering from nasal stenosis, a condition that often leads to respiratory distress and feeding difficulties. Given the increasing utilization of HFNC in various upper and lower respiratory tract indications, characterized by its flow-based mechanism and minimal mucosal damage, we seek to investigate its potential benefits in this specific patient population. METHODS: A retrospective chart review of newborns with congenital nasal stenosis treated with HFNC for respiratory distress or feeding difficulties in a pediatric tertiary center between 2014 and 2022. Data were collected for demographic characteristics, clinical presentation and ventilatory requirements, pre and post HFNC application. RESULTS: Six infants with nasal stenosis were included in the study cohort. Five were diagnosed with congenital pyriform aperture stenosis, three of whom had additional midnasal stenosis. One patient had nasal synechiae. Two patients had failed surgical treatment and all patients failed conservative treatment prior to HFNC treatment. Following HFNC use, improvement was noted in oxygen saturations, heart and respiratory rates, meal volumes and weight. None of the patients required any additional sinonasal surgical treatment. No complications were observed. CONCLUSIONS: In this case series, we present the first documented use of HFNC treatment for nasal stenosis, showing favorable results. Further studies with a larger cohort, wider range of conditions and extended follow-up periods are needed to establish the risks and benefits of HFNC for neonatal nasal stenosis.

Utilizing collimated aperture with proton pencil beam scanning (PBS) for stereotactic radiotherapy.

PURPOSE: Proton stereotactic radiosurgery (PSRS) has emerged as an innovative proton therapy modality aimed at achieving precise dose delivery with minimal impact on healthy tissues. This study explores the dosimetric outcomes of PSRS in comparison to traditional intensity-modulated proton therapy (IMPT) by focusing on cases with small target volumes. A custom-made aperture system designed for proton therapy, specifically tailored to small target volumes, was developed and implemented for this investigation. METHODS: A prerequisite mechanical validation through an isocentricity test precedes dosimetric assessments, ensuring the seamless integration of mechanical and dosimetry analyses. Five patients were enrolled in the study, including two with choroid melanoma and three with arteriovenous malformations (AVM). Two treatment plans were meticulously executed for each patient, one utilizing a collimated aperture and the other without. Both plans were subjected to robust optimization, maintaining identical beam arrangements and consistent optimization parameters to account for setup errors of 2 mm and range uncertainties of 3.5%. Plan evaluation metrics encompassing the Heterogeneity Index (HI), Paddick Conformity Index (CIPaddick), Gradient Index (GI), and the R50% index to evaluate alterations in low-dose volume distribution. RESULTS: The comparative analysis between PSRS and traditional PBS treatment revealed no significant differences in plan outcomes, with both modalities demonstrating comparable target coverage. However, collimated apertures resulted in discernible improvements in dose conformity, dose fall-off, and reduced low-dose volume. CONCLUSIONS: This study underscores the advantageous impact of the aperture system on proton therapy, particularly in cases involving small target volumes.

Ultrasonic Imaging of Deeper Bone Defect Using Virtual Source Synthetic Aperture with Phased Shift Migration: A Phantom Study.

Ultrasound imaging for bone is a difficult task in the field of medical ultrasound. Compared with other phase array techniques, the synthetic aperture (SA) has a better lateral resolution but a limited imaging depth due to the limited ultrasonic energy emitted by the single emitter in each transmission. In contrast, the virtual source (VS) synthetic aperture allows a simultaneous multi-element emission and could provide a higher ultrasonic incident energy in each transmission. Therefore, the VS might achieve a high imaging quality at a deeper depth for bone imaging than the traditional SA. In this study, we proposed the virtual source phase shift migration (VS-PSM) method to achieve ultrasonic imaging of the deeper bone defect featured in the multilayer structure. The proposed VS-PSM method was validated using standard soft tissue phantom and printed bone phantom with artificial defects. The image quality was evaluated in terms of contrast-to-noise ratios (CNR) and amplitudes of scatters and defects at different imaging depths. The results showed that the VS-PSM method could achieve a high imaging quality of the soft tissues with a significant improvement in the scattering amplitude and without a significant sacrifice of the lateral and axial resolution. The PSM was superior to the DAS in suppressing the background noise in the images. Compared with the traditional SA-PSM, the VS-PSM method could image deeper bone defects at different ultrasonic frequencies, with an average improvement of 50% in CNR. In conclusion, this study demonstrated that the proposed VS-PSM method could image deeper bone defects and might help the diagnosis of bone disease using ultrasonic imaging.

Simulation of Modal Control of Metal Mode-Filtered Vertical-Cavity Surface-Emitting Laser.

In this study, a novel metal-dielectric film mode filter structure that can flexibly regulate the transverse mode inside vertical-cavity surface-emitting lasers (VCSELs) is proposed. The number, volume, and stability of transverse modes inside the VCSEL can be adjusted according to three key parameters-the oxide aperture, the metal aperture, and the distance between the oxide aperture and the metal aperture-to form a flexible window, and a new parameter is defined to describe the mode identification. This study provides a complete simulation theory basis and calculation method, which is of great significance for the optical mode control in VCSELs.

An Airborne Arc Array Synthetic Aperture Radar Vibration Error Compensation Method.

Compared to conventional radars, arc array synthetic aperture radar (SAR) enables wide-area observation under ideal conditions. However, helicopters carrying arc array SAR platforms are generally smaller in size and more sensitive to vibration, which has a greater impact on the imaging quality. In this paper, the vibration error of the arc array SAR platform is investigated, and a vibration error model of the arc array SAR platform is established. Based on the study of the vibration error model, a vibration phase estimation and compensation algorithm based on the delayed conjugate multiplication method is proposed. In the first step, distance pulse pressure processing is performed on the echo signal. In the second step, the pulse pressure signals and their delays in the same distance unit are subjected to conjugate multiplication, and the phase of the signal after conjugate multiplication is extracted. The extracted phase is then amplitude- and phase-compensated to estimate the vibration phase. In the third step, the vibration phase is compensated in the azimuthal direction of the distance pulse pressure signal, and the pairwise echo is eliminated, which completes the compensation of the airborne arc array SAR vibration platform.

Simulation-Based Approach to the Matching of a Dielectric-Filled Circular Waveguide Aperture.

The circular waveguide aperture or open-end radiator, one of the canonical antenna elements, can be filled with a dielectric material for miniaturization. With dielectric filling, the aperture reflection increases and impedance matching is necessary. This paper presents a simple but innovative simulation-based approach to the aperture matching of a dielectric-filled circular waveguide aperture. By properly loading the aperture with two- or three-section dielectric rings, the impedance matching is possible over a wide frequency range starting slightly above the TE11-mode cutoff and continuing upward. The material for the aperture matching is the same as that filling the waveguide. The proposed matching structure is analyzed and optimized using a simulation tool for the dielectric constant εr of the filling material ranging from 1.8 to 10. For εr ≥ 5, the unmatched reflection coefficient ranges from -6.0 dB to -0.9 dB while the matched reflection coefficient is from -20.4 dB to -10.0 dB. The impedance matching has been achieved over more than an octave bandwidth.

Full-Scale Aggregated MobileUNet: An Improved U-Net Architecture for SAR Oil Spill Detection.

Oil spills are a major threat to marine and coastal environments. Their unique radar backscatter intensity can be captured by synthetic aperture radar (SAR), resulting in dark regions in the images. However, many marine phenomena can lead to erroneous detections of oil spills. In addition, SAR images of the ocean include multiple targets, such as sea surface, land, ships, and oil spills and their look-alikes. The training of a multi-category classifier will encounter significant challenges due to the inherent class imbalance. Addressing this issue requires extracting target features more effectively. In this study, a lightweight U-Net-based model, Full-Scale Aggregated MobileUNet (FA-MobileUNet), was proposed to improve the detection performance for oil spills using SAR images. First, a lightweight MobileNetv3 model was used as the backbone of the U-Net encoder for feature extraction. Next, atrous spatial pyramid pooling (ASPP) and a convolutional block attention module (CBAM) were used to improve the capacity of the network to extract multi-scale features and to increase the speed of module calculation. Finally, full-scale features from the encoder were aggregated to enhance the network's competence in extracting features. The proposed modified network enhanced the extraction and integration of features at different scales to improve the accuracy of detecting diverse marine targets. The experimental results showed that the mean intersection over union (mIoU) of the proposed model reached more than 80% for the detection of five types of marine targets including sea surface, land, ships, and oil spills and their look-alikes. In addition, the IoU of the proposed model reached 75.85 and 72.67% for oil spill and look-alike detection, which was 18.94% and 25.55% higher than that of the original U-Net model, respectively. Compared with other segmentation models, the proposed network can more accurately classify the black regions in SAR images into oil spills and their look-alikes. Furthermore, the detection performance and computational efficiency of the proposed model were also validated against other semantic segmentation models.

A SAR Ship Detection Method Based on Adversarial Training.

SAR (synthetic aperture radar) ship detection is a hot topic due to the breadth of its application. However, limited by the volume of the SAR image, the generalization ability of the detector is low, which makes it difficult to adapt to new scenes. Although many data augmentation methods-for example, clipping, pasting, and mixing-are used, the accuracy is improved little. In order to solve this problem, the adversarial training is used for data generation in this paper. Perturbation is added to the SAR image to generate new samples for training, and it can make the detector learn more abundant features and promote the robustness of the detector. By separating batch normalization between clean samples and disturbed images, the performance degradation on clean samples is avoided. By simultaneously perturbing and selecting large losses of classification and location, it can keep the detector adaptable to more confrontational samples. The optimization efficiency and results are improved through K-step average perturbation and one-step gradient descent. The experiments on different detectors show that the proposed method achieves 8%, 10%, and 17% AP (Average Precision) improvement on the SSDD, SAR-Ship-Dataset, and AIR-SARShip, compared to the traditional data augmentation methods.

Ship Detection in Synthetic Aperture Radar Images under Complex Geographical Environments, Based on Deep Learning and Morphological Networks.

Synthetic Aperture Radar (SAR) ship detection is applicable to various scenarios, such as maritime monitoring and navigational aids. However, the detection process is often prone to errors due to interferences from complex environmental factors like speckle noise, coastlines, and islands, which may result in false positives or missed detections. This article introduces a ship detection method for SAR images, which employs deep learning and morphological networks. Initially, adaptive preprocessing is carried out by a morphological network to enhance the edge features of ships and suppress background noise, thereby increasing detection accuracy. Subsequently, a coordinate channel attention module is integrated into the feature extraction network to improve the spatial awareness of the network toward ships, thus reducing the incidence of missed detections. Finally, a four-layer bidirectional feature pyramid network is designed, incorporating large-scale feature maps to capture detailed characteristics of ships, to enhance the detection capabilities of the network in complex geographic environments. Experiments were conducted using the publicly available SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Image Dataset (HRSID). Compared with the baseline model YOLOX, the proposed method increased the recall by 3.11% and 0.22% for the SSDD and HRSID, respectively. Additionally, the mean Average Precision (mAP) improved by 0.7% and 0.36%, reaching 98.47% and 91.71% on these datasets. These results demonstrate the outstanding detection performance of our method.

A Novel Joint Motion Compensation Algorithm for ISAR Imaging Based on Entropy Minimization.

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk-Nelder-Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm.

Sub-Nyquist SAR Imaging and Error Correction Via an Optimization-Based Algorithm.

Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time-space modulation has been proposed to increase the swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by an optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and the pre-definition of optimization parameters, and a low signal-noise ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm, named pseudo-ℒ0-norm optimization algorithm, is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of nonzero elements based on Bayesian estimation, and then this model is solved by the Cauchy-Newton method. Additionally, an error correction method combined with our proposed pseudo-ℒ0-norm optimization algorithm is also present to eliminate defocusing in the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm.